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Background: Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient. Manually sorting and labelling whole‐slide images (WSIs) is a very time‐consuming process, hindering the direct application of AI on the collected tissue samples from large cohorts. In this study we addressed this issue by developing a deep‐learning (DL)‐based method for automatic curation of large pathology datasets with several slides per patient. Methods: We collected multiple large multicentric datasets of colorectal cancer histopathological slides from the United Kingdom (FOXTROT, N = 21,384 slides; CR07, N = 7985 slides) and Germany (DACHS, N = 3606 slides). These datasets contained multiple types of tissue slides, including bowel resection specimens, endoscopic biopsies, lymph node resections, immunohistochemistry‐stained slides, and tissue microarrays. We developed, trained, and tested a deep convolutional neural network model to predict the type of slide from the slide overview (thumbnail) image. The primary statistical endpoint was the macro‐averaged area under the receiver operating curve (AUROCs) for detection of the type of slide. Results: In the primary dataset (FOXTROT), with an AUROC of 0.995 [95% confidence interval [CI]: 0.994–0.996] the algorithm achieved a high classification performance and was able to accurately predict the type of slide from the thumbnail image alone. In the two external test cohorts (CR07, DACHS) AUROCs of 0.982 [95% CI: 0.979–0.985] and 0.875 [95% CI: 0.864–0.887] were observed, which indicates the generalizability of the trained model on unseen datasets. With a confidence threshold of 0.95, the model reached an accuracy of 94.6% (7331 classified cases) in CR07 and 85.1% (2752 classified cases) for the DACHS cohort. Conclusion: Our findings show that using the low‐resolution thumbnail image is sufficient to accurately classify the type of slide in digital pathology. This can support researchers to make the vast resource of existing pathology archives accessible to modern AI models with only minimal manual annotations. [ABSTRACT FROM AUTHOR]

Introduction: After the outbreak of COVID-19 unprecedented changes in the healthcare systems worldwide were necessary resulting in a reduction of urological capacities with postponements of consultations and surgeries., Material and Methods: An email was sent to 66 urological hospitals with focus on robotic surgery (RS) including a link to a questionnaire (e.g. bed/staff capacity, surgical caseload, protection measures during RS) that covered three time points: a representative baseline week prior to COVID-19, the week of March 16th-22nd and April 20th-26th 2020. The results were evaluated using descriptive analyses., Results: 27 out of 66 questionnaires were analyzed (response rate: 41%). We found a decrease of 11% in hospital beds and 25% in OR capacity with equal reductions for endourological, open and robotic procedures. Primary surgical treatment of urolithiasis and benign prostate syndrome (BPS) but also of testicular and penile cancer dropped by at least 50% while the decrease of surgeries for prostate, renal and urothelial cancer (TUR-B and cystectomies) ranged from 15 to 37%. The use of personal protection equipment (PPE), screening of staff and patients and protection during RS was unevenly distributed in the different centers-however, the number of COVID-19 patients and urologists did not reach double digits., Conclusion: The German urological landscape has changed since the outbreak of COVID-19 with a significant shift of high priority surgeries but also continuation of elective surgical treatments. While screening and staff protection is employed heterogeneously, the number of infected German urologists stays low., Competing Interests: The authors have declared that no competing interests exist.