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Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the forefront of artificial intelligence (AI) revolution, conventional pathology, which commonly relies on examination of tissue samples on a glass slide, is falling behind in leveraging this technology. On the other hand, ex vivo confocal laser scanning microscopy (ex vivo CLSM), owing to its digital workflow features, has a high potential to benefit from integrating AI tools into the assessment and decision-making process. Aim of this work was to explore a preliminary application of CNN in digitally stained ex vivo CLSM images of cutaneous squamous cell carcinoma (cSCC) for automated detection of tumor tissue. Thirty-four freshly excised tissue samples were prospectively collected and examined immediately after resection. After the histologically confirmed ex vivo CLSM diagnosis, the tumor tissue was annotated for segmentation by experts, in order to train the MobileNet CNN. The model was then trained and evaluated using cross validation. The overall sensitivity and specificity of the deep neural network for detecting cSCC and tumor free areas on ex vivo CLSM slides compared to expert evaluation were 0.76 and 0.91, respectively. The area under the ROC curve was equal to 0.90 and the area under the precision-recall curve was 0.85. The results demonstrate a high potential of deep learning models to detect cSCC regions on digitally stained ex vivo CLSM slides and to distinguish them from tumor-free skin.

Integration of digital pathology (DP) into clinical diagnostic workflows is increasingly receiving attention as new hardware and software become available. To facilitate the adoption of DP, the Swiss Digital Pathology Consortium (SDiPath) organized a Delphi process to produce a series of recommendations for DP integration within Swiss clinical environments. This process saw the creation of 4 working groups, focusing on the various components of a DP system (1) scanners, quality assurance and validation of scans, (2) integration of Whole Slide Image (WSI)-scanners and DP systems into the Pathology Laboratory Information System, (3) digital workflow—compliance with general quality guidelines, and (4) image analysis (IA)/artificial intelligence (AI), with topic experts for each recruited for discussion and statement generation. The work product of the Delphi process is 83 consensus statements presented here, forming the basis for “SDiPath Recommendations for Digital Pathology”. They represent an up-to-date resource for national and international hospitals, researchers, device manufacturers, algorithm developers, and all supporting fields, with the intent of providing expectations and best practices to help ensure safe and efficient DP usage.

Bringing digital teaching materials into residency training programs has seen slow adoption, expected for many new technologies. The COVID-19 pandemic dramatically shifted the paradigm for many resident teaching modalities as institutions instituted social distancing to prevent spread of the novel coronavirus. The impact of this shift on pathology trainee education has not been well studied. We conducted an online survey of pathology trainees, program directors, and faculty to assess pre- and post-COVID-19 use of, and response to, various digital pathology modalities. Responses were solicited through both social media and directed appeals. A total of 261 respondents (112 faculty, 52 program directors, and 97 trainees) reported a dramatic and significant increase in the use of digital pathology-related education tools. A significant majority of faculty and program directors agreed that this shift had adversely affected the quality (59% and 62%, respectively) and effectiveness (66%) of their teaching. This perception was similar among learners relative to the impact on quality (59%) and effectiveness (64%) of learning. Most respondents (70%-92%) anticipate that their use of digital pathology education tools will increase or remain the same post-COVID. The global COVID-19 pandemic created a unique opportunity and challenge for pathology training programs. Digital pathology resources were accordingly readily adopted to continue supporting educational activities. The learning curve and utilization of this technology was perceived to impair the quality and effectiveness of teaching and learning. Since the use of digital tools appears poised to continue to grow post-COVID19, challenges due to impaired quality and effectiveness will need to be addressed.

