Fact-checked by Grok 2 weeks ago

Digital pathology

Digital pathology is the process of digitizing glass slides containing specimens to produce high-resolution whole slide images (WSIs), enabling pathologists to view, analyze, and share these images using computer-based systems rather than traditional light . This field encompasses the scanning of at microscopic resolution, storage and management of digital images via image management systems, and integration with laboratory information systems and electronic medical records to support diagnostic workflows. Emerging as a distinct over the past two decades, digital pathology has evolved alongside advancements in computing power, storage capacity, and (), transitioning from basic telepathology in the to comprehensive AI-assisted analysis today. At its core, digital pathology leverages whole slide scanners to capture entire slides in a single high-fidelity image, often at 20x or 40x , allowing for seamless and similar to a virtual microscope. Key technologies include computational pathology tools, which apply and algorithms—such as convolutional neural networks—to automate tasks like cell segmentation, tumor detection, and prognostic prediction in . For instance, models have demonstrated diagnostic equivalence to conventional , with discordance rates of 4.9% for digital versus 4.6% for glass slides in large-scale studies involving nearly 2,000 cases. The adoption of digital pathology has accelerated, with surveys indicating that 53% of pathologists used it for primary by 2020, up from 30% in 2013, and 64% for secondary consultations by 2021. Advantages include enhanced efficiency through reduced turnaround times (reported by 91% of users), cost savings (e.g., up to $267,000 annually in large institutions), remote via telepathology, and improved through accessible digital archives. Applications span clinical diagnostics—particularly in for detecting metastases or predicting outcomes—research for quantitative morphometric analysis, in labs, and multidisciplinary tumor boards. However, challenges persist, including high initial costs, technical issues like scanning artifacts requiring rescans in approximately 5% of cases on average, data privacy concerns, and the need for pathologist training to mitigate issues like , which impacts 64-90% of prolonged screen users. Standardization efforts and regulatory approvals, such as the FDA-cleared Prostate Detect for detection (2021) and more recent tools like Prostate Detect (2025), are addressing these to broaden implementation.

Overview

Definition and Principles

Digital pathology refers to the application of to digitize, manage, analyze, and share images derived from traditional practices. It primarily relies on whole slide imaging (WSI), a process that scans entire glass slides at high resolutions, typically equivalent to 20x to 40x , producing gigapixel-scale digital images that capture microscopic details across the full specimen area. This digitization enables pathologists to view and interact with slides on digital platforms, replicating the navigation capabilities of optical microscopes while facilitating computational enhancements. The foundational principles of digital pathology center on achieving high-fidelity image capture that preserves diagnostic quality comparable to conventional light . Key aspects include pixel resolution, which is standardized at approximately 0.25–0.5 μm per pixel to ensure sufficient detail for identifying cellular and tissue structures, with 0.25 μm/pixel common at 40x magnification and 0.5 μm/pixel at 20x. Additionally, z-stack captures multiple focal planes through the specimen thickness, allowing for that provides volumetric insights beyond single-plane views, particularly useful for thicker tissues or complex architectures. These principles underpin the transition from analog —limited by physical handling and local viewing—to digital workflows that support remote access, collaborative review, and automated analysis without compromising interpretive accuracy. The basic workflow in digital pathology begins with traditional slide preparation, involving tissue fixation, sectioning, and staining to create glass slides suitable for microscopic examination. These slides are then digitized via automated scanning systems that systematically capture the entire surface in a tiled, high-resolution format, generating a composite digital image file. The output—a navigable digital slide—enhances the traditional light microscopy process by enabling seamless integration with software for annotation, measurement, and sharing, while maintaining the core goal of diagnostic evaluation.

Significance and Benefits

Digital pathology significantly enhances efficiency in pathology workflows by reducing the time associated with physical handling and enabling faster diagnostic turnaround. Studies have demonstrated that of digital systems can decrease pathologist workload by an average of 29.2%, with reductions exceeding 50% during peak periods, primarily through elimination of manual retrieval, transport, and filing tasks. This allows for quicker case sign-out, with median times dropping by up to 71 seconds per external case compared to analog methods, facilitating turnaround times that shift from days to hours in consultations. Additionally, telepathology enabled by supports remote diagnostics, providing real-time access to for experts in underserved or distant locations, thereby streamlining consultations without the delays of physical shipment. The technology also improves diagnostic accuracy and objectivity by standardizing image viewing conditions, which minimizes human variability in interpretation. Through consistent magnification, lighting, and tools, digital pathology reduces subjective biases inherent in traditional , leading to more reproducible results across pathologists. In grading, digital workflows have shown inter-observer agreement comparable to routine , with kappa values ranging from moderate (0.36–0.55) to excellent (0.72) for parameters like primary Gleason grade and complex features such as Gleason patterns. Beyond core diagnostics, digital pathology fosters broader systemic benefits, including enhanced inter-institutional collaboration and support for . Secure sharing of high-resolution images via cloud-based platforms enables multidisciplinary teams to review cases asynchronously or in , accelerating second opinions and collaborations worldwide. It also facilitates quantifiable biomarker analysis, such as PD-L1 expression scoring with 84.6% concordance to manual methods, aiding tailored therapies in . Cost efficiencies arise from reduced physical infrastructure needs, with one implementation yielding annual savings of $267,000 through a 93% drop in glass slide archival requests and minimized transport expenses, equating to 20–30% reductions in storage and logistics budgets for high-volume labs. In high-throughput settings, digital process over 400 slides per day, compared to manual workflows handling 20–50 slides, boosting overall capacity without proportional staff increases.

History

Early Developments

Digital pathology emerged in the late as an extension of telepathology systems designed for remote sharing of images. The field traces its origins to 1986, when Ronald S. Weinstein, often regarded as the pioneer of telepathology, first described the transmission of static images over long-distance networks in a seminal outlining the necessary technological steps for remote consultations. These early systems relied on basic digital capture and transfer of still images, laying the groundwork for broader efforts in the , where telepathology evolved to support consultative diagnostics between distant institutions despite nascent technological constraints. Key innovations in the late 1990s focused on improving image capture and enabling full-slide digitization, addressing the limitations of traditional microscopy. The introduction of megapixel digital cameras for microscopic applications during this period allowed for higher-resolution pathology imaging, facilitating the shift from film-based to digital workflows. A pivotal advancement came with the development of whole-slide imaging (WSI) scanners; James Bacus created the first commercial WSI system, the BLISS (Bacus Laboratories Inc. Slide Scanner), around 1994, which used a single optical axis microscope to generate high-resolution virtual slides from entire glass slides. Building on this, in 1999, Arthur Wetzel and John Gilbertson at Interscope Technologies developed the first automated high-speed WSI system, significantly reducing scan times from hours to minutes and enabling practical use in research settings. Early challenges in digital pathology centered on technical barriers such as limited for transferring large files and the lack of user-friendly software for viewing and . Researchers addressed issues through techniques and selective in telepathology setups, while initial software tools provided basic zooming and panning capabilities for static and dynamic images. The first commercial prototypes emerged soon after, with Aperio launching its ScanScope system in 2000, featuring a linear detector for efficient strip-based scanning of slides. Hamamatsu Photonics followed with its NanoZoomer prototype around 2003, introducing high-throughput scanning capabilities that further mitigated time and resolution hurdles. A significant milestone occurred in 1998 with the establishment of the first dedicated digital pathology laboratory at the (UPMC), founded by Michael J. Becich and colleagues through Interscope Technologies, which integrated early WSI and tools for clinical and applications. This lab pioneered the practical implementation of digital systems, overcoming initial skepticism about image fidelity and workflow integration, and set the stage for broader adoption in the early 2000s.

