The automatic detection of diabetic kidney disease from retinal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients

BMC Medical Informatics and Decision Making

Research

Baifang Zhang¹  Juan Zhang²  Lihua Ni²  Xiaoyan Wu³  Shaomin Shi⁴  Jing Xiao⁵  Wan Xu⁵  Yuan Tian⁵  Ling Gao⁵ 

[1] Department of Biochemistry, Wuhan University TaiKang Medical School (School of Basic Medical Sciences), 430071, Wuhan, Hubei, China;Department of Nephrology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, 430071, Wuhan, Hubei, China;Department of Nephrology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, 430071, Wuhan, Hubei, China;Department of General Practice, Zhongnan Hospital of Wuhan University, 169 Donghu Road, 430071, Wuhan, Hubei, China;Department of Nephrology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, 430071, Wuhan, Hubei, China;Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, 441000, Xiangyang, Hubei, China;Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, 441000, Xiangyang, Hubei, China;

关键词: Diabetic kidney disease;    Diabetic retinopathy;    Type 2 diabetes;    Fundus photography;    Artificial intelligence;   

DOI  :  10.1186/s12911-023-02343-9

 received in 2023-06-14, accepted in 2023-10-16,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundDiabetic kidney disease (DKD) has become the largest cause of end-stage kidney disease. Early and accurate detection of DKD is beneficial for patients. The present detection depends on the measurement of albuminuria or the estimated glomerular filtration rate, which is invasive and not optimal; therefore, new detection tools are urgently needed. Meanwhile, a close relationship between diabetic retinopathy and DKD has been reported; thus, we aimed to develop a novel detection algorithm for DKD using artificial intelligence technology based on retinal vascular parameters combined with several easily available clinical parameters in patients with type-2 diabetes.MethodsA total of 515 consecutive patients with type-2 diabetes mellitus from Xiangyang Central Hospital were included. Patients were stratified by DKD diagnosis and split randomly into either the training set (70%, N = 360) or the testing set (30%, N = 155) (random seed = 1). Data from the training set were used to develop the machine learning algorithm (MLA), while those from the testing set were used to validate the MLA. Model performances were evaluated.ResultsThe MLA using the random forest classifier presented optimal performance compared with other classifiers. When validated, the accuracy, sensitivity, specificity, F1 score, and AUC for the optimal model were 84.5%(95% CI 83.3–85.7), 84.5%(82.3–86.7), 84.5%(82.7–86.3), 0.845(0.831–0.859), and 0.914(0.903–0.925), respectively.ConclusionsA new machine learning algorithm for DKD diagnosis based on fundus images and 8 easily available clinical parameters was developed, which indicated that retinal vascular changes can assist in DKD screening and detection.

【 授权许可】

CC BY   
© The Author(s) 2023

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