Eiichiro Uchino1, Kanata Suzuki2, Noriaki Sato3, Ryosuke Kojima4, Yoshinori Tamada5, Shusuke Hiragi6, Hideki Yokoi7, Nobuhiro Yugami2, Sachiko Minamiguchi8, Hironori Haga8, Motoko Yanagita9, Yasushi Okuno10. 1. Department of Medical Intelligent Systems, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan. 2. Fujitsu Laboratories LTD., Kawasaki, Japan. 3. Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan. 4. Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan. 5. Department of Medical Intelligent Systems, Graduate School of Medicine, Kyoto University, Kyoto, Japan. 6. Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Division of Medical Informatics and Administration Planning, Kyoto University Hospital, Kyoto, Japan. 7. Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan. 8. Department of Diagnostic Pathology, Graduate School of Medicine, Kyoto University, Kyoto, Japan. 9. Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, Japan. Electronic address: motoy@kuhp.kyoto-u.ac.jp. 10. Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan; RIKEN, The Drug Development Data Intelligence Platform Group, Yokohama, Japan. Electronic address: okuno.yasushi.4c@kyoto-u.ac.jp.
Abstract
BACKGROUND: 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. METHODS: 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 InceptionV3 convolutional neural network. Subsequently, we compared the agreement to truth labels between majority decision among nephrologists with or without the AI model as a voter. RESULTS: Our model for global sclerosis showed high performance (area under the curve: periodic acid-Schiff, 0.986; periodic acid methenamine silver, 0.983); the models for the other findings also showed performance close to those of nephrologists. By adding the AI model output to majority decision among nephrologists, out of the 14 constructed models, the results of the majority decision showed improvement in sensitivity for 10 models (four of them were statistically significant) and specificity for eight models (five significant). CONCLUSION: Our study showed a proof-of-concept for the classification of multiple glomerular findings in a comprehensive method of deep learning and suggested its potential effectiveness in improving diagnostic accuracy of clinicians.
BACKGROUND: 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. METHODS: 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 InceptionV3 convolutional neural network. Subsequently, we compared the agreement to truth labels between majority decision among nephrologists with or without the AI model as a voter. RESULTS: Our model for global sclerosis showed high performance (area under the curve: periodic acid-Schiff, 0.986; periodic acidmethenamine silver, 0.983); the models for the other findings also showed performance close to those of nephrologists. By adding the AI model output to majority decision among nephrologists, out of the 14 constructed models, the results of the majority decision showed improvement in sensitivity for 10 models (four of them were statistically significant) and specificity for eight models (five significant). CONCLUSION: Our study showed a proof-of-concept for the classification of multiple glomerular findings in a comprehensive method of deep learning and suggested its potential effectiveness in improving diagnostic accuracy of clinicians.
Authors: Elise Marechal; Adrien Jaugey; Georges Tarris; Michel Paindavoine; Jean Seibel; Laurent Martin; Mathilde Funes de la Vega; Thomas Crepin; Didier Ducloux; Gilbert Zanetta; Sophie Felix; Pierre Henri Bonnot; Florian Bardet; Luc Cormier; Jean-Michel Rebibou; Mathieu Legendre Journal: Clin J Am Soc Nephrol Date: 2021-12-03 Impact factor: 8.237
Authors: Yi Zheng; Clarissa A Cassol; Saemi Jung; Divya Veerapaneni; Vipul C Chitalia; Kevin Y M Ren; Shubha S Bellur; Peter Boor; Laura M Barisoni; Sushrut S Waikar; Margrit Betke; Vijaya B Kolachalama Journal: Am J Pathol Date: 2021-05-23 Impact factor: 5.770