Modern Milestones and Adoption

In the early , regulatory frameworks began to formalize the use of digital pathology in clinical practice. The Royal College of Pathologists issued guidance on telepathology in October 2013, outlining principles for its implementation in primary diagnosis, consultations, and , which helped standardize remote pathology workflows across the . A pivotal regulatory occurred in 2017 when the U.S. (FDA) granted the first clearance for a whole-slide imaging (WSI) system for primary diagnostic use. The IntelliSite Pathology Solution received De Novo classification, enabling pathologists to review and interpret digital slides of specimens as an alternative to conventional light microscopy. This approval marked a shift from exploratory applications to validated clinical tools, addressing prior concerns over image fidelity and diagnostic equivalence. The formation of the Digital Pathology Association (DPA) in 2009 played a key role in advancing standardization efforts, including collaborations with regulatory bodies to harmonize guidelines and promote . By the mid-2010s, the DPA had facilitated initiatives like white papers on computational , influencing global standards for data handling and AI integration. The in 2020 dramatically accelerated adoption, as labs shifted to remote diagnostics to minimize in-person exposure; the FDA temporarily relaxed enforcement on WSI for primary diagnosis, leading to widespread implementation for telepathology and case reviews. This surge enabled continuity of services amid lockdowns, with reports indicating a rapid uptick in digital workflows, particularly in high-volume centers. By 2025, adoption had reached approximately 30% in U.S. clinical laboratories, driven by validated systems and growing evidence of workflow efficiency. At the (ASCO) Annual Meeting that year, presentations highlighted -enhanced digital pathology tools for improving detection in , such as HER2 assessment and treatment response prediction, underscoring their integration into precision . In September 2025, announced a collaboration with to enhance digital pathology capabilities, focusing on integration and addressing pathologist shortages. Key drivers of adoption include seamless integration with electronic health records (EHRs) for streamlined data sharing and for scalable storage and remote access, reducing latency in multi-site collaborations. Globally, has seen steady growth aligned with EU Medical Device Regulation (MDR) compliance, which classifies WSI systems as Class C devices requiring rigorous validation for diagnostic use since 2021. In , Japan's national initiatives have promoted adoption to address pathologist shortages. Adoption rates, which were very low in U.S. labs around 2010 due to regulatory and technical barriers, are projected to exceed 50% by 2030, fueled by market expansion and workforce demands, according to industry analyses. The global digital pathology market, valued at about $1.1 billion in 2024, is expected to reach $2.75 billion by 2030, reflecting broader clinical uptake.

Technical Infrastructure

Image Acquisition and Scanning

Image acquisition in digital pathology primarily involves the use of automated whole slide to digitize glass slides containing sections. These employ brightfield or to capture high-resolution images of entire slides, utilizing robotic mechanisms for slide loading, precise stage movement, automated focusing, and systematic image tiling or line scanning. The process begins with the scanner's loading slides into the system, followed by illumination of the specimen and capture of images in overlapping tiles or continuous lines, which are then computationally stitched into a cohesive whole slide image (WSI). Typical resolutions range from 0.25 to 0.5 μm per , corresponding to 20x to 40x magnification, enabling detailed visualization of cellular structures suitable for diagnostics. Hardware components of these scanners include high-resolution cameras, objective lenses, motorized stages, and controlled light sources, with two primary imaging approaches: area-scan (tile-based) and line-scan cameras. Area-scan cameras capture rectangular image tiles sequentially, often with per-tile focusing for optimal sharpness, while line-scan cameras use a linear to sweep across the slide in a single dimension, generating images line by line for faster acquisition. Scan times typically range from 30 seconds to 2 minutes per slide for standard brightfield of 15 mm × 15 mm areas at 40x, with throughput capacities of 60 to 100 slides per hour depending on the model. For thicker s or three-dimensional , multi-layer via z-stacking captures multiple focal planes, though this extends scan times to several minutes or more. Prominent vendors include ' Aperio series, such as the GT 450 model with a 450-slide capacity and 81 slides per hour throughput at 40x, and Philips' IntelliSite Pathology Solution, offering up to 60 slides per hour. Image quality is influenced by several factors during acquisition, including illumination uniformity, focus accuracy, and mitigation of artifacts. Robotic control ensures consistent Köhler illumination to minimize variations in brightness and color across the slide, while advanced focus algorithms—such as continuous refocusing or pre-generated focus maps—maintain sharpness, particularly challenging in uneven or dense tissues. Common artifacts like tissue folds, bubbles, or stitching errors are addressed through automated detection and rejection protocols, with scanners often rescanning affected areas to achieve diagnostic fidelity. For instance, Leica Aperio systems incorporate precise positioning (50 nm accuracy) and quality checks to reduce such issues. Pre-scanning preparation is crucial for compatibility and optimal results, involving standardized staining protocols and careful slide handling. Hematoxylin and eosin (H&E) staining is the most common, providing contrast for (blue) and cytoplasmic/extracellular (pink) structures, with sections typically 4-6 μm thick mounted on standard 26 × 76 mm glass slides. Slides must be free of coverslip defects, air bubbles, or excessive thickness to prevent focusing issues or folds; liquid-based preparations enhance uniformity for cytology. Quantitative assessment of staining intensity and consistency prior to scanning helps ensure .

Viewing, Management, and Sharing

Digital pathology relies on specialized software tools for viewing whole-slide images (WSIs), which enable pathologists to navigate high-resolution scans with intuitive interfaces supporting pan and zoom functionalities at various magnification levels, often mimicking traditional light microscopy. These viewers typically incorporate support to display multiple slides or annotations simultaneously, enhancing efficiency during case reviews. Annotation capabilities allow users to draw regions of interest (ROIs), add text notes, or highlight pathological features, facilitating collaborative discussions and educational purposes. For instance, Aperio ImageScope, developed by , provides pyramid-based zooming for rapid navigation across gigapixel images, while open-source alternatives like QuPath offer extensible plugins for custom and measurements. Management of digital pathology data involves integrating with Laboratory Information Systems (LIS) to track essential , such as patient identifiers, specimen details, and scan timestamps, ensuring and compliance in clinical workflows. Storage solutions handle the massive data volumes—often terabytes per slide collection—through scalable architectures, including on-premise servers for sensitive environments or cloud-based platforms like those from Sectra or PathAI for distributed access. These systems employ hierarchical indexing and techniques to optimize without compromising . A key aspect of management is robust archiving policies, which mandate long-term retention (e.g., 10+ years for clinical records) while supporting quick access, with typical full-slide load times under 5 seconds in modern viewers to maintain pathologist productivity. Sharing digital pathology images requires secure mechanisms to enable teleconsultation and multi-site collaboration, often utilizing protocols like over VPNs or DICOM-based Picture Archiving and Communication Systems (PACS) adapted for . These ensure encrypted transmission and access controls, aligning with regulatory standards such as HIPAA in the United States for protecting patient health information and GDPR in for data privacy. For example, platforms like Proscia Concentriq integrate federated sharing models, allowing remote experts to view and annotate slides without transferring raw data, thereby reducing needs and enhancing security. Such systems support real-time collaboration tools, including shared cursors and chat interfaces, which have been shown to improve diagnostic concordance in multicenter studies.

Data Formats and Standards

Proprietary File Formats

Proprietary file formats in digital pathology are vendor-specific structures designed to store high-resolution whole slide images (WSIs) generated by scanner hardware, often incorporating custom and optimized for viewing and analysis software. These formats enable efficient handling of gigapixel-scale images but prioritize within the vendor's ecosystem, limiting seamless integration across different systems. Common examples include the SVS format from Aperio (now ), NDPI from , and iSyntax from , each tailored to specific scanning technologies and workflows. The SVS format, developed by Aperio, utilizes a single-file pyramidal tiled structure with non-standard and compression, sometimes incorporating JPEG2000 for enhanced quality. This allows for multi-resolution representation, where the full-resolution image at 40x is downsampled into successively lower levels for quick navigation and zooming. NDPI, Hamamatsu's format, resembles a file but includes custom tags for , supporting -compressed images and accommodating multi-channel data for applications like . It stores image data in a single file, with embedded details on scan parameters and tissue positioning. Philips' iSyntax employs a hierarchical model based on JPEG2000 transformations, featuring clusters of tiles at varying scales for fast compression and decompression, achieving medical-grade quality with speeds up to 10 times faster than standard JPEG2000, albeit at a slightly larger . These formats typically incorporate hierarchical levels—ranging from 16 to 20 or more, spanning full down to thumbnail views—to facilitate efficient multi- viewing without loading the entire into memory. Embedded , such as scanner settings, , and coordinates, is integrated directly into the file structure, enabling precise and within vendor tools. For instance, SVS files embed Aperio-specific tags in the header, while NDPI uses custom extensions for multi-channel support, and iSyntax organizes within its wavelet-based clusters. Typical file sizes for a standard 40x scanned slide range from 500 MB to 2 GB, depending on area and , reflecting the high volume of WSIs. While these formats offer high optimization for their respective vendor software—such as seamless integration with proprietary viewers and analysis modules—they impose significant drawbacks due to , requiring conversions for use with third-party tools and potentially introducing data loss or processing overhead. This interoperability challenge often necessitates specialized libraries like OpenSlide for reading, but full support remains limited for some formats. Other notable proprietary formats include Ventana's BIF (BioImagene Image File) from , which uses a BigTIFF with XML in XMP tags for pyramidal storage, and 3DHistech's MRXS, supporting and other compressions in a multi-layer format for high-speed scanning outputs.

