A multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imaging
Abstract Differentiating between diabetic nephropathy (DN) and non-diabetic renal disease (NDRD) without a kidney biopsy remains a major challenge, often leading to missed opportunities for targeted treatments that could greatly improve NDRD outcomes. To reform the traditional biopsy-all diagnostic...
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Nature Portfolio
2025-01-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-024-01393-1 |
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| author | Zheyi Dong Xiaofei Wang Sai Pan Taohan Weng Xiaoniao Chen Shuangshuang Jiang Ying Li Zonghua Wang Xueying Cao Qian Wang Pu Chen Lai Jiang Guangyan Cai Li Zhang Yong Wang Jinkui Yang Yani He Hongli Lin Jie Wu Li Tang Jianhui Zhou Shengxi Li Zhaohui Li Yibing Fu Xinyue Yu Yanqiu Geng Yingjie Zhang Liqiang Wang Mai Xu Xiangmei Chen |
| author_facet | Zheyi Dong Xiaofei Wang Sai Pan Taohan Weng Xiaoniao Chen Shuangshuang Jiang Ying Li Zonghua Wang Xueying Cao Qian Wang Pu Chen Lai Jiang Guangyan Cai Li Zhang Yong Wang Jinkui Yang Yani He Hongli Lin Jie Wu Li Tang Jianhui Zhou Shengxi Li Zhaohui Li Yibing Fu Xinyue Yu Yanqiu Geng Yingjie Zhang Liqiang Wang Mai Xu Xiangmei Chen |
| author_sort | Zheyi Dong |
| collection | DOAJ |
| description | Abstract Differentiating between diabetic nephropathy (DN) and non-diabetic renal disease (NDRD) without a kidney biopsy remains a major challenge, often leading to missed opportunities for targeted treatments that could greatly improve NDRD outcomes. To reform the traditional biopsy-all diagnostic paradigm and avoid unnecessary biopsy, we developed a transformer-based deep learning (DL) system for detecting DN and NDRD upon non-invasive multi-modal data of fundus images and clinical characteristics. Our Trans-MUF achieved an AUC of 0.980 (95% CI: 0.979 to 0.980) over the internal retrospective set and also had superior generalizability over a prospective dataset (AUC: 0.989, 95% CI: 0.987 to 0.990) and a multicenter, cross-machine and multi-operator dataset (AUC: 0.932, 95% CI: 0.931 to 0.939). Moreover, the nephrologists‘ diagnosis accuracy can be improved by 21%, through visualization assistance of the DL system. This paper lays a foundation for automatically differentiating DN and NDRD without biopsy. (Registry name: Correlation Study Between Clinical Phenotype and Pathology of Type 2 Diabetic Nephropathy. ID: NCT03865914. Date: 2017-11-30). |
| format | Article |
| id | doaj-art-b1f193c5917a4e5f9ea7168200776bd1 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-b1f193c5917a4e5f9ea7168200776bd12025-01-26T12:53:54ZengNature Portfolionpj Digital Medicine2398-63522025-01-018111410.1038/s41746-024-01393-1A multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imagingZheyi Dong0Xiaofei Wang1Sai Pan2Taohan Weng3Xiaoniao Chen4Shuangshuang Jiang5Ying Li6Zonghua Wang7Xueying Cao8Qian Wang9Pu Chen10Lai Jiang11Guangyan Cai12Li Zhang13Yong Wang14Jinkui Yang15Yani He16Hongli Lin17Jie Wu18Li Tang19Jianhui Zhou20Shengxi Li21Zhaohui Li22Yibing Fu23Xinyue Yu24Yanqiu Geng25Yingjie Zhang26Liqiang Wang27Mai Xu28Xiangmei Chen29Department of Nephrology, First Medical Center of Chinese People’s Liberation Army General HospitalDepartment of Clinical Neurosciences, University of CambridgeDepartment of Nephrology, First Medical Center of Chinese People’s Liberation Army General HospitalSchool of Electronic and Information Engineering, Beihang UniversitySenior Department of Ophthalmology, Third Medical Center of Chinese PLA General HospitalDepartment of Nephrology, First Medical Center of Chinese People’s Liberation Army General HospitalDepartment of Ophthalmology, First Medical Center of Chinese PLA General HospitalDepartment of Ophthalmology, Seventh Medical Center of Chinese PLA General HospitalDepartment of Nephrology, First Medical Center of Chinese People’s Liberation Army General HospitalDepartment of Nephrology, First Medical Center of Chinese People’s Liberation Army General HospitalDepartment