Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy
Objectives Renal biopsy is the gold standard for the diagnosis of glomerular diseases including membranous nephropathy (MN), however, it faces challenges in accuracy, objectivity, and reproducibility of tissue evaluation. This study aims to develop a multimodal pathological diagnosis system to assis...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2025.2528106 |
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| author | Xiuxiu Hu Jinyue Yang Yiping Li Yuxiang Gong Haifeng Ni Qing Wei Minyu Yang Yu Zhang Jing Huang Cao Ma Bizhen Wei Kaijie Yu Jiayun Xu Siyu Xia Taotao Tang Pingsheng Chen |
| author_facet | Xiuxiu Hu Jinyue Yang Yiping Li Yuxiang Gong Haifeng Ni Qing Wei Minyu Yang Yu Zhang Jing Huang Cao Ma Bizhen Wei Kaijie Yu Jiayun Xu Siyu Xia Taotao Tang Pingsheng Chen |
| author_sort | Xiuxiu Hu |
| collection | DOAJ |
| description | Objectives Renal biopsy is the gold standard for the diagnosis of glomerular diseases including membranous nephropathy (MN), however, it faces challenges in accuracy, objectivity, and reproducibility of tissue evaluation. This study aims to develop a multimodal pathological diagnosis system to assist pathologists in diagnosing MN in morphology.Methods Using PASM-stained, immunofluorescence, and electron microscopy images from MN patients, we built three deep-learning models to detect lesions. The outputs of these models were combined to provide a comprehensive pathological diagnosis. Our system was compared with pathologists, validated on external test sets, and detected in 138 patients with various kidney diseases.Results Considering PASM-stained images, our model had a classification accuracy of 91.74%, a recall of 81.97%, and an F1 score of 86.58% for spike identification. For immunofluorescence images, our model had an accuracy rate of 98.97%, a recall rate of 99.65%, and an F1 score of 99.31% for MN classification. Regarding the segmentation of electron-dense deposits, the segmentation model had a Dice coefficient of 85.66% and an IoU of 75.93%. Our model presented superior performance to that of pathologists in fluorescence image classification and segmentation of deposits, achieved high accuracy in spike identification and fluorescence image classification in external test sets, and could be targeted to diagnose MN in a wide range of glomerular diseases.Conclusions This multimodal pathological diagnosis system can not only assist pathologists in diagnosing MN rapidly and accurately but also lays the foundation to develop diagnostic models for other glomerular diseases. |
| format | Article |
| id | doaj-art-4303eaa8de974f72bf05328bbe5c5c6d |
| institution | OA Journals |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-4303eaa8de974f72bf05328bbe5c5c6d2025-08-20T02:37:21ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492025-12-0147110.1080/0886022X.2025.2528106Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathyXiuxiu Hu0Jinyue Yang1Yiping Li2Yuxiang Gong3Haifeng Ni4Qing Wei5Minyu Yang6Yu Zhang7Jing Huang8Cao Ma9Bizhen Wei10Kaijie Yu11Jiayun Xu12Siyu Xia13Taotao Tang14Pingsheng Chen15Department of Pathology, School of Medicine, Southeast University, Nanjing, ChinaSchool of Automation, Southeast University, Nanjing, ChinaDepartment of Pathology, School of Medicine, Southeast University, Nanjing, ChinaDepartment of Nephrology, Zhongda Hospital, Southeast University, Nanjing, ChinaDepartment of Nephrology, Zhongda Hospital, Southeast University, Nanjing, ChinaDepartment of Nephrology, Zhongda Hospital, Southeast University, Nanjing, ChinaDepartment of Nephrology, Zhongda Hospital, Southeast University, Nanjing, ChinaDepartment of Pathology, School of Medicine, Southeast University, Nanjing, ChinaDepartment of Respiratory and Critical Care Medicine, Zhongda Hospital, Southeast University, Nanjing, ChinaDepartment of Pathology, School of Medicine, Southeast University, Nanjing, ChinaDepartment of Pathology, School of Medicine, Southeast University, Nanjing, ChinaDepartment of Pathology, School of Medicine, Southeast University, Nanjing, ChinaDepartment of Nephrology, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, ChinaSchool of Automation, Southeast University, Nanjing, ChinaDepartment of Pathology, School of Medicine, Southeast University, Nanjing, ChinaDepartment of Pathology, School of Medicine, Southeast University, Nanjing, ChinaObjectives Renal biopsy is the gold standard for the diagnosis of glomerular diseases including membranous nephropathy (MN), however, it faces challenges in accuracy, objectivity, and reproducibility of tissue evaluation. This study aims to develop a multimodal pathological diagnosis system to assist pathologists in diagnosing MN in morphology.Methods Using PASM-stained, immunofluorescence, and electron microscopy images from MN patients, we built three deep-learning models to detect lesions. The outputs of these models were combined to provide a comprehensive pathological diagnosis. Our system was compared with pathologists, validated on external test sets, and detected in 138 patients with various kidney diseases.Results Considering PASM-stained images, our model had a classification accuracy of 91.74%, a recall of 81.97%, and an F1 score of 86.58% for spike identification. For immunofluorescence images, our model had an accuracy rate of 98.97%, a recall rate of 99.65%, and an F1 score of 99.31% for MN classification. Regarding the segmentation of electron-dense deposits, the segmentation model had a Dice coefficient of 85.66% and an IoU of 75.93%. Our model presented superior performance to that of pathologists in fluorescence image classification and segmentation of deposits, achieved high accuracy in spike identification and fluorescence image classification in external test sets, and could be targeted to diagnose MN in a wide range of glomerular diseases.Conclusions This multimodal pathological diagnosis system can not only assist pathologists in diagnosing MN rapidly and accurately but also lays the foundation to develop diagnostic models for other glomerular diseases.https://www.tandfonline.com/doi/10.1080/0886022X.2025.2528106Membranous nephropathyrenal biopsymultimodal pathological diagnosisartificial intelligencedeep learning |
| spellingShingle | Xiuxiu Hu Jinyue Yang Yiping Li Yuxiang Gong Haifeng Ni Qing Wei Minyu Yang Yu Zhang Jing Huang Cao Ma Bizhen Wei Kaijie Yu Jiayun Xu Siyu Xia Taotao Tang Pingsheng Chen Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy Renal Failure Membranous nephropathy renal biopsy multimodal pathological diagnosis artificial intelligence deep learning |
| title | Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy |
| title_full | Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy |
| title_fullStr | Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy |
| title_full_unstemmed | Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy |
| title_short | Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy |
| title_sort | multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy |
| topic | Membranous nephropathy renal biopsy multimodal pathological diagnosis artificial intelligence deep learning |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2025.2528106 |
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