Interoperable and Open Standards

Interoperable and open standards in digital pathology facilitate the exchange of whole slide images (WSIs) across diverse systems, vendors, and applications, promoting vendor-neutral workflows and long-term data accessibility. These standards address the limitations of proprietary formats by defining structured data models, metadata schemas, and compression methods that ensure compatibility without reliance on vendor-specific software. Key examples include the Digital Imaging and Communications in Medicine (DICOM) standard, the Open Microscopy Environment Tagged Image File Format (OME-TIFF) supported by Bio-Formats, and the Iris File Extension (IFE). DICOM Supplement 145, finalized in 2010 and actively evolved through subsequent updates, specifies the Whole Slide Microscopic Image Information Object Definition (IOD) and associated Service-Object Pair () classes tailored for pathology WSIs. These classes enable the representation of large, tiled microscopic images as multi-frame objects, supporting attributes for specimen details, acquisition parameters, and image pyramids for efficient viewing at multiple resolutions. In 2025, advancements such as native output from scanners have streamlined integration with picture archiving and communication systems (PACS), viewers, and tools, significantly reducing the need for format conversions and associated or delays. For instance, the Spring 2025 Working Group 26 (WG-26) Connectathon marked a milestone in , with multiple vendors demonstrating seamless exchange of WSI data across platforms, fostering multi-vendor workflows and aiding for clinical use. Additionally, now incorporates compression, as implemented in ' 2025 SGi pathology scanner, which produces files up to 50% smaller than traditional while preserving diagnostic quality, thus optimizing storage and transmission in resource-constrained environments. OME-TIFF, developed by the Open Microscopy Environment (OME) consortium, extends the TIFF format to handle multidimensional microscopy data, including WSIs in digital pathology, through embedded OME-XML metadata. This XML-based schema standardizes descriptions of experimental conditions, image channels, acquisition instruments, and annotations, ensuring comprehensive and machine-readable provenance information without proprietary extensions. Bio-Formats, an open-source library, reads and writes OME-TIFF files, converting from over 150 proprietary formats while preserving metadata integrity, which has been widely adopted in academic and clinical labs for and analysis pipelines. The File Extension (IFE), introduced in , provides a optimized for high-performance WSI rendering and transfer. IFE supports compressed, multi-resolution tiled data structures with low-overhead indexing, enabling rapid deep-zoom navigation in web-based viewers and reducing latency in real-time applications. Designed for vendor-agnostic use, IFE incorporates modern codecs like and facilitates seamless integration into existing digital pathology ecosystems, addressing bottlenecks in data for large-scale deployments. Initiatives such as the Working Group 26 (WG-26) and the International Health Informatics Standards (IHE) Pathology and Laboratory Medicine () domain have played pivotal roles in overcoming proprietary by coordinating efforts and testing events like Connectathons. These collaborations promote open specifications that enhance liquidity, support federated model training across institutions, and ensure future-proofing against evolving technologies.

Methods

Traditional Image Analysis

Traditional image analysis in digital pathology refers to deterministic, rule-based computational techniques that process whole-slide images to extract quantitative features from histological structures, predating the widespread adoption of methods. These approaches leverage mathematical algorithms to segment, measure, and classify image components, enabling objective assessment of without probabilistic learning. By focusing on pixel-level operations and predefined criteria, traditional methods provide reproducible results for basic quantification tasks in workflows. A core technique is thresholding, which binarizes images by classifying pixels above or below an cutoff to isolate objects like nuclei from the background. , introduced in 1979, automates threshold selection by maximizing the between-class variance in the , making it particularly effective for nuclei detection in stained tissue sections. Following segmentation, morphometric analysis computes geometric attributes such as area, perimeter, and circularity of detected objects; for instance, circularity quantifies how closely a approximates a perfect circle, aiding in the differentiation of normal versus atypical cells. These measurements offer insights into cellular and tissue organization, supporting histopathological evaluation. Software tools like and its extensible variant facilitate the implementation of these techniques through user-friendly interfaces and plugins tailored for . ImageJ, an open-source platform developed since 1997, supports thresholding, morphometrics, and watershed algorithms for separating overlapping objects, while extends it with specialized bioimage analysis capabilities. Color deconvolution complements these by unmixing overlapping stains in images like H&E, decomposing RGB channels into separate hematoxylin (nuclear) and eosin (cytoplasmic) components via basis vector estimation and linear transformation. This preprocessing step, formalized by Ruifrok and Johnston in 2001, enhances the accuracy of downstream feature extraction by isolating stain-specific signals. In practical applications, traditional image analysis automates tasks such as counting mitotic figures to gauge tumor proliferation rates or estimating tumor infiltration scores by quantifying immune cell densities within tissue regions. For example, thresholding-based nuclei detection has been applied to enumerate cells in stained slides, achieving similarity coefficients of approximately 94%, of 0.91, and of 0.89 in cytology images comparable to pathology specimens. Such methods improve inter-observer consistency over manual assessment. Despite these strengths, traditional techniques are limited by their reliance on fixed rules, rendering them sensitive to variations in intensity, scanner calibration, or tissue preparation, which can shift pixel values and degrade segmentation performance. They also falter in managing complex scenarios, such as densely packed or irregularly shaped structures, where rule-based heuristics fail to capture nuanced morphological variations.

Artificial Intelligence and Machine Learning

Artificial intelligence and techniques have advanced digital pathology by automating complex image analysis tasks, surpassing traditional rule-based methods through adaptive . (CNNs) form the backbone of many applications, particularly for semantic segmentation of histological structures. The architecture, a popular CNN variant, excels in tumor boundary detection; for example, it has been applied to segment esophageal in pathological sections with high precision. Similarly, models based on ResNet architectures achieve up to 93% accuracy in classifying HER2 expression levels in immunohistochemistry slides, aiding in decisions. Training these models requires extensive annotated datasets to capture the variability in tissue morphology and staining. (TCGA) provides a seminal resource, encompassing over 20,000 whole-slide images across multiple cancer types for . To mitigate data scarcity in pathology-specific domains, adapts pre-trained models by fine-tuning on images, enhancing classification performance on limited samples. As of 2025, has emerged as a key trend, enabling collaborative model training across institutions without sharing sensitive data, thus preserving while improving generalizability. In predictive modeling, supports risk stratification for treatment response; for instance, systems in analysis have reduced false negatives by up to 9.4% compared to standard protocols. Generative adversarial networks (GANs) further enhance model robustness through , synthesizing realistic histopathological images to balance underrepresented classes in colorectal polyp datasets. These approaches build on traditional image analysis by incorporating end-to-end learning for more nuanced predictions. At the 2025 (ASCO) Annual Meeting, presentations underscored AI's role in precision oncology, showcasing applications for detection and personalized in digital pathology workflows. FDA-designated breakthrough tools, such as Paige.AI's PanCancer Detect, exemplify clinical translation, assisting pathologists in identifying cancer across tissue types.

