标题： 基于人工智能的糖尿病视网膜病变筛查：AIDRSS在印度的多中心验证 显示英文标题

标题： AI-Driven Diabetic Retinopathy Screening: Multicentric Validation of AIDRSS in India

摘要： 目的：糖尿病视网膜病变（DR）是视力丧失的主要原因，尤其是在印度，农村地区视网膜专家的资源有限。 本研究旨在评估基于人工智能的糖尿病视网膜病变筛查系统（AIDRSS），用于DR检测和患病率评估，以解决资源有限环境中对可扩展、自动化筛查解决方案日益增长的需求。 方法：在印度加尔各答进行了一项多中心、横断面研究，涉及5,029名参与者和10,058张黄斑中心视网膜眼底图像。AIDRSS采用了一个具有5000万可训练参数的深度学习算法，并集成了对比限制自适应直方图均衡化（CLAHE）预处理以提高图像质量。 使用国际临床糖尿病视网膜病变（ICDR）量表对DR进行分级，将疾病分为五个阶段（DR0至DR4）。 统计指标包括敏感性、特异性和患病率，与专家视网膜专科医生的评估进行比较。 结果：一般人群中的DR患病率为13.7%，而在随机血糖水平升高的个体中，患病率上升至38.2%。 AIDRSS总体敏感性为92%，特异性为88%，对于可转诊DR（DR3和DR4）的检测敏感性为100%。 这些结果表明该系统在准确识别和分级多样化人群中的DR方面表现出色。 结论：AIDRSS为资源匮乏环境中的早期DR检测提供了一个可靠且可扩展的解决方案。 其集成的先进AI技术确保了高诊断准确性，有望显著减轻资源不足地区的糖尿病相关视力损失负担。 显示英文摘要

摘要： Purpose: Diabetic retinopathy (DR) is a major cause of vision loss, particularly in India, where access to retina specialists is limited in rural areas. This study aims to evaluate the Artificial Intelligence-based Diabetic Retinopathy Screening System (AIDRSS) for DR detection and prevalence assessment, addressing the growing need for scalable, automated screening solutions in resource-limited settings. Approach: A multicentric, cross-sectional study was conducted in Kolkata, India, involving 5,029 participants and 10,058 macula-centric retinal fundus images. The AIDRSS employed a deep learning algorithm with 50 million trainable parameters, integrated with Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing for enhanced image quality. DR was graded using the International Clinical Diabetic Retinopathy (ICDR) Scale, categorizing disease into five stages (DR0 to DR4). Statistical metrics including sensitivity, specificity, and prevalence rates were evaluated against expert retina specialist assessments. Results: The prevalence of DR in the general population was 13.7%, rising to 38.2% among individuals with elevated random blood glucose levels. The AIDRSS achieved an overall sensitivity of 92%, specificity of 88%, and 100% sensitivity for detecting referable DR (DR3 and DR4). These results demonstrate the system's robust performance in accurately identifying and grading DR in a diverse population. Conclusions: AIDRSS provides a reliable, scalable solution for early DR detection in resource-constrained environments. Its integration of advanced AI techniques ensures high diagnostic accuracy, with potential to significantly reduce the burden of diabetes-related vision loss in underserved regions.