Digitization of Pathology Labs: A Review of Lessons Learned.

Pathology laboratories are increasingly using digital workflows. This has the potential of increasing laboratory efficiency, but the digitization process also involves major challenges. Several reports have been published describing the individual experiences of specific laboratories with the digitization process. However, a comprehensive overview of the lessons learned is still lacking. We provide an overview of the lessons learned for different aspects of the digitization process, including digital case management, digital slide reading, and computer-aided slide reading. We also cover metrics used for monitoring performance and pitfalls and corresponding values observed in practice. The overview is intended to help pathologists, information technology decision makers, and administrators to benefit from the experiences of others and to implement the digitization process in an optimal way to make their own laboratory future-proof.

Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology.

Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is important to evaluate their predictive performance and obtain regulatory approval. This assessment requires appropriate test datasets. However, compiling such datasets is challenging and specific recommendations are missing. A committee of various stakeholders, including commercial AI developers, pathologists, and researchers, discussed key aspects and conducted extensive literature reviews on test datasets in pathology. Here, we summarize the results and derive general recommendations on compiling test datasets. We address several questions: Which and how many images are needed? How to deal with low-prevalence subsets? How can potential bias be detected? How should datasets be reported? What are the regulatory requirements in different countries? The recommendations are intended to help AI developers demonstrate the utility of their products and to help pathologists and regulatory agencies verify reported performance measures. Further research is needed to formulate criteria for sufficiently representative test datasets so that AI solutions can operate with less user intervention and better support diagnostic workflows in the future.

Assessment of scalability and performance of the record linkage tool E-PIX® in managing multi-million patients in research projects at a large university hospital in Germany.

BACKGROUND: The identity management is a central component in medical research. Patients are recruited from various sites, which requires an error tolerant record linkage method, to ensure that patients are registered only once. In large research projects or institutions, the identity management has to deal with several thousands or millions of patients. In environments with large numbers of patients the register process could lead to high runtimes caused by record linkage. The Central Biomaterial Bank of the Charité (ZeBanC) searched for an identity management solution, which can handle millions of patients in large research projects with an acceptable performance. The goal of this paper was to simulate the registration of several million patients using the E-PIX service at Charité - Universitätsmedizin Berlin. The E-PIX service was evaluated in terms of needed runtimes, memory requirements, and processor utilization. A total of at least 20 million patients had to be registered. The runtimes to register patients into databases with various sizes should be examined, and the maximum number of patients, which the E-PIX service could handle, should be determined. METHODS: Tools were set up or developed to measure the needed runtimes, the memory used and the processor usage to register patients into various sizes of databases. To generate runtimes close to reality, modified patient data based on transposed real patient data were used for the simulation. The transposed patient data were sent to E-PIX to measure the runtimes of the registration process. This measurement was repeated for various database sizes. RESULTS: E-PIX is suitable to manage multi-million patients within a dataset. With the given hardware, it was possible to register a total of more than 30 million patients. It was possible to register more than 16 thousand patients per day into this database. CONCLUSIONS: The E-PIX tool fulfills the requirements of the Charité to be used for large research projects. The use of E-PIX is intended for the research context in the Charité.

Dermatomyositis With or Without Anti-Melanoma Differentiation-Associated Gene 5 Antibodies: Common Interferon Signature but Distinct NOS2 Expression.

The anti-melanoma differentiation-associated gene 5 (MDA5) autoantibody is specifically associated with dermatomyositis (DM). Nevertheless, anti-MDA5(+)-patients experience characteristic symptoms distinct from classic DM, including severe signs of extramuscular involvement; however, the clinical signs of myopathy are mild or even absent. The morphological and immunological features are not yet described in adulthood. Data concerning the pathophysiology of anti-MDA5 DM are sparse; however, the importance of the interferon (IFN) type I pathway involved in DM has been shown. Our aim was to define morphological alterations of the skeletal muscle and the intrinsic immune response of anti-MDA5-positive DM patients. Immunohistological and RT-PCR analysis of muscle biopsy specimens from anti-MDA5 and classic DM were compared. Those with anti-MDA5 DM did not present the classic features of perifascicular fiber atrophy and major histocompatibility complex class I expression. They did not show significant signs of capillary loss; tubuloreticular formations were observed less frequently. Inflammation was focal, clustering around single vessels but significantly less intense. Expression of IFN-stimulated genes was up-regulated in anti-MDA5 DM; however, the IFN score was significantly lower. Characteristic features were observed in anti-MDA5 DM and not in classic DM patients. Only anti-MDA5 DM showed numerous nitric oxide synthase 2-positive muscle fibers with sarcoplasmic colocalization of markers of regeneration and cell stress. Anti-MDA5-positive patients demonstrate a morphological pattern distinct from classic DM.