Applications

Clinical Diagnostics

In clinical diagnostics, digital pathology enables primary through whole-slide (WSI) systems, which digitize slides for histopathological equivalent to traditional microscopy. The U.S. Food and Drug Administration (FDA) approved the first WSI system, the Philips IntelliSite Pathology Solution, in 2017 for primary , confirming its non-inferiority to conventional light microscopy in accuracy and workflow efficiency. This approval paved the way for routine use in , including grading via the Gleason scoring system, where pathologists assess glandular architecture on slides to determine tumor aggressiveness and guide decisions. Oncology applications exemplify digital pathology's clinical impact, particularly in detecting metastases, a critical step in . In cases, AI-assisted WSI analysis has achieved high sensitivities, such as a slide-level of 0.994 and approximately 92% for metastasis detection, matching or surpassing time-constrained unassisted pathologist performance and reducing false negatives in routine diagnostics. Integration with molecular testing further enhances companion diagnostics; for instance, digital pathology platforms support PD-L1 expression assessment in non-small cell to predict response, for example, Roche's uPath PD-L1 (SP263) image analysis algorithm aids in identifying PD-L1 positive tumor cells in NSCLC, combining histological images with genomic data for personalized workflows. Practical benefits include minimized diagnostic errors and accelerated reporting times. Digital workflows have reduced accessioning errors in pathology labs from 6.3% to 0.5% by streamlining slide handling and processes. For frozen sections during intraoperative consultations, facilitates rapid remote consultations, cutting turnaround times by over 50% in some settings while improving detection rates through enhanced visualization. As of late 2024, U.S. laboratory adoption stands at approximately 10%, while European labs show higher adoption rates and stronger growth in the driven by regulatory approvals, reflecting a market projected to expand at 10.8% CAGR through 2033. By mid-2025, further regulatory advancements, including updates to existing WSI systems, continue to support broader adoption. Multidisciplinary applications, such as tumor boards, leverage digital pathology for collaborative patient management. Shared WSI with annotations allows oncologists, surgeons, and radiologists to review cases synchronously, improving treatment planning efficiency by up to 30% in preparation and discussion times. Tools like NAVIFY Tumor Board integrate digital slides into end-to-end workflows, enabling real-time annotations and documentation to support evidence-based decisions in complex cases.

Research, Education, and Drug Development

Digital pathology plays a pivotal role in scientific research by enabling of large patient cohorts through high-throughput image processing and with data. For instance, frameworks like combine images with transcriptomic data to provide detailed spatial descriptions of tumor architecture, facilitating studies on the in cancers such as and tumors. This approach allows researchers to quantify immune cell infiltration and at scale, revealing patterns that traditional microscopy cannot efficiently capture. In prostate cancer research, models trained on datasets from the PANDA challenge have supported hypothesis generation by automating Gleason grading and tumor detection across diverse multi-center slides, accelerating the identification of prognostic features from over 10,000 whole-slide images. In education, digital pathology enhances training for pathologists through virtual microscopy platforms that simulate real-world slide examination without physical hardware. Interactive whole-slide image suites, such as open-source tools for pathology education, allow trainees to annotate and discuss cases remotely, improving accessibility and reducing reliance on costly glass slide collections. These systems have demonstrated cost savings by minimizing the need for microscope laboratories, which can exceed $100,000 annually for a class of 50 students, while enabling self-paced learning. Additionally, digital archives support the simulation of rare pathological cases by curating annotated whole-slide images of uncommon tumors, allowing repeated practice and collaborative review to build expertise in low-prevalence conditions like certain lymphomas. In , digital pathology streamlines quantification during clinical trials, particularly for response predictors. AI-powered tools for PD-L1 expression scoring provide continuous, cell-level quantification on tumor cells from slides, offering greater precision than manual methods and identifying more eligible patients at thresholds like ≥1% or ≥5%. This has improved patient selection in non-small cell trials, where digital scoring correlated with better outcomes in atezolizumab-treated cohorts compared to manual assessment. At the 2025 ASCO annual meeting, studies highlighted AI-driven spatial s from digital pathology for stratifying patients in advanced trials, predicting immune checkpoint inhibitor responses through of the . Collaborative platforms further advance global research by providing open repositories for sharing whole-slide images and annotations. Initiatives like the PANDA challenge repository enable multi-institutional access to large-scale datasets for AI model validation and hypothesis testing in . Open-source systems, such as distributed networks for virtual slide management, facilitate secure data exchange among researchers worldwide, supporting without compromising patient privacy. These resources promote and accelerate discoveries in tumor by allowing pooled analysis of diverse cohorts.

Challenges

Technical and Validation Issues

Digital pathology faces several technical challenges related to image acquisition and processing. Scanner artifacts, such as —which causes uneven illumination and reduced brightness at image edges—and drift, which leads to riness in out-of-focus regions, can compromise image quality during whole slide imaging (WSI). These issues arise from hardware limitations in scanning large sections and have been documented in studies evaluating WSI systems, where and artifact detection algorithms are essential for mitigation. Additionally, data compression techniques, particularly lossy methods like , introduce losses that degrade resolution and affect downstream analyses, with performance decreasing significantly as compression ratios increase beyond visually lossless thresholds. Such compression artifacts can alter pixel values, impacting AI-based diagnostic accuracy. Handling the immense data volumes generated by WSI exacerbates these challenges. Whole slide images often span gigapixel scales, with a single ×40 magnification scan producing files up to several gigabytes, necessitating robust solutions. Large pathology centers may generate over 1 petabyte of uncompressed data annually, while full workflows in mid-sized labs require at least 100 TB of per year to accommodate ongoing case volumes and . Validation of pathology systems relies on concordance studies that compare readings to traditional glass slide , aiming to demonstrate non-inferiority. The 2017 FDA approval of the IntelliSite Pathology Solution was based on a large multicenter study involving nearly 2,000 slides, which reported major discordance rates of approximately 4.7% for versus 4.4% for optical , establishing non-inferiority for primary diagnosis. Scanner-specific validations, aligned with accreditation standards for medical laboratories, involve verifying image fidelity, color accuracy, and resolution through internal quality checks and pathologist proficiency testing to ensure diagnostic equivalence. Standardization gaps persist due to variability in and scanning protocols across institutions, which can introduce inconsistencies in color, intensity, and representation. This variability contributes to diagnostic discrepancies, with studies reporting major discordance rates of 3-4% between digital and conventional methods, often linked to protocol differences. In 2025, efforts to enhance model in digital pathology include the development of foundation models trained on datasets to improve and reduce training variability, as well as guidelines for standardized frameworks to ensure consistent model performance across scanners. Quality assurance in digital pathology incorporates metrics like (SNR) to quantify image fidelity, where higher SNR values indicate clearer separation of tissue signals from , aiding in the detection of subtle pathological features. Automated drift correction algorithms, such as those using electrically tunable lenses or refocusing in WSI , address focus instability during acquisition, enabling continuous tracking and correction to maintain high-resolution imaging over extended scans.

Regulatory and Implementation Barriers

The regulatory landscape for digital pathology encompasses stringent oversight in major jurisdictions to ensure safety and efficacy. In the United States, the Food and Drug Administration (FDA) classifies whole slide imaging systems used in digital pathology as Class II medical devices, subjecting them to the 510(k) premarket notification pathway, which requires demonstration of substantial equivalence to predicate devices through clinical and performance data. In the European Union, the In Vitro Diagnostic Regulation (IVDR) categorizes digital pathology slide scanners and systems as Class C devices, necessitating conformity assessments by notified bodies to verify compliance with essential requirements for performance, safety, and quality control. As of 2025, updates to frameworks for artificial intelligence (AI) in software as a medical device (SaMD) have intensified scrutiny; the FDA has issued guidance on AI/ML-based SaMD lifecycles, including predetermined change control plans for post-market modifications, while the EU AI Act imposes risk-based obligations, classifying high-risk AI in diagnostics like pathology as requiring third-party conformity assessments and transparency measures. Approval processes for digital pathology tools, particularly AI-enabled ones, involve rigorous validation but face notable hurdles. For instance, in 2021, received FDA de novo clearance for its Paige Prostate software, the first AI application authorized to assist in detecting and characterizing in whole slide images, based on clinical studies demonstrating improved pathologist efficiency and accuracy. However, validating AI algorithms for diverse populations remains challenging due to biases in training datasets, which often underrepresent ethnic minorities and vary in tissue preparation across global labs, potentially leading to reduced performance in underrepresented groups and requiring extensive, inclusive validation studies to meet regulatory standards. Implementation barriers extend beyond to practical and economic obstacles. High initial costs for digital pathology systems, including , servers, and software, often exceed $500,000, deterring adoption in budget-constrained facilities despite long-term savings in workflow efficiency. Cybersecurity risks further complicate deployment, as sharing large whole slide images over networks exposes systems to threats like and data breaches, which could compromise confidentiality and diagnostic without robust and access controls. Additionally, training gaps persist, with surveys indicating that many pathologists lack proficiency in digital tools, underscoring the need for targeted programs. Global disparities exacerbate these barriers, particularly in low-resource settings where adoption lags due to inadequate , such as unreliable and high-speed , limiting the feasibility of scanner deployment and integration. Initiatives like those from the promote equitable access through guidelines on technologies, emphasizing scalable, low-cost solutions and international collaboration to bridge gaps in services for underserved regions.