Background: Despite increasing interest in whole-slide imaging (WSI) over optical microscopy (OM), limited information on comparative assessment of various digital pathology systems (DPSs) is available. Materials and Methods: A comprehensive evaluation was undertaken to investigate the technical performance–assessment and diagnostic accuracy of four DPSs with an objective to establish the noninferiority of WSI over OM and find out the best possible DPS for clinical workflow. Results: A total of 2376 digital images, 15,775 image reads (OM - 3171 + WSI - 12,404), and 6100 diagnostic reads (OM - 1245, WSI - 4855) were generated across four DPSs (coded as DPS: 1, 2, 3, and 4) using a total 240 cases (604 slides). Onsite technical evaluation revealed successful scan rate: DPS3 < DPS2 < DPS4 < DPS1; mean scanning time: DPS4 < DPS1 < DPS2 < DPS3; and average storage space: DPS3 < DPS2 < DPS1 < DPS4. Overall diagnostic accuracy, when compared with the reference standard for OM and WSI, was 95.44% (including 2.48% minor and 2.08% major discordances) and 93.32% (including 4.28% minor and 2.4% major discordances), respectively. The difference between the clinically significant discordances by WSI versus OM was 0.32%. Major discordances were observed mostly using DPS4 and least in DPS1; however, the difference was statistically insignificant. Almost perfect (Κ ≥ 0.8)/substantial (Κ = 0.6–0.8) inter/intra-observer agreement between WSI and OM was observed for all specimen types, except cytology. Overall image quality was best for DPS1 followed by DPS4. Mean digital artifact rate was 6.8% (163/2376 digital images) and maximum artifacts were noted in DPS2 (n = 77) followed by DPS3 (n = 36). Most pathologists preferred viewing software of DPS1 and DPS2. Conclusion: WSI was noninferior to OM for all specimen types, except for cytology. Each DPS has its own pros and cons; however, DPS1 closely emulated the real-world clinical environment. This evaluation is intended to provide a roadmap to pathologists for the selection of the appropriate DPSs while adopting WSI.

The regulatory field for digital pathology (DP) has advanced significantly. A major milestone was accomplished when the FDA allowed the first vendor to market their device for primary diagnostic use in the USA and published in the classification order that this device, and substantially equivalent devices of this generic type, should be classified into class II instead of class III as previously proposed. The Digital Pathology Association (DPA) regulatory task force had a major role in the accomplishment of getting the application request for Whole Slide Imaging (WSI) Systems recommended for a de novo. This article reviews the past and emerging regulatory environment of WSI for clinical use in the USA. A WSI system with integrated subsystems is defined in the context ofmedical device regulations. The FDA technical performance assessment guideline is also discussed as well as parameters involved in analytical testing and clinical studies to demonstrate that WSI devices are safe and effective for clinical use.

Background Breast cancer subtype can be classified using standard clinical markers (estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2)), supplemented with additional markers. However, automated biomarker scoring and classification schemes have not been standardized. The aim of this study was to optimize tumor classification using automated methods in order to describe subtype frequency in the African American Breast Cancer Epidemiology and Risk (AMBER) consortium. Methods Using immunohistochemistry (IHC), we quantified the expression of ER, PR, HER2, the proliferation marker Ki67, and two basal-like biomarkers, epidermal growth factor receptor (EGFR) and cytokeratin (CK)5/6, in 1381 invasive breast tumors from African American women. RNA-based (prediction analysis of microarray 50 (PAM50)) subtype, available for 574 (42%) cases, was used to optimize classification. Subtype frequency was calculated, and associations between subtype and tumor characteristics were estimated using logistic regression. Results Relative to ER, PR and HER2 from medical records, central IHC staining and the addition of Ki67 or combined tumor grade improved accuracy for classifying PAM50-based luminal subtypes. Few triple negative cases (< 2%) lacked EGFR and CK5/6 expression, thereby providing little improvement in accuracy for identifying basal-like tumors. Relative to luminal A subtype, all other subtypes had higher combined grade and were larger, and ER-/HER2+ tumors were more often lymph node positive and late stage tumors. The frequency of basal-like tumors was 31%, exceeded only slightly by luminal A tumors (37%). Conclusions Our findings indicate that automated IHC-based classification produces tumor subtype frequencies approximating those from PAM50-based classification and highlight high frequency of basal-like and low frequency of luminal A breast cancer in a large study of African American women.

Background/Objectives: Digital pathology has the potential to revolutionize pathology diagnostics, especially in geo-graphically isolated and underserved regions. By leveraging technology, telepathology, and integration with computer-aided diagnostic tools, digital pathology can improve access to prompt and accurate diagnostics. Methods: Our key steps to implementing digital pathology and transitioning operations to a digital network are assessing existing infrastructure, identifying gaps in connectivity and resources, and creating a workflow tailored to the needs of the healthcare system. Results: We present an approach of implementing digital pathology in Timmins, Northern Ontario, Canada, focusing on addressing regional disparities and the improvements that come alongside utilizing digital pathology. Our results show that digital pathology can provide prompt, efficient and better-quality diagnostic services to rural and un-deserved areas, improving patient care and outcomes. It also represents a cost-effective option with savings from eliminating travel costs, courier costs and additional operational efficiencies. Conclusions: Implementing digital pathology in rural settings presented with challenges related to infrastructure, technical abilities, workforce readiness, cost and other aspects involved in transitioning from traditional microscopy to a fully digital pathway. Digital pathology systems can help ensuring seamless data flow and improving overall healthcare delivery. Telepathology also allows pathologists to provide diagnostic services from a distance, which is particularly beneficial in areas with a shortage of pathologists.