Quality Assessment for Secondary Use of Imaging Trials.

Secondary use of health data has become an emerging topic in medical informatics. Many initiatives focus on clinical routine data, but clinical trial data has complementary strengths regarding highly structured documentation and mandatory data quality (DQ) reviews during the implementation. Clinical imaging trials investigate new imaging methods and procedures. Recently, DQ frameworks for structured data were proposed for harmonized quality assessments (QA). In this article, we investigate the application of these concepts to imaging trials and how a DQ framework could be defined for secondary use scenarios. We conclude that image quality can be assessed through both pixel data and metadata, and the latter can mostly be handled like structured study documentation in QA. For pixel data, typical quality indicators can be mapped to existing frameworks, but require additional image processing. Specific attention needs to be drawn to complete de-identification of imaging data, both on pixel data and metadata level.

Evolving educational landscape in pathology: a comprehensive bibliometric and visual analysis including digital teaching and learning resources.

AIMS: Pathology education is a core component of medical training, and its literature is critical for refining educational modalities. We performed a cross-sectional bibliometric analysis to explore publications on pathology education, focusing on new medical education technologies. METHODS: The analysis identified 64 pathology journals and 53 keywords. Relevant articles were collected using a web application, PaperScraper, developed to accelerate literature search. Citation data were collected from multiple sources. Descriptive statistics, with time period analysis, were performed using Microsoft Excel and visualised with Flourish Studio. Two article groups were further investigated with a bibliometric software, VOSViewer, to establish co-authorship and keyword relationships. RESULTS: 8946 citations were retrieved from 905 selected articles. Most articles were published in the last decade (447, 49.4%). The top journals were Archives of Pathology & Laboratory Medicine (184), Human Pathology (122) and the American Journal of Clinical Pathology (117). The highest number of citations was found for Human Pathology (2120), followed by Archives of Pathology & Laboratory Medicine (2098) and American Journal of Clinical Pathology (1142). Authors with different backgrounds had the greatest number of articles and citations. 12 co-authorship, 3 keyword and 8 co-citation clusters were found for the social media/online resources group, 8 co-authorship, 4 keyword and 7 co-citation clusters for the digital pathology/virtual microscopy/mobile technologies group. CONCLUSIONS: The analysis revealed a significant increase in publications over time. The emergence of digital teaching and learning resources played a major role in this growth. Overall, these findings underscore the transformative potential of technology in pathology education.

Joining forces for pathology diagnostics with AI assistance: The EMPAIA initiative.

Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numerous challenges, including technical and regulatory hurdles in translating research results into clinical diagnostic products and the lack of standardized interfaces. The open and vendor-neutral EMPAIA initiative addresses these challenges. Here, we provide an overview of EMPAIA's achievements and lessons learned. EMPAIA integrates various stakeholders of the pathology AI ecosystem, i.e., pathologists, computer scientists, and industry. In close collaboration, we developed technical interoperability standards, recommendations for AI testing and product development, and explainability methods. We implemented the modular and open-source EMPAIA Platform and successfully integrated 14 AI-based image analysis apps from eight different vendors, demonstrating how different apps can use a single standardized interface. We prioritized requirements and evaluated the use of AI in real clinical settings with 14 different pathology laboratories in Europe and Asia. In addition to technical developments, we created a forum for all stakeholders to share information and experiences on digital pathology and AI. Commercial, clinical, and academic stakeholders can now adopt EMPAIA's common open-source interfaces, providing a unique opportunity for large-scale standardization and streamlining of processes. Further efforts are needed to effectively and broadly establish AI assistance in routine laboratory use. To this end, a sustainable infrastructure, the non-profit association EMPAIA International, has been established to continue standardization and support broad implementation and advocacy for an AI-assisted digital pathology future.

Anonymization of whole slide images in histopathology for research and education.

Objective: The exchange of health-related data is subject to regional laws and regulations, such as the General Data Protection Regulation (GDPR) in the EU or the Health Insurance Portability and Accountability Act (HIPAA) in the United States, resulting in non-trivial challenges for researchers and educators when working with these data. In pathology, the digitization of diagnostic tissue samples inevitably generates identifying data that can consist of sensitive but also acquisition-related information stored in vendor-specific file formats. Distribution and off-clinical use of these Whole Slide Images (WSIs) are usually done in these formats, as an industry-wide standardization such as DICOM is yet only tentatively adopted and slide scanner vendors currently do not provide anonymization functionality. Methods: We developed a guideline for the proper handling of histopathological image data particularly for research and education with regard to the GDPR. In this context, we evaluated existing anonymization methods and examined proprietary format specifications to identify all sensitive information for the most common WSI formats. This work results in a software library that enables GDPR-compliant anonymization of WSIs while preserving the native formats. Results: Based on the analysis of proprietary formats, all occurrences of sensitive information were identified for file formats frequently used in clinical routine, and finally, an open-source programming library with an executable CLI tool and wrappers for different programming languages was developed. Conclusions: Our analysis showed that there is no straightforward software solution to anonymize WSIs in a GDPR-compliant way while maintaining the data format. We closed this gap with our extensible open-source library that works instantaneously and offline.