of Nephrology, First Medical Center of Chinese People’s Liberation Army General HospitalSchool of Electronic and Information Engineering, Beihang UniversityDepartment of Nephrology, First Medical Center of Chinese People’s Liberation Army General HospitalDepartment of Nephrology, First Medical Center of Chinese People’s Liberation Army General HospitalDepartment of Nephrology, First Medical Center of Chinese People’s Liberation Army General HospitalBeijing Key Laboratory of Diabetes Research and Care, Beijing Diabetes Institute, Beijing Tongren Hospital, Capital Medical UniversityDepartment of nephrology, Daping Hospital, Army Medical UniversityThe First Affiliated Hospital of Dalian Medical UniversityKey Laboratory of Kidney Disease, Center for the Transformation Medicine of Kidney DiseaseDepartment of Nephrology, First Medical Center of Chinese People’s Liberation Army General HospitalDepartment of Nephrology, First Medical Center of Chinese People’s Liberation Army General HospitalDepartment of Nephrology, First Medical Center of Chinese People’s Liberation Army General HospitalSchool of Electronic and Information Engineering, Beihang UniversitySchool of Clinical Medicine, Guangdong Pharmaceutical UniversitySchool of Electronic and Information Engineering, Beihang UniversitySchool of Clinical Medicine, Guangdong Pharmaceutical UniversityDepartment of Nephrology, Third Medical Center of Chinese PLA General HospitalDepartment of Nephrology, First Medical Center of Chinese People’s Liberation Army General HospitalSenior Department of Ophthalmology, Third Medical Center of Chinese PLA General HospitalSchool of Electronic and Information Engineering, Beihang UniversityDepartment of Nephrology, First Medical Center of Chinese People’s Liberation Army General HospitalAbstract Differentiating between diabetic nephropathy (DN) and non-diabetic renal disease (NDRD) without a kidney biopsy remains a major challenge, often leading to missed opportunities for targeted treatments that could greatly improve NDRD outcomes. To reform the traditional biopsy-all diagnostic paradigm and avoid unnecessary biopsy, we developed a transformer-based deep learning (DL) system for detecting DN and NDRD upon non-invasive multi-modal data of fundus images and clinical characteristics. Our Trans-MUF achieved an AUC of 0.980 (95% CI: 0.979 to 0.980) over the internal retrospective set and also had superior generalizability over a prospective dataset (AUC: 0.989, 95% CI: 0.987 to 0.990) and a multicenter, cross-machine and multi-operator dataset (AUC: 0.932, 95% CI: 0.931 to 0.939). Moreover, the nephrologists‘ diagnosis accuracy can be improved by 21%, through visualization assistance of the DL system. This paper lays a foundation for automatically differentiating DN and NDRD without biopsy. (Registry name: Correlation Study Between Clinical Phenotype and Pathology of Type 2 Diabetic Nephropathy. ID: NCT03865914. Date: 2017-11-30).https://doi.org/10.1038/s41746-024-01393-1 |
| spellingShingle | Zheyi Dong Xiaofei Wang Sai Pan Taohan Weng Xiaoniao Chen Shuangshuang Jiang Ying Li Zonghua Wang Xueying Cao Qian Wang Pu Chen Lai Jiang Guangyan Cai Li Zhang Yong Wang Jinkui Yang Yani He Hongli Lin Jie Wu Li Tang Jianhui Zhou Shengxi Li Zhaohui Li Yibing Fu Xinyue Yu Yanqiu Geng Yingjie Zhang Liqiang Wang Mai Xu Xiangmei Chen A multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imaging npj Digital Medicine |
| title | A multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imaging |
| title_full | A multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imaging |
| title_fullStr | A multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imaging |
| title_full_unstemmed | A multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imaging |
| title_short | A multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imaging |
| title_sort | multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imaging |
| url | https://doi.org/10.1038/s41746-024-01393-1 |
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