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A multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imaging

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机构: [1]Chinese Peoples Liberat Army Gen Hosp, Dept Nephrol, Med Ctr 1, Beijing, Peoples R China [2]Chinese Peoples Liberat Army, Nephrol Inst, Beijing, Peoples R China [3]State Key Lab Kidney Dis, Beijing, Peoples R China [4]Natl Clin Res Ctr Kidney Dis, Beijing, Peoples R China [5]Chinese Peoples Liberat Army Gen Hosp, Beijing, Peoples R China [6]Univ Cambridge, Dept Clin Neurosci, Cambridge, England [7]Beihang Univ, Sch Elect & Informat Engn, Beijing, Peoples R China [8]Chinese Peoples Liberat Army Gen Hosp, Senior Dept Ophthalmol, Med Ctr 3, Beijing, Peoples R China [9]Capital Med Univ, Beijing Tongren Hosp, Beijing Tongren Eye Ctr, Beijing, Peoples R China [10]Guangdong Pharmaceut Univ, Sch Clin Med, Guangzhou, Peoples R China [11]Chinese Peoples Liberat Army Gen Hosp, Dept Ophthalmol, Med Ctr 1, Beijing, Peoples R China [12]Chinese Peoples Liberat Army Gen Hosp, Dept Pediat, Seventh Med Ctr, Beijing, Peoples R China [13]Capital Med Univ, Beijing Tongren Hosp, Beijing Diabet Inst, Beijing Key Lab Diabet Res & Care, Beijing, Peoples R China [14]Army Med Univ, Daping Hosp, Dept Nephrol, Chongqing, Peoples R China [15]Dalian Med Univ, Ctr Transformat Med Kidney Dis, Key Lab Kidney Dis, Affiliated Hosp 1, Dalian, Peoples R China [16]Chinese Peoples Liberat Army Gen Hosp, Dept Ophthalmol, Med Ctr 3, Beijing, Peoples R China
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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).

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大类 | 1 区 医学
小类 | 1 区 卫生保健与服务 1 区 医学:信息
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大类 | 1 区 医学
小类 | 1 区 卫生保健与服务 1 区 医学:信息
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出版当年[2023]版:
Q1 HEALTH CARE SCIENCES & SERVICES Q1 MEDICAL INFORMATICS
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Q1 HEALTH CARE SCIENCES & SERVICES Q1 MEDICAL INFORMATICS

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第一作者机构: [1]Chinese Peoples Liberat Army Gen Hosp, Dept Nephrol, Med Ctr 1, Beijing, Peoples R China [2]Chinese Peoples Liberat Army, Nephrol Inst, Beijing, Peoples R China [3]State Key Lab Kidney Dis, Beijing, Peoples R China [4]Natl Clin Res Ctr Kidney Dis, Beijing, Peoples R China [5]Chinese Peoples Liberat Army Gen Hosp, Beijing, Peoples R China
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通讯机构: [1]Chinese Peoples Liberat Army Gen Hosp, Dept Nephrol, Med Ctr 1, Beijing, Peoples R China [2]Chinese Peoples Liberat Army, Nephrol Inst, Beijing, Peoples R China [3]State Key Lab Kidney Dis, Beijing, Peoples R China [4]Natl Clin Res Ctr Kidney Dis, Beijing, Peoples R China [5]Chinese Peoples Liberat Army Gen Hosp, Beijing, Peoples R China
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