Automated recognition of glomerular lesions in the kidneys of mice by using deep learning
Background: In recent years, digital pathology has been rapidly developing and applied throughout the world. Especially in clinical settings, it has been utilized in a variety of situations, including automated cancer diagnosis. Conversely, in non-clinical research, it has not yet been utilized as m...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Journal of Pathology Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353922007234 |
| _version_ | 1797976969884729344 |
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| author | Airi Akatsuka Yasushi Horai Airi Akatsuka |
| author_facet | Airi Akatsuka Yasushi Horai Airi Akatsuka |
| author_sort | Airi Akatsuka |
| collection | DOAJ |
| description | Background: In recent years, digital pathology has been rapidly developing and applied throughout the world. Especially in clinical settings, it has been utilized in a variety of situations, including automated cancer diagnosis. Conversely, in non-clinical research, it has not yet been utilized as much as in clinical settings. We have been performing automated recognition of various pathological animal tissues and quantitative analysis of pathological findings, including liver and lung. In this study, we attempted to construct an artificial intelligence (AI)-based trained model that can automatedly recognize glomerular lesions in mouse kidneys that are characterized by complex structures. Materials and methods: By using hematoxylin and eosin (HE)-stained whole slide images (WSI) from Col4a3 KO mice as variation data, normal glomeruli and glomerular lesions were annotated, and deep learning (DL) was performed with the use of the neural network classifier DenseNet system in HALO AI. The trained model was refined by correcting the annotation of misrecognized tissue area and reperforming DL. The accuracy of the trained model was confirmed by comparing the AI-obtained results with the pathological grades evaluated by pathologists. The generality of the trained model was also confirmed by analyzing the WSI of adriamycin (ADR)-induced nephropathy mice, which is a different disease model. Results: Glomerular lesions (including mesangial proliferation, crescent formation, and sclerosis) observed in Col4a3 KO mice and ADR mice were detected by our trained model. The number of glomerular lesions detected by our trained model were also highly correlated with that of counted by pathologists. Conclusion: In this study, we constructed a trained model allowing us to automatedly recognize glomerular lesions in the mouse kidney with the use of the HALO AI system. The findings and insights of this study will facilitate the development of digital pathology in non-clinical research and improve the probability of success in drug discovery research. |
| first_indexed | 2024-04-11T04:59:29Z |
| format | Article |
| id | doaj.art-17ee94255b8e4108a6dc870a22a190a5 |
| institution | Directory Open Access Journal |
| issn | 2153-3539 |
| language | English |
| last_indexed | 2024-04-11T04:59:29Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj.art-17ee94255b8e4108a6dc870a22a190a52022-12-26T04:08:53ZengElsevierJournal of Pathology Informatics2153-35392022-01-0113100129Automated recognition of glomerular lesions in the kidneys of mice by using deep learningAiri Akatsuka0Yasushi Horai1Airi Akatsuka2Syonan iPark C43 building, Muraoka-Higashi 2-26-1, Fujisawa, Kanagawa 251-8555, JapanSyonan iPark C43 building, Muraoka-Higashi 2-26-1, Fujisawa, Kanagawa 251-8555, JapanCorresponding author.; Syonan iPark C43 building, Muraoka-Higashi 2-26-1, Fujisawa, Kanagawa 251-8555, JapanBackground: In recent years, digital pathology has been rapidly developing and applied throughout the world. Especially in clinical settings, it has been utilized in a variety of situations, including automated cancer diagnosis. Conversely, in non-clinical research, it has not yet been utilized as much as in clinical settings. We have been performing automated recognition of various pathological animal tissues and quantitative analysis of pathological findings, including liver and lung. In this study, we attempted to construct an artificial intelligence (AI)-based trained model that can automatedly recognize glomerular lesions in mouse kidneys that are characterized by complex structures. Materials and methods: By using hematoxylin and eosin (HE)-stained whole slide images (WSI) from Col4a3 KO mice as variation data, normal glomeruli and glomerular lesions were annotated, and deep learning (DL) was performed with the use of the neural network classifier DenseNet system in HALO AI. The trained model was refined by correcting the annotation of misrecognized tissue area and reperforming DL. The accuracy of the trained model was confirmed by comparing the AI-obtained results with the pathological grades evaluated by pathologists. The generality of the trained model was also confirmed by analyzing the WSI of adriamycin (ADR)-induced nephropathy mice, which is a different disease model. Results: Glomerular lesions (including mesangial proliferation, crescent formation, and sclerosis) observed in Col4a3 KO mice and ADR mice were detected by our trained model. The number of glomerular lesions detected by our trained model were also highly correlated with that of counted by pathologists. Conclusion: In this study, we constructed a trained model allowing us to automatedly recognize glomerular lesions in the mouse kidney with the use of the HALO AI system. The findings and insights of this study will facilitate the development of digital pathology in non-clinical research and improve the probability of success in drug discovery research.http://www.sciencedirect.com/science/article/pii/S2153353922007234Digital pathologyHALO AIDeep learning whole slide imageAutomated recognitionGlomerular lesionsPathological evaluation |
| spellingShingle | Airi Akatsuka Yasushi Horai Airi Akatsuka Automated recognition of glomerular lesions in the kidneys of mice by using deep learning Journal of Pathology Informatics Digital pathology HALO AI Deep learning whole slide image Automated recognition Glomerular lesions Pathological evaluation |
| title | Automated recognition of glomerular lesions in the kidneys of mice by using deep learning |
| title_full | Automated recognition of glomerular lesions in the kidneys of mice by using deep learning |
| title_fullStr | Automated recognition of glomerular lesions in the kidneys of mice by using deep learning |
| title_full_unstemmed | Automated recognition of glomerular lesions in the kidneys of mice by using deep learning |
| title_short | Automated recognition of glomerular lesions in the kidneys of mice by using deep learning |
| title_sort | automated recognition of glomerular lesions in the kidneys of mice by using deep learning |
| topic | Digital pathology HALO AI Deep learning whole slide image Automated recognition Glomerular lesions Pathological evaluation |
| url | http://www.sciencedirect.com/science/article/pii/S2153353922007234 |
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