Future Directions

Emerging Technologies

Hyperspectral imaging extends beyond traditional RGB-based microscopy by capturing over 100 spectral bands, enabling detailed molecular phenotyping of tissues without physical staining. This technology leverages the unique spectral signatures of biomolecules to differentiate cellular components and pathological states, such as tumor heterogeneity in cancer samples. For instance, hyperspectral systems integrated with machine learning have demonstrated high accuracy in virtual staining and autofluorescence analysis, reducing the need for invasive procedures. Complementing this, multiplex imaging allows simultaneous detection of multiple biomarkers in a single tissue section, providing spatial context for immune cell interactions and disease progression. Platforms for multiplexed immunofluorescence have enabled quantitative phenotyping of up to nine markers, enhancing precision in immuno-oncology diagnostics. These advancements in hyperspectral and multiplex approaches are poised to transform routine pathology workflows by integrating molecular data directly into digital slides. Emerging integrations of these imaging modalities with technologies are facilitating live-cell imaging applications relevant to , where /Cas systems enable real-time visualization of genomic loci in dynamic cellular environments. By tagging endogenous proteins with fluorescent markers via , researchers can track pathological processes, such as protein misfolding in neurodegenerative diseases, within living tissues. This non-invasive labeling supports high-resolution, time-lapse imaging that bridges static with live dynamics, potentially accelerating drug screening for precision medicine. In advanced AI applications, explainable AI (XAI) methods are addressing the black-box limitations of models in by providing transparent rationales for diagnostic decisions. XAI techniques, such as attention maps and counterfactual explanations, allow pathologists to verify outputs for tasks like tumor grading, improving trust and clinical adoption. Meanwhile, shows promise for accelerating feature extraction from large-scale datasets, where quantum algorithms can process complex image correlations faster than classical systems. Early quantum-enhanced has demonstrated potential in handling high-dimensional medical data, addressing noise and variability in large cohorts. Other innovations include volumetric scanning, which captures thick sections to preserve spatial lost in 2D slicing. Techniques like light-sheet microscopy and deep learning-based reconstruction enable non-destructive imaging of entire organoids or biopsies, revealing tumor microenvironments with sub-cellular . technology is emerging for secure data sharing in digital pathology, using decentralized ledgers to ensure tamper-proof transmission of whole-slide images while maintaining patient privacy through cryptographic hashing. Prototypes have demonstrated reliable multi-institution for rare disease annotations without compromising data integrity. Additionally, 2025 prototypes of (AR) and (VR) systems are enabling immersive slide review, overlaying AI annotations onto microscopic views for collaborative diagnostics. AR microscopy, for example, integrates real-time holograms to highlight regions of interest, reducing review time by up to 30% in pilot studies. Integration trends are fusing digital pathology with through digital-spatial , where transcriptomic data is co-registered with histological images to map . Tools like platforms align multi-omics layers, uncovering spatial patterns in tumor that inform targeted therapies. is automating processes, from sectioning to , with systems like SectionStar employing AI-guided arms to standardize workflows and minimize in high-throughput settings. These synergies are setting the stage for fully integrated, AI-orchestrated labs by the late 2020s.

Market Growth and Global Impact

The global digital pathology market was valued at USD 1.39 billion in 2024 and is projected to reach USD 2.97 billion by 2033, expanding at a (CAGR) of 8.6%. As of October 2025, estimates indicate the market will reach USD 1.83 billion in 2025 and grow to USD 2.9 billion by 2030 at a CAGR of 9.65%. This trajectory is primarily driven by strategic partnerships integrating (AI) with pathology workflows, enabling enhanced diagnostic accuracy and efficiency. In 2025, nearly half of announced partnerships in the sector emphasized and , including AI-laboratory information system (LIS) platforms to streamline and automate routine tasks. The clinical segment, encompassing disease diagnosis and treatment planning, represents the largest application area and is poised for robust expansion. Growth drivers include rising demand for precision oncology and chronic disease management, bolstered by AI-enabled tools for faster image analysis. Emerging markets, particularly in the region, are experiencing accelerated adoption, with the sub-regional market projected to grow at a CAGR of 9.8% from 2025 to 2033, fueled by healthcare infrastructure investments and increasing cancer incidence. On a global scale, digital pathology holds significant potential to address diagnostic disparities in low- and middle-income countries (LMICs) through cloud-based telepathology platforms, which facilitate remote consultations and reduce turnaround times for reports. By supporting precision initiatives, such as personalized cancer therapies based on molecular profiling, it contributes to improved outcomes, including enhanced prognostic accuracy in . Societally, the field is reshaping pathologist roles from manual slide reviewers to oversight specialists focused on validation and complex case interpretation, while raising ethical concerns around data equity to ensure equitable access across diverse populations.

