Abstract
OBJECTIVE: To develop a multimodal model to predict chronic kidney disease (CKD) in patients with type 2 diabetes mellitus (T2DM), given the limited research on this integrative approach.
RESEARCH DESIGN AND METHODS: We obtained multimodal data sets from Kyung Hee University Medical Center (n = 7,028; discovery cohort) for training and internal validation and UK Biobank (n = 1,544; validation cohort) for external validation. CKD was defined based on ICD-9 and ICD-10 codes and/or estimated glomerular filtration rate (eGFR) ≤60 mL/min/1.73 m2. We ensembled various deep learning models and interpreted their predictions using explainable artificial intelligence (AI) methods, including Shapley additive explanation values (SHAP) and gradient-weighted class activation mapping (Grad-CAM). Subsequently, we investigated the potential association between the model probability and vascular complications.
RESULTS: The multimodal model, which ensembles visual geometry group 16 and deep neural network, presented high performance in predicting CKD, with area under the receiver operating characteristic curve of 0.880 (95% CI 0.806-0.954) in the discovery cohort and 0.722 in the validation cohort. SHAP and Grad-CAM highlighted key predictors, including eGFR and optic disc, respectively. The model probability was associated with an increased risk of macrovascular complications (tertile 1 [T1]: adjusted hazard ratio, 1.42 [95% CI 1.06-1.90]; T2: 1.59 [1.17-2.16]; T3: 1.64 [1.20-2.26]) and microvascular complications (T3: 1.30 [1.02-1.67]).
CONCLUSIONS: Our multimodal AI model integrates fundus images and clinical data from binational cohorts to predict the risk of new-onset CKD within 5 years and associated vascular complications in patients with T2DM.