Classification of glomerular pathological findings using deep learning and nephrologist-AI collective intelligence approach

International Journal of Medical Informatics
E. UchinoYasushi Okuno

Abstract

Automated classification of glomerular pathological findings is potentially beneficial in establishing an efficient and objective diagnosis in renal pathology. While previous studies have verified the artificial intelligence (AI) models for the classification of global sclerosis and glomerular cell proliferation, there are several other glomerular pathological findings required for diagnosis, and the comprehensive models for the classification of these major findings have not yet been reported. Whether the cooperation between these AI models and clinicians improves diagnostic performance also remains unknown. Here, we developed AI models to classify glomerular images for major findings required for pathological diagnosis and investigated whether those models could improve the diagnostic performance of nephrologists. We used a dataset of 283 kidney biopsy cases comprising 15,888 glomerular images that were annotated by a total of 25 nephrologists. AI models to classify seven pathological findings: global sclerosis, segmental sclerosis, endocapillary proliferation, mesangial matrix accumulation, mesangial cell proliferation, crescent, and basement membrane structural changes, were constructed using deep learning by fine-tuning of...Continue Reading

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Citations

Sep 17, 2020·International Journal of Molecular Sciences·Georg SteinbussMark Kriegsmann
Feb 16, 2021·Health Information Science and Systems·Federico CabitzaLuca Maria Sconfienza
May 26, 2021·The American Journal of Pathology·Yi ZhengVijaya B Kolachalama

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