References

  1. [1]
    Digital Pathology: Transforming Diagnosis in the Digital Age - PMC
    Sep 3, 2023 · Digital pathology is a transformative process that converts glass slides containing tissue samples into high-resolution digital images. This ...
  2. [2]
    Introduction to digital pathology and computer-aided pathology - PMC
    Feb 13, 2020 · This review describes various concepts related to DP and computer-aided pathologic diagnosis (CAPD), current applications of DP, and various issues related to ...
  3. [3]
    Digital Pathology: Advantages, Limitations and Emerging Perspectives
    Nov 18, 2020 · The advent of digitized images to pathology has propelled this traditional field into what is now described as digital pathology (DP). Digital ...
  4. [4]
    a white paper from the Digital Pathology Association - PMC
    In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology.
  5. [5]
    Whole Slide Imaging (WSI) in Pathology: Current Perspectives and ...
    May 28, 2020 · Digital imaging is widely used by pathologists for creation of static images using microscope-dedicated optical cameras and, more recently, ...
  6. [6]
    PSY - Product Classification - FDA
    Whole slide imaging system. Definition, The whole slide imaging system is an automated digital slide creation, viewing, and management system intended as an aid ...
  7. [7]
    Digital pathology systems enabling quality patient care - PMC
    Jul 17, 2023 · Digital pathology systems impact clinical, education, and research use cases where pathologists use digital technologies to perform tasks in lieu of using ...
  8. [8]
    Aperio Slide Scanning Core - Penn State College of Medicine
    ... whole slide imaging for up to 400 slides at a time. The AT2 generates bright-field whole slide images at 20X (0.50µm/pixel) or 40X (0.25µm/pixel) ...
  9. [9]
    Enhanced resolution 3D digital cytology and pathology with dual ...
    Jul 9, 2019 · Although both studies have demonstrated that 3D imaging and reconstruction can be more informative than traditional 2D histology, the serial ...
  10. [10]
    Recommendations for pathologic practice using digital ... - PMC - NIH
    Digital pathology (DP) refers to the use of a digital scanner to convert and save pathologic slides as digital images and the use of those images for pathologic ...
  11. [11]
    A Survival Guide for the Rapid Transition to a Fully Digital Workflow
    Oct 16, 2021 · A useful “handy” guide to lead the digital transition of “analog”, non-tracked pathology laboratories following the experience of the Caltagirone pathology ...
  12. [12]
    Best Practice Recommendations for the Implementation of a Digital ...
    Nov 22, 2021 · Best practice recommendations for the implementation of a digital pathology workflow in the anatomic pathology laboratory.
  13. [13]
    Classification of the Whole Slide Imaging System - Federal Register
    Jan 2, 2018 · The whole slide imaging system is an automated digital slide creation, viewing, and management system intended as an aid to the pathologist to ...
  14. [14]
    Comparison of the efficiency of digital pathology with the ... - NIH
    Apr 1, 2025 · Additionally, DP decreased the pathologist workload by 29.2% on average, with reductions exceeding 50% during peak months. Conclusion. Our study ...
  15. [15]
    An equivalency and efficiency study for one year digital pathology ...
    Feb 18, 2025 · Our results demonstrate a 99% concordance between the analog and digital reports while at the same time reducing the time to sign out a case by ...
  16. [16]
    Telepathology. Long-distance diagnosis - PubMed
    Potential benefits of telepathology include providing a means of conveniently delivering pathology services in real-time to remote sites or underserviced areas ...
  17. [17]
    Digital pathology and artificial intelligence in translational medicine ...
    Oct 5, 2021 · The current advances in digital pathology offer practical advantages over manual pathology, including enhanced accuracy and precision, the ...
  18. [18]
    Interobserver and intraobserver reproducibility in digital and routine ...
    Interobserver agreement for routine microscopy was excellent for primary Gleason grade (κ = 0.72) and good for all other parameters (κ ranging from 0.36 to 0.55) ...
  19. [19]
    Implementation of Digital Pathology Offers Clinical and Operational ...
    With a decrease in archival glass slide transport between off-site storage ... storage costs by moving their physical storage facilities to remote, less ...
  20. [20]
    WSI commercial platforms - Pathology Outlines
    Nov 22, 2023 · WSI commercial platforms · Capacity: 420 slides per day (15 x 15 mm specimen); load an entire case at once and view all slides live; scan or view ...
  21. [21]
    Invention and Early History of Telepathology (1985-2000) - PMC
    Jan 24, 2019 · This narrative-based paper provides a first-person account of the early history of telepathology (1985–2000) by the field's inventor, Ronald S. Weinstein, MD.
  22. [22]
    Telepathology - an overview | ScienceDirect Topics
    Weinstein, now described to be as the “father of telepathology” in a 1986 editorial outlined the actions that would be needed to create remote pathology ...
  23. [23]
    Microscopic Image Photography Techniques of the Past, Present ...
    May 19, 2015 · Digital cameras began to gain popularity through the late 1990s and early 2000s, and microscopic image photography followed the shift from ...
  24. [24]
    Twenty Years of Digital Pathology: An Overview of the Road ... - PMC
    Nov 21, 2018 · The first commercial slide scanner, called the BLISS (Bacus Laboratories Inc., Slide Scanner) system (http://www.jamesbacus.com/page10.html), ...Missing: 1999 | Show results with:1999
  25. [25]
    A Practical Guide to Whole Slide Imaging: A White Paper From the ...
    Oct 11, 2018 · Whole slide imaging (WSI) represents a paradigm shift in pathology ... It was first described by Wetzel and Gilbertson in 1999.2 The ...BASICS OF OPERATION · USE CASES · THE FUTURE OF WSI
  26. [26]
    Reconciliation of diverse telepathology system designs. Historic ...
    Mar 19, 2012 · Actually, a feature of the Weinstein groups' robotic system was the incorporation of a low resolution static-image module that was used, by his ...
  27. [27]
    (PDF) Twenty Years of Digital Pathology: An Overview of the Road ...
    Oct 31, 2025 · ... first digital pathology system for primary diagnosis in surgical pathology. ... Pathology, University of Pittsburgh Medical Center. Shadyside, ...
  28. [28]
    [PDF] Telepathology - Royal College of Pathologists
    Telepathology: Guidance from The Royal College of Pathologists. October 2013. Author. Professor James Lowe, Chair of the Specialty Advisory Committee on ...
  29. [29]
    FDA allows marketing of first whole slide imaging system for digital ...
    Apr 12, 2017 · The US Food and Drug Administration today permitted marketing of the Philips IntelliSite Pathology Solution (PIPS), the first whole slide imaging (WSI) system.
  30. [30]
    A Brief History of the DPA - Digital Pathology Association
    Feb 3, 2017 · The year 2007 signified an important inflection point in the history of digital pathology and the continuation of this new and disruptive ...
  31. [31]
    Digital Pathology Trends and the Impact of COVID-19 on Speed of ...
    Oct 29, 2020 · The COVID-19 pandemic accelerated the adoption of digital pathology in this country for a number of reasons.
  32. [32]
    Digital Pathology: The Next Hurdles | Imaging Technology News
    Mar 21, 2022 · When the FDA relaxed these regulations in April 2020 to support healthcare providers dealing with COVID-19, the adoption of new entrants soared.
  33. [33]
    Slow Digital Pathology Adoption Continues, According to Labcorp ...
    Jan 30, 2025 · A recent report from Labcorp indicates that just one-third of clinical laboratories have carried out plans for whole-slide imaging.
  34. [34]
    Digital Pathology and AI Highlights from ASCO 2025 - Proscia
    Jun 16, 2025 · The latest technology is making significant strides in improving diagnostic accuracy, predicting treatment response, and refining risk stratification across ...Missing: conference | Show results with:conference
  35. [35]
    Advancements in pathology: Digital transformation, precision ...
    Cloud-based solutions. The adoption of cloud-based solutions for telepathology can facilitate the storage, sharing, and analysis of DP images on a global scale.
  36. [36]
    New European Union Regulations Related to Whole Slide Image ...
    This paper reviews the current status of the European Union (EU) regulation on in vitro diagnostic medical devices (IVD-MDs).
  37. [37]
    Japan Pathology Lab Services Market Report & Outlook 2033
    The market is driven by the rapid adoption of digital pathology and AI, supported by government initiatives promoting digital healthcare transformation and ...
  38. [38]
    Digitization of Pathology Labs: A Review of Lessons Learned
    One advantage of working digitally is that pathologists do not need to handle physical slides with residual chemicals. For a quantitative comparison, the number ...
  39. [39]
    Digital Pathology Market Size & Growth Forecast to 2030
    The global digital pathology market, valued at US$1.30 billion in 2024, stood at US$1.46 billion in 2025 and is projected to advance at a resilient CAGR of 13.5 ...
  40. [40]
    FAQs - Digital Pathology Association
    A few benefits of digital pathology include high throughput scanning of glass slides, quantitative analysis of whole slide images, immediate web based ...Missing: 100 day
  41. [41]
    High‐throughput whole‐slide scanning to enable large‐scale data ...
    Whole‐slide scanning may include the capture of patient information either directly or indirectly to support the clinical implementation of digital pathology ...
  42. [42]
    Aperio GT 450 DX Scanner - Digital Pathology - Leica Biosystems
    How many slides per hour can it scan? 81 slides per hour @ 40x (15mm x 15mm area). What is the scan speed? 32 seconds at 40x for 15mm x 15mm area. Does it ...
  43. [43]
    Pathology Scanner Second Generation SG300 - Philips
    Each slide is scanned at the equivalence of 40 times magnification (0.25 um/pixel). This results in sharp high resolution images which are vital for digitizing ...<|separator|>
  44. [44]
    Quantitative assessment of H&E staining for pathology - PMC - NIH
    Feb 23, 2024 · We propose a method for quantitative haematoxylin and eosin stain assessment to facilitate quality assurance of histopathology staining.
  45. [45]
    Aperio format - OpenSlide
    Aperio format. Format: single-file pyramidal tiled TIFF, with non-standard metadata and compression ... Aperio slides are stored in single-file TIFF format.Missing: digital pathology
  46. [46]
    Hamamatsu format - OpenSlide
    NDPI consists of a single TIFF-like file with some custom TIFF tags. VMS and NDPI contain JPEG images; VMU contains NGR images (a custom uncompressed format).
  47. [47]
    [PDF] Philips' iSyntax for Digital Pathology
    The Philips' iSyntax image format for pathology whole slide images was designed to combine the medical grade image quality of. JPEG 2000 with the speed of JPEG, ...Missing: XML | Show results with:XML
  48. [48]
    SVS image files - Paul Bourke
    Some Aperio files use compression type 33003 or 33005. Images using this compression need to be decoded as a JPEG 2000 codestream. For 33003: YCbCr format ...Missing: structure digital pathology
  49. [49]
    Working with Aperio SVS files in Matlab – Introduction
    Jan 12, 2015 · Aperio scanners generate a semi-proprietary file format called SVS. At its heart, SVS files are really a multi-page tiff file storing a pyramid of smaller tiff ...Missing: structure JPEG<|separator|>
  50. [50]
    Bringing Open Data to Whole Slide Imaging - PMC
    Jul 3, 2019 · Here we describe the structure of this format, its performance characteristics, as well as an open-source library support for reading and writing pyramidal OME ...
  51. [51]
    [PDF] The Leeds Guide to Digital Pathology
    Our experience demonstrates that, on average, a slide scanned at 40x will produce between 1GB – 2GB of data, subject to the size of tissue with a 20x image ...
  52. [52]
    Solving Proprietary Format Challenges in Digital Pathology Workflows
    Proprietary slide formats such as Philips iSyntax, Hamamatsu NDPI, Leica Aperio SVS, and TIFF introduce barriers to enterprise imaging integration. Some ...Missing: Ventana MRXS
  53. [53]
    [PDF] Precision Pathology – Integrated Solutions - Visiopharm
    The different scanners come with a proprietary file format, which will likely be the case for several years to come. In a time with multi-lab consolidation ...Missing: BFF | Show results with:BFF<|control11|><|separator|>
  54. [54]
    Ventana format - OpenSlide
    Ventana slides are stored in single-file BigTIFF format. OpenSlide will detect a file as Ventana if: The file is TIFF. The XMP tag contains valid XML. The XML ...Missing: structure | Show results with:structure
  55. [55]
    Supported whole slide image formats - Pathomation
    Supported whole slide image formats ; 3DHistech MRXS .mrxs, JPEG, JPEGXR, PNG, BMP ; Aperio / Leica AFI (Image Set) .afi, JPEG, JPEG2000 ; Aperio / Leica CWS .cws ...Missing: proprietary | Show results with:proprietary
  56. [56]
    Developing image analysis methods for digital pathology - PMC
    This review begins by introducing the main approaches and techniques involved in analysing pathology images.
  57. [57]
    https://imageanalysis.visiopharm.com/hubfs/Knowledge%2520Base%25202020/Downloadables/brochure-integration-a4.pdf
  58. [58]
    https://openslide.org/formats/ventana/
  59. [59]
    https://www.pathomation.com/formats/
  60. [60]
    Digital pathology and computational image analysis in ... - Nature
    In this Review, we discuss how developments in digital pathology and computational image analysis are shaping a new digital era for nephropathology.
  61. [61]
    Digital pathology image analysis: opportunities and challenges - PMC
    Traditional methods of analysis of cancer samples were limited to a few variables, usually stage, grade and the measurement of a few clinical markers, such ...
  62. [62]
    The development and validation of pathological sections based U ...
    This study developed U-Net based deep learning segmentation models for esophageal squamous cell carcinoma.
  63. [63]
    A HER2 classification method based on deep residual network - NIH
    Jan 28, 2022 · Our proposed HER2-ResNet achieved 93% accuracy, 92% recall, 91% F-score and 91% precision. The higher accuracy and F-score suggest that the ...
  64. [64]
    Cancer Digital Slide Archive: an informatics resource to support ...
    Jul 26, 2013 · The vast amount of TCGA WSIs (20 000+ images) is much larger than anything managed by existing non-commercial options, prompting us to create ...
  65. [65]
    Effectiveness of transfer learning for enhancing tumor classification ...
    Dec 14, 2020 · Transfer learning has been used to effectively train the CNN model with the limited dataset, which could enhance the model performance by using ...
  66. [66]
    [PDF] Institutional AI in Healthcare via Digital Pathology - Preprints.org
    Sep 16, 2025 · Federated Learning (FL) holds great potential for transforming digital pathology [48–50] by enabling collaborative training of AI models across ...
  67. [67]
    Artificial intelligence for breast cancer: Implications for diagnosis and ...
    They found absolute reductions of 5.7 % and 1.2 % in false positives and 9.4 % and 2.7 % in false negatives (U.S. and U.K. datasets, respectively) [59].
  68. [68]
    Generative Image Translation for Data Augmentation in Colorectal ...
    We present an image translation approach to generate augmented data for mitigating data imbalances in a dataset of histopathology images of colorectal polyps.
  69. [69]
    U.S. FDA Grants Paige Breakthrough Device Designation for AI ...
    Apr 3, 2025 · Paige PanCancer Detect recognized by FDA as a Breakthrough Device intended to assist pathologists in the detection of cancer across multiple tissue and organ ...
  70. [70]
    [PDF] DEN200080 B. Purpose for Submis - accessdata.fda.gov
    Paige Prostate is a software only device intended to assist pathologists in the detection of foci that are suspicious for cancer during the review of ...
  71. [71]
    Diagnostic Assessment of Deep Learning Algorithms for Detection of ...
    Dec 12, 2017 · This diagnostic accuracy study compares the ability of machine learning algorithms vs clinical pathologists to detect cancer metastases in ...
  72. [72]
    Roche granted FDA Breakthrough Device Designation for first AI ...
    This Breakthrough Device Designation (BDD) demonstrates Roche's continued innovation in companion diagnostics and digital pathology to enable more precise ...
  73. [73]
    Clinical implementation of artificial-intelligence-assisted detection of ...
    Jun 27, 2024 · Secondary endpoints showed significant time reductions and up to 30% improved sensitivity for AI-assisted pathologists. This trial demonstrates ...
  74. [74]
    Digital pathology at 10% adoption and in lab plans - CAP TODAY
    Dec 19, 2024 · We're at about 10 percent adoption, but organizations have this on their plan. Organizations of all types are hiring titles now for digital ...<|separator|>
  75. [75]
    Europe Digital Pathology Market Size, Share & Growth, 2033
    Oct 10, 2025 · The Europe digital pathology market is forecasted to grow at a CAGR of 10.8% from 2025 to 2033. Countries of Europe profiled in this report ...Missing: MDR | Show results with:MDR<|separator|>
  76. [76]
    The Use of an Integrated Digital Tool to Improve the Efficiency of ...
    Jan 28, 2025 · This study assessed the impact of the navify Tumor Board digital tool on multidisciplinary team tumor boards' (MDTs) efficiency.
  77. [77]
    A New Software Platform to Improve Multidisciplinary Tumor Board ...
    The NAVIFY Tumor Board solution provides an end-to-end, collaborative workflow that enables coordinating, scheduling, preparing, presenting and documenting ...
  78. [78]
    Integrating histopathology and transcriptomics for spatial tumor ...
    Nov 7, 2024 · This study introduces SPoTLIghT, a computational framework providing a quantitative description of the tumor architecture from hematoxylin and eosin (H&E) ...
  79. [79]
    Next-Generation Digital Histopathology of the Tumor ...
    Analysis of the tumor immune microenvironment using next-generation digital pathology. A representative example of the automated detection of CD8+ immune ...
  80. [80]
    Artificial intelligence for diagnosis and Gleason grading of prostate ...
    Jan 13, 2022 · Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer ...
  81. [81]
    An Open Source, Whole Slide Image-based Pathology Education ...
    Our objective was to create an inexpensive whole slide image (WSI) education suite to address these limitations and improve the education of pathology trainees.
  82. [82]
    Digitizing pathology training: Opportunities and advantages