Explainability and causability in digital pathology.

The current move towards digital pathology enables pathologists to use artificial intelligence (AI)-based computer programmes for the advanced analysis of whole slide images. However, currently, the best-performing AI algorithms for image analysis are deemed black boxes since it remains - even to their developers - often unclear why the algorithm delivered a particular result. Especially in medicine, a better understanding of algorithmic decisions is essential to avoid mistakes and adverse effects on patients. This review article aims to provide medical experts with insights on the issue of explainability in digital pathology. A short introduction to the relevant underlying core concepts of machine learning shall nurture the reader's understanding of why explainability is a specific issue in this field. Addressing this issue of explainability, the rapidly evolving research field of explainable AI (XAI) has developed many techniques and methods to make black-box machine-learning systems more transparent. These XAI methods are a first step towards making black-box AI systems understandable by humans. However, we argue that an explanation interface must complement these explainable models to make their results useful to human stakeholders and achieve a high level of causability, i.e. a high level of causal understanding by the user. This is especially relevant in the medical field since explainability and causability play a crucial role also for compliance with regulatory requirements. We conclude by promoting the need for novel user interfaces for AI applications in pathology, which enable contextual understanding and allow the medical expert to ask interactive 'what-if'-questions. In pathology, such user interfaces will not only be important to achieve a high level of causability. They will also be crucial for keeping the human-in-the-loop and bringing medical experts' experience and conceptual knowledge to AI processes.

Telestroke ambulances in prehospital stroke management: concept and pilot feasibility study.

BACKGROUND AND PURPOSE: Pre- and intrahospital time delays are major concerns in acute stroke care. Telemedicine-equipped ambulances may improve time management and identify patients with stroke eligible for thrombolysis by an early prehospital stroke diagnosis. The aims of this study were (1) to develop a telestroke ambulance prototype; (2) to test the reliability of stroke severity assessment; and (3) to evaluate its feasibility in the prehospital emergency setting. METHODS: Mobil, real-time audio-video streaming telemedicine devices were implemented into advanced life support ambulances. Feasibility of telestroke ambulances and reliability of the National Institutes of Health Stroke Scale assessment were tested using current wireless cellular communication technology (third generation) in a prehospital stroke scenario. Two stroke actors were trained in simulation of differing right and left middle cerebral artery stroke syndromes. National Institutes of Health Stroke Scale assessment was performed by a hospital-based stroke physician by telemedicine, by an emergency physician guided by telemedicine, and "a posteriori" on the basis of video documentation. RESULTS: In 18 of 30 scenarios, National Institutes of Health Stroke Scale assessment could not be performed due to absence or loss of audio-video signal. In the remaining 12 completed scenarios, interrater agreement of National Institutes of Health Stroke Scale examination between ambulance and hospital and ambulance and "a posteriori" video evaluation was moderate to good with weighted κ values of 0.69 (95% CI, 0.51-0.87) and 0.79 (95% CI, 0.59-0.98), respectively. CONCLUSION: Prehospital telestroke examination was not at an acceptable level for clinical use, at least on the basis of the used technology. Further technical development is needed before telestroke is applicable for prehospital stroke management during patient transport.

EMPAIA App Interface: An open and vendor-neutral interface for AI applications in pathology.

BACKGROUND AND OBJECTIVE: Artificial intelligence (AI) apps hold great potential to make pathological diagnoses more accurate and time efficient. Widespread use of AI in pathology is hampered by interface incompatibilities between pathology software. We studied the existing interfaces in order to develop the EMPAIA App Interface, an open standard for the integration of pathology AI apps. METHODS: The EMPAIA App Interface relies on widely-used web communication protocols and containerization. It consists of three parts: A standardized format to describe the semantics of an app, a mechanism to deploy and execute apps in computing environments, and a web API through which apps can exchange data with a host application. RESULTS: Five commercial AI app manufacturers successfully adapted their products to the EMPAIA App Interface and helped improve it with their feedback. Open source tools facilitate the adoption of the interface by providing reusable data access and scheduling functionality and enabling automatic validation of app compliance. CONCLUSIONS: Existing AI apps and pathology software can be adapted to the EMPAIA App Interface with little effort. It is a viable alternative to the proprietary interfaces of current software. If enough vendors join in, the EMPAIA App Interface can help to advance the use of AI in pathology.