Background Digital pathology (DP) offers significant advantages in diagnostic efficiency and reproducibility. However, its implementation in low-resource settings remains challenging due to cost, infrastructure limitations, and workflow constraints. Objective To describe the implementation and validation of a digital pathology workflow in a high-volume laboratory in Northeastern Brazil, highlighting strategies for deployment in a low-cost environment. Methods A midrange scanner (MoticEasyScan®) was integrated with the laboratory information system (apLIS®) to support whole slide imaging (WSI) for hematoxylin and eosin (H&E) stained and ancillary slides. The workflow was redesigned to include technical infrastructure upgrades and staff training. Validation followed CAP guidelines and included 384 slides from 64 cases, evaluated by two pathologists using both digital and physical formats. Results Concordance between digital and traditional diagnoses reached 98.72%, with near-perfect interobserver agreement (Kappa = 0.928 and 0.958; p  < 0.05). Challenges included limited scanner throughput, storage demands (~ 12 TB/quarter), and variable monitor quality. Despite these constraints, the laboratory successfully digitized 60% of its routine workload, facilitating case review, image sharing, and research expansion. Conclusion This study demonstrates the feasibility of implementing digital pathology in resource-limited settings using cost-effective solutions and workflow optimization. The validated process offers a scalable model for similar laboratories, with potential to integrate artificial intelligence tools in future diagnostic applications.

Background Artificial Intelligence (AI) is rapidly evolving, presenting both beneficial and challenging implications for society. The critical choice lies in how humanity chooses to harness this technology, particularly in the realm of healthcare diagnostics. This field stands out as a promising area where AI can provide significant assistance, with the potential to transform the diagnostic process into one that is fast, reliable, affordable, repeatable, and accurate. By integrating AI into diagnostic workflows, we can foster evidence-based science in a more efficient manner. All facets of pathological diagnostics can benefit from AI collaboration, which could lead to a transformative future for the industry. Main body This review aims to examine the current advancements of AI in diagnostic applications while offering perspectives on future developments. It covers the fundamental workflows of AI models, highlighting the advantages of unsupervised foundation models in various medical contexts. The discussion explores their utility across disciplines such as histopathology, cytopathology, and hematology, emphasizing their potential to enhance diagnostic accuracy. Additionally, the review addresses existing limitations, challenges faced in implementation, and underscores the ongoing vital role of pathologists in integrating AI into clinical practice. Conclusion The widespread accessibility of data and advanced software tools has significantly propelled and expedited progress in AI research. While the Food and Drug Administration has established regulations to safeguard private information, many researchers persist in developing and training AI models that demonstrate high accuracy. Despite these advancements, challenges remain in deploying fully autonomous AI systems for individual diagnostics. Notably, recent developments in foundation models have shown remarkable potential, surpassing traditional supervised models in diagnosing multiple cancer types, indicating a promising trajectory toward more comprehensive and reliable AI-driven diagnostic solutions in the near future.

Artificial intelligence (AI) is transforming liver pathology by enhancing diagnostic accuracy, standardizing assessments, and supporting personalized care. This review explores current applications of AI across neoplastic and non-neoplastic liver diseases, transplant pathology, and histopathological reporting. Deep learning models have demonstrated strong performance in classifying hepatocellular carcinoma, cholangiocarcinoma, and liver metastases, as well as subtyping hepatocellular adenomas. In chronic liver diseases, AI enables continuous quantification of fibrosis and inflammation, improving reproducibility. In transplantation, algorithms assist in predicting rejection and graft viability. The pathologist plays a central role in AI tool development, validation, and clinical integration. Despite promising advances, key challenges such as data standardization, explainability, and regulatory oversight persist. Rather than replacing human expertise, AI may complement the pathologist’s role in delivering high-quality, efficient, and precise liver disease diagnosis and management.