    Apr 14, 2023 · calculated the cost of a microscope laboratory for 50 students to be about $100,000 per year, which approaches the complete start-up cost for ...Missing: atlases reduction
  83. [83]
    The Effect of Window Size on Pathologists' Search for Rare ...
    Jan 2, 2025 · Digital pathology requires pathologists to assess tissue digitally rather than on an analog microscope, which has been the mainstay tool for ...
  84. [84]
    Enhanced patient selection with quantitative continuous scoring of ...
    Sep 18, 2025 · In this work, we present PD-L1 Quantitative Continuous Scoring (PD-L1 QCS), a computer vision system for granular cell-level quantification of ...
  85. [85]
    Digital Versus Manual PD-L1 Scoring in Advanced NSCLC From the ...
    Aug 2, 2025 · This demonstrates the clinical utility of digital pathology-based scoring to identify new subgroups of patients benefiting from atezolizumab ...
  86. [86]
    Digital pathology–based AI spatial biomarker to predict outcomes for ...
    May 28, 2025 · A single-cell computational pathology approachidentifies interpretable spatial biomarkers that predict ICI response and outcomes in advanced lung cancer.
  87. [87]
    Home - The PANDA challenge - Grand Challenge
    The PANDA challenge involves developing models to detect prostate cancer on tissue images and estimate severity using a large multi-center dataset.
  88. [88]
    A Flexible, Open, Decentralized System for Digital Pathology Networks
    In this paper, we describe a distributed collaborative system developed for pathologists who must manage and share large numbers of virtual slides as well as ...Missing: SlideHub | Show results with:SlideHub
  89. [89]
    Open Practices and Resources for Collaborative Digital Pathology
    Nov 14, 2019 · In this paper, we describe open practices and open resources in the field of digital pathology with a specific focus on approaches that ease ...
  90. [90]
    New European Union Regulations Related to Whole Slide Image ...
    Jan 24, 2019 · According to the new EU IVDR, digital pathology slide scanners and complete digital pathology slide systems should be considered as Class C ...
  91. [91]
    Artificial Intelligence in Software as a Medical Device - FDA
    Mar 25, 2025 · This paper is intended to complement the "AI/ML SaMD Action Plan" and represents a commitment between the FDA's Center for Biologics Evaluation ...FDA Digital Health and... · Research on AI/ML-Based... · Draft Guidance
  92. [92]
    The FDA vs. EU AI Act: What Regulatory Teams Must Know Now
    Looking Beyond FDA and EU: The Global AI Regulatory Landscape. While FDA and EU lead, AI medical device regulation is expanding worldwide. In ...Missing: pathology | Show results with:pathology
  93. [93]
    [PDF] September 21, 2021 Paige.AI, Inc. Emre Gulturk Senior Director of ...
    Sep 21, 2021 · FDA concludes that this device should be classified into Class II. This order, therefore, classifies the Paige Prostate, and substantially ...
  94. [94]
    Artificial intelligence in digital pathology: a systematic review and ...
    May 4, 2024 · The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta- ...
  95. [95]
    AI in Pathology: What could possibly go wrong? - ScienceDirect
    We discuss the technological, clinical, legal, and sociological factors that will influence the adoption of AI in pathology, and its eventual impact for good ...Missing: ratio | Show results with:ratio<|control11|><|separator|>
  96. [96]
    Digital Pathology Market - healthHQ
    A typical digital pathology system, which includes a slide scanner, an image server, and software, costs around USD 500,000 to USD 700,000. The average price of ...
  97. [97]
    Protecting Your Pathology: Cybersecurity for Digital Workflows
    Jul 19, 2024 · Digital pathology opens up new cybersecurity risks for labs—but simple steps can mitigate those risks and increase digital resilience.
  98. [98]
    [EPUB] Pathologists' user experience in the era of digital pathology - Frontiers
    Sep 1, 2025 · It increases diagnostic accuracy and efficiency, enhances workflow productivity, and reduces human error. It eases remote diagnosis and ...
  99. [99]
    Implementation of digital pathology in a low-resource setting
    Sep 20, 2025 · This advancement eliminates the need to transport glass slides between hospitals, ensuring their secure storage in centralized laboratories (Ho ...<|separator|>
  100. [100]
    A suggested way forward for adoption of AI-Enabled digital ...
    May 18, 2023 · Even in low resource settings, pathologists have started utilizing digital images by using microscope cameras for the purpose of education, ...
  101. [101]
    An End-to-End Platform for Digital Pathology Using Hyperspectral ...
    We present a novel end-to-end workflow for pathology powered by hyperspectral microscopy and deep learning.
  102. [102]
    Advancing hyperspectral imaging and machine learning tools ...
    Dec 6, 2024 · The review aims to appraise the present advancement and challenges in HSI for medical applications.
  103. [103]
    Deep cell phenotyping and spatial analysis of multiplexed imaging ...
    Jun 15, 2024 · To examine the role of cellular niches, spatial clustering has been introduced to digital pathology and multiplexed imaging in recent years.
  104. [104]
    A platform-independent framework for phenotyping of multiplex ...
    Multiplex imaging is a powerful tool to analyze the structural and functional states of cells in their morphological and pathological contexts.
  105. [105]
    CRISPR/Cas-Based Techniques for Live-Cell Imaging and Bioanalysis
    Aug 30, 2023 · When a bacterial cell is invaded by a virus, it integrates a portion of the viral DNA into its own CRISPR region, forming new spacer sequences.
  106. [106]
    Live cell imaging of low- and non-repetitive chromosome loci using ...
    Mar 14, 2017 · Recently, the RNA-guidable feature of CRISPR-Cas9 has been utilized for imaging of chromatin within live cells. However, these methods are ...
  107. [107]
    The explainability paradox: Challenges for xAI in digital pathology
    This study reveals challenging dilemmas faced by developers of xAI solutions for medicine and proposes empirically-backed principles for their safer and more ...
  108. [108]
    Review of medical image processing using quantum-enabled ...
    Sep 20, 2024 · Increased computational power: Quantum computing has great potential to perform certain computations in parallel, which allows them to possibly ...
  109. [109]
    An end-to-end workflow for non-destructive 3D pathology - PMC - NIH
    Recent advances in 3D pathology offer the ability to image orders-of-magnitude more tissue than conventional pathology while providing a volumetric context ...
  110. [110]
    Privacy-preserving pathological data sharing among multiple remote ...
    This paper presents a novel solution that leverages blockchain technology to ensure reliability in pathological data sharing.
  111. [111]
    Augmented reality microscopy to bridge trust between AI ... - Nature
    May 12, 2025 · To understand the factors that shape AI trustworthiness in pathology practice, we devised a framework implementing an augmented reality ...Missing: AR | Show results with:AR
  112. [112]
    FUSION: a web-based application for in-depth exploration of multi ...
    Sep 25, 2025 · Structure-level analysis of digital pathology image data. At a granular level, FUSION enables the analysis of distributions of derived features ...
  113. [113]
    Cutting-edge technology and automation in the pathology laboratory
    Nov 6, 2023 · In this paper, we describe the state-of-the-art of automation in pathology laboratories in order to lead technological progress and evolution.
  114. [114]
    Digital pathology market projected for significant growth
    Jul 17, 2025 · The Global Digital Pathology Market is projected to be valued at US$1.10 billion in 2024 and reach US$1.73 billion by 2030, ...<|control11|><|separator|>
  115. [115]
    Digital & Computational Pathology: Partnership Momentum in 2025
    May 27, 2025 · Nearly half of all 2025 partnerships so far have focused on improving integration—whether between AI algorithms, image management systems, ...
  116. [116]
    Digital Pathology Market Size, Share | Industry Report, 2033
    The global digital pathology market size was valued at USD 1.39 billion in 2024 and is expected to reach USD 2.97 billion by 2033, growing at a CAGR of 8.6% ...Missing: MDR | Show results with:MDR
  117. [117]
    Asia Pacific Digital Pathology Market Size & Outlook, 2033
    The Asia Pacific digital pathology market generated a revenue of USD 297.5 million in 2024. · The market is expected to grow at a CAGR of 9.8% from 2025 to 2033.
  118. [118]
    https://pmc.ncbi.nlm.nih.gov/articles/PMC11062949/
  119. [119]
    Unique Opportunities and Challenges in Digital Pathology ...
    Aug 8, 2025 · By leveraging technology, telepathology, and integration with computer-aided diagnostic tools, digital pathology can improve access to prompt ...
  120. [120]
    The Rise of AI-Assisted Diagnosis: Will Pathologists Be Partners or ...
    Sep 11, 2025 · Furthermore, the U.S. FDA has granted Breakthrough Device Designation to Paige's PanCancer Detect (https://www.paige.ai/diagnostic-ai; accessed ...
  121. [121]
    The ethical challenges of artificial intelligence‐driven digital pathology
    Feb 17, 2022 · Key ethical issues in AI-driven digital pathology include privacy, choice, equity, and trust, which are central to data use for research.