Europe Unites for the Digital Transformation of Pathology: The Role of the New ESDIP.

The European Society for Digital and Integrative Pathology (ESDIP) was formally founded in 2016 in Berlin. After a well-participated annual general meeting, ESDIP members elected a new active structure for the next term of office. The priority goals of this new and highly motivated team will be to support the digital transformation in the pathology laboratories, to build inter-institutional bridges for cooperation, to establish a solid educational program, and to increase the collaboration with industry partners.

Artificial Intelligence in Pathology: From Prototype to Product.

Modern image analysis techniques based on artificial intelligence (AI) have great potential to improve the quality and efficiency of diagnostic procedures in pathology and to detect novel biomarkers. Despite thousands of published research papers on applications of AI in pathology, hardly any research implementations have matured into commercial products for routine use. Bringing an AI solution for pathology to market poses significant technological, business, and regulatory challenges. In this paper, we provide a comprehensive overview and advice on how to meet these challenges. We outline how research prototypes can be turned into a product-ready state and integrated into the IT infrastructure of clinical laboratories. We also discuss business models for profitable AI solutions and reimbursement options for computer assistance in pathology. Moreover, we explain how to obtain regulatory approval so that AI solutions can be launched as in vitro diagnostic medical devices. Thus, this paper offers computer scientists, software companies, and pathologists a road map for transforming prototypes of AI solutions into commercial products.

Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP).

The interest in implementing digital pathology (DP) workflows to obtain whole slide image (WSI) files for diagnostic purposes has increased in the last few years. The increasing performance of technical components and the Food and Drug Administration (FDA) approval of systems for primary diagnosis led to increased interest in applying DP workflows. However, despite this revolutionary transition, real world data suggest that a fully digital approach to the histological workflow has been implemented in only a minority of pathology laboratories. The objective of this study is to facilitate the implementation of DP workflows in pathology laboratories, helping those involved in this process of transformation to identify: (a) the scope and the boundaries of the DP transformation; (b) how to introduce automation to reduce errors; (c) how to introduce appropriate quality control to guarantee the safety of the process and (d) the hardware and software needed to implement DP systems inside the pathology laboratory. The European Society of Digital and Integrative Pathology (ESDIP) provided consensus-based recommendations developed through discussion among members of the Scientific Committee. The recommendations are thus based on the expertise of the panel members and on the agreement obtained after virtual meetings. Prior to publication, the recommendations were reviewed by members of the ESDIP Board. The recommendations comprehensively cover every step of the implementation of the digital workflow in the anatomic pathology department, emphasizing the importance of interoperability, automation and tracking of the entire process before the introduction of a scanning facility. Compared to the available national and international guidelines, the present document represents a practical, handy reference for the correct implementation of the digital workflow in Europe.

Necrosis in anti-SRP+ and anti-HMGCR+myopathies: Role of autoantibodies and complement.

OBJECTIVE: To characterize muscle fiber necrosis in immune-mediated necrotizing myopathies (IMNM) with anti-signal recognition particle (SRP) or anti-3-hydroxy-3-methylglutarylcoenzyme A reductase (HMGCR) antibodies and to explore its underlying molecular immune mechanisms. METHODS: Muscle biopsies from patients with IMNM were analyzed and compared to biopsies from control patients with myositis. In addition to immunostaining and reverse transcription PCR on muscle samples, in vitro immunostaining on primary muscle cells was performed. RESULTS: Creatine kinase levels and muscle regeneration correlated with the proportion of necrotic fibers (r = 0.6, p < 0.001). CD68+iNOS+ macrophages and a Th-1 immune environment were chiefly involved in ongoing myophagocytosis of necrotic fibers. T-cell densities correlated with necrosis but no signs of cytotoxicity were detected. Activation of the classical pathway of the complement cascade, accompanied by deposition of sarcolemmal immunoglobulins, featured involvement of humoral immunity. Presence of SRP and HMGCR proteins on altered myofibers was reproduced on myotubes exposed to purified patient-derived autoantibodies. Finally, a correlation between sarcolemmal complement deposits and fiber necrosis was observed (r = 0.4 and p = 0.004). Based on these observations, we propose to update the pathologic criteria of IMNM. CONCLUSION: These data further corroborate the pathogenic role of anti-SRP and anti-HMGCR autoantibodies in IMNM, highlighting humoral mechanisms as key players in immunity and myofiber necrosis.

Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.

Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection.