Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China.
Department of Ophthalmology, Shanghai General Hospital, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
J Diabetes. 2022 Feb;14(2):111-120. doi: 10.1111/1753-0407.13241. Epub 2021 Dec 9.
The aim of our research was to prospectively explore the clinical value of a deep learning algorithm (DLA) to detect referable diabetic retinopathy (DR) in different subgroups stratified by types of diabetes, blood pressure, sex, BMI, age, glycosylated hemoglobin (HbA1c), diabetes duration, urine albumin-to-creatinine ratio (UACR), and estimated glomerular filtration rate (eGFR) at a real-world diabetes center in China.
A total of 1147 diabetic patients from Shanghai General Hospital were recruited from October 2018 to August 2019. Retinal fundus images were graded by the DLA, and the detection of referable DR (moderate nonproliferative DR or worse) was compared with a reference standard generated by one certified retinal specialist with more than 12 years of experience. The performance of DLA across different subgroups stratified by types of diabetes, blood pressure, sex, BMI, age, HbA1c, diabetes duration, UACR, and eGFR was evaluated.
For all 1674 gradable images, the area under the receiver operating curve, sensitivity, and specificity of the DLA for referable DR were 0.942 (95% CI, 0.920-0.964), 85.1% (95% CI, 83.4%-86.8%), and 95.6% (95% CI, 94.6%-96.6%), respectively. The DLA showed consistent performance across most subgroups, while it showed superior performance in the subgroups of patients with type 1 diabetes, UACR ≥ 30 mg/g, and eGFR < 90 mL/min/1.73m .
This study showed that the DLA was a reliable alternative method for the detection of referable DR and performed superior in patients with type 1 diabetes and diabetic nephropathy who were prone to DR.
我们的研究旨在前瞻性探索深度学习算法（DLA）在中国实际糖尿病中心对不同类型糖尿病、血压、性别、BMI、年龄、糖化血红蛋白（HbA1c）、糖尿病病程、尿白蛋白与肌酐比值（UACR）和估算肾小球滤过率（eGFR）等亚组中检测可转诊糖尿病视网膜病变（DR）的临床价值。
本研究共纳入 2018 年 10 月至 2019 年 8 月上海某医院的 1147 例糖尿病患者。使用 DLA 对视网膜眼底图像进行分级，并与一名具有 12 年以上经验的认证视网膜专家生成的参考标准进行比较，以检测可转诊的 DR（中度非增生性 DR 或更严重）。评估 DLA 在不同类型糖尿病、血压、性别、BMI、年龄、HbA1c、糖尿病病程、UACR 和 eGFR 分层的亚组中的性能。
对于所有 1674 张可分级图像，DLA 检测可转诊 DR 的曲线下面积、敏感度和特异度分别为 0.942（95% CI，0.920-0.964）、85.1%（95% CI，83.4%-86.8%）和 95.6%（95% CI，94.6%-96.6%）。DLA 在大多数亚组中的表现一致，而在 1 型糖尿病、UACR≥30mg/g 和 eGFR<90mL/min/1.73m 患者亚组中表现更优。
该研究表明，DLA 是检测可转诊 DR 的可靠替代方法，在易患 DR 的 1 型糖尿病和糖尿病肾病患者中表现更佳。
