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SMART(启用人工智能的)DROP(糖尿病视网膜病变结局与路径):糖尿病视网膜病变管理研究方案

SMART (artificial intelligence enabled) DROP (diabetic retinopathy outcomes and pathways): Study protocol for diabetic retinopathy management.

作者信息

Rani Padmaja Kumari, Kalavalapalli DurgaBhavani, Narayanan Raja, Kalavalapalli Shyam, Narula Ritesh, Sahay Rakesh K, Deo Sarang

机构信息

Department of Teleophthalmology, L V Prasad Eye Institute, Hyderabad, Telangana, India.

Anant Bajaj Retina Institute, L V Prasad Eye Institute, Hyderabad, Telangana, India.

出版信息

PLoS One. 2025 May 19;20(5):e0324382. doi: 10.1371/journal.pone.0324382. eCollection 2025.

Abstract

INTRODUCTION

Delayed diagnosis of diabetic retinopathy (DR) remains a significant challenge, often leading to preventable blindness and visual impairment. Given that physicians are frequently the first point of contact for people with diabetes, there is a critical need for integrated screening programs within diabetes clinics to enhance DR management and reduce the risk of severe vision loss.

METHODS AND ANALYSIS

We will conduct a prospective cohort study comparing (i) the intervention cohort, screened at diabetes clinics and referred to eye clinics per the proposed pathway, and (ii) the standard-of-care (SOC) eye clinic cohort. The study will be conducted in Hyderabad, India, at LV Prasad Eye Institute and four IDEA (Institute of Diabetes, Endocrinology, and Adiposity) Clinics. The primary objective is to evaluate the effectiveness of a systematic diabetic retinopathy screening program in achieving earlier detection and reducing visual impairment among People With Diabetes (PWD) attending IDEA clinics compared to routine care at eye care settings. The screening program will be operationalized using AI-enabled tools and supported by trained non-medical technicians. We will perform visual acuity tests and non-mydriatic fundus photography using AI-assisted cameras. DR-positive patients will be referred for treatment and follow-up. We aim to achieve high accuracy (>90%) in appropriate referral of DR and high screening coverage (>80%) of eligible PWD. Success metrics include screening uptake, AI diagnostic accuracy, referral rates, cost-effectiveness, patient satisfaction, follow-up adherence, and long-term outcomes.

CONCLUSION

This study aims to enhance diabetic retinopathy screening and management through an AI-enabled approach at diabetes clinics, improving early detection and care pathways. The findings will contribute to evidence-based strategies for optimizing DR screening and management, with results disseminated through peer-reviewed publications to inform policy and practice.

TRIAL REGISTRATION

Trial registration number: CTRI/2024/03/064518 [Registered on: 20/03/2024] (https://ctri.nic.in/).

摘要

引言

糖尿病视网膜病变(DR)的延迟诊断仍然是一项重大挑战,常常导致可预防的失明和视力损害。鉴于医生通常是糖尿病患者的首要接触点,糖尿病诊所迫切需要实施综合筛查项目,以加强DR管理并降低严重视力丧失的风险。

方法与分析

我们将开展一项前瞻性队列研究,比较(i)干预队列,即在糖尿病诊所进行筛查并按照提议路径转诊至眼科诊所的队列,以及(ii)标准护理(SOC)眼科诊所队列。该研究将在印度海得拉巴的LV普拉萨德眼科研究所和四家IDEA(糖尿病、内分泌学和肥胖症研究所)诊所进行。主要目标是评估系统性糖尿病视网膜病变筛查项目在实现早期检测以及与眼科护理机构的常规护理相比减少就诊于IDEA诊所的糖尿病患者(PWD)视力损害方面的有效性。筛查项目将使用人工智能工具实施,并由经过培训的非医疗技术人员提供支持。我们将使用人工智能辅助相机进行视力测试和非散瞳眼底摄影。DR阳性患者将被转诊接受治疗和随访。我们的目标是在DR的适当转诊方面实现高准确率(>90%),并对符合条件的PWD实现高筛查覆盖率(>80%)。成功指标包括筛查接受率、人工智能诊断准确率、转诊率、成本效益、患者满意度、随访依从性和长期结果。

结论

本研究旨在通过糖尿病诊所的人工智能方法加强糖尿病视网膜病变的筛查和管理,改善早期检测和护理路径。研究结果将为优化DR筛查和管理的循证策略做出贡献,研究结果将通过同行评审出版物传播,为政策和实践提供参考。

试验注册

试验注册号:CTRI/2024/03/064518 [注册日期:2024年3月20日](https://ctri.nic.in/)

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/123a/12088010/3cf9b215a2b6/pone.0324382.g001.jpg

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