CASE STUDIES 案例研究

National Use of Artificial Intelligence for Eye Screening in Singapore

D.V. Gunasekeran and Others

Abstract

Diabetes is a major health care challenge, affecting 10% of the global population. One third of patients with diabetes have an ocular complication known as diabetic retinopathy (DR). DR progression to manifestations such as vision-threatening diabetic retinopathy (VTDR) remains the leading cause of blindness in working-aged adults. Yearly DR screening is a universally recommended practice in primary care settings for patients with diabetes, but it is often difficult to implement due to a lack