ArticleVeterinary pathology2022
Computer-assisted mitotic count using a deep learning-based algorithm improves interobserver reproducibility and accuracy.
Article in Veterinary pathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 60 citations in OpenAlex.
- Mitotic activity: A systematic literature review of the assessment methodology and prognostic value in canine tumors.Veterinary pathology · 2024Pooled it
- Mitotic activity: A systematic literature review of the assessment methodology and prognostic value in feline tumors.Veterinary pathology · 2024Pooled it
- Comparative Analysis of Conventional and Digital Microscopy for Counting Mitotic Figures in Cutaneous Neoplasms of Dogs and Cats.Animals : an open access journal from MDPI · 2026Article
- Artificial intelligence predictsVeterinary pathology · 2026Article
- Adaptive example selection for prototype based explainable mitosis detection in digital pathology.Scientific reports · 2026Article
- The influence, promise, and potential perils of artificial intelligence in veterinary medicine: a call for improved awareness and literacy.Journal of veterinary internal medicine · 2026Article
- Artificial intelligence in mitotic checkpoint modeling: transforming our understanding of cellular division through machine learning and predictive biology.Briefings in bioinformatics · 2026Review
- The application of artificial intelligence in veterinary oncology: a scoping review.BMC veterinary research · 2025Article
- Aligning computational pathology with clinical practice for colorectal cancer.NPJ precision oncology · 2025Review
- Evaluating AI-Based Mitosis Detection for Breast Carcinoma in Digital Pathology: A Clinical Study on Routine Practice Integration.Diagnostics (Basel, Switzerland) · 2025Article
- Nuclear pleomorphism in canine cutaneous mast cell tumors: Comparison of reproducibility and prognostic relevance between estimates, manual morphometry, and algorithmic morphometry.Veterinary pathology · 2025Article
- Breast cancer survival prediction using an automated mitosis detection pipeline.The journal of pathology. Clinical research · 2024Article
- Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis.Scientific reports · 2024Article
- Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility.Veterinary sciences · 2024Article
- Enhancing mitosis quantification and detection in meningiomas with computational digital pathology.Acta neuropathologica communications · 2024Article
- Atypical Mitotic Figures Are Prognostically Meaningful for Canine Cutaneous Mast Cell Tumors.Veterinary sciences · 2023Article
- Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing.Scientific reports · 2023Article
- Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images.Veterinary pathology · 2023Article
- A comprehensive multi-domain dataset for mitotic figure detection.Scientific data · 2023Article
- Artificial Intelligence to Predict the BRAF V595E Mutation in Canine Urinary Bladder Urothelial Carcinomas.Animals : an open access journal from MDPI · 2023Article
Corrections and comments
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Authors and funding
33 authors at 14 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected hotspot MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study demonstrate that computer assistance may lead to more reproducible and accurate MCs in ccMCTs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.