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Ito Y, Unagami M, Yamabe F, Mitsui Y, Nakajima K, Nagao K, Kobayashi H. A method for utilizing automated machine learning for histopathological classification of testis based on Johnsen scores. Sci Rep 2021; 11:9962. [PMID: 33967273 PMCID: PMC8107178 DOI: 10.1038/s41598-021-89369-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 04/26/2021] [Indexed: 12/17/2022] Open
Abstract
We examined whether a tool for determining Johnsen scores automatically using artificial intelligence (AI) could be used in place of traditional Johnsen scoring to support pathologists’ evaluations. Average precision, precision, and recall were assessed by the Google Cloud AutoML Vision platform. We obtained testicular tissues for 275 patients and were able to use haematoxylin and eosin (H&E)-stained glass microscope slides from 264 patients. In addition, we cut out of parts of the histopathology images (5.0 × 5.0 cm) for expansion of Johnsen’s characteristic areas with seminiferous tubules. We defined four labels: Johnsen score 1–3, 4–5, 6–7, and 8–10 to distinguish Johnsen scores in clinical practice. All images were uploaded to the Google Cloud AutoML Vision platform. We obtained a dataset of 7155 images at magnification 400× and a dataset of 9822 expansion images for the 5.0 × 5.0 cm cutouts. For the 400× magnification image dataset, the average precision (positive predictive value) of the algorithm was 82.6%, precision was 80.31%, and recall was 60.96%. For the expansion image dataset (5.0 × 5.0 cm), the average precision was 99.5%, precision was 96.29%, and recall was 96.23%. This is the first report of an AI-based algorithm for predicting Johnsen scores.
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Affiliation(s)
- Yurika Ito
- Department of Urology, Toho University School of Medicine, 6-11-1, Omori-Nishi, Ota-ku, Tokyo, 143-8541, Japan
| | - Mami Unagami
- Department of Urology, Toho University School of Medicine, 6-11-1, Omori-Nishi, Ota-ku, Tokyo, 143-8541, Japan
| | - Fumito Yamabe
- Department of Urology, Toho University School of Medicine, 6-11-1, Omori-Nishi, Ota-ku, Tokyo, 143-8541, Japan
| | - Yozo Mitsui
- Department of Urology, Toho University School of Medicine, 6-11-1, Omori-Nishi, Ota-ku, Tokyo, 143-8541, Japan
| | - Koichi Nakajima
- Department of Urology, Toho University School of Medicine, 6-11-1, Omori-Nishi, Ota-ku, Tokyo, 143-8541, Japan
| | - Koichi Nagao
- Department of Urology, Toho University School of Medicine, 6-11-1, Omori-Nishi, Ota-ku, Tokyo, 143-8541, Japan
| | - Hideyuki Kobayashi
- Department of Urology, Toho University School of Medicine, 6-11-1, Omori-Nishi, Ota-ku, Tokyo, 143-8541, Japan.
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Naz M, Kamal M. Classification, causes, diagnosis and treatment of male infertility: a review. ACTA ACUST UNITED AC 2017. [DOI: 10.1007/s13596-017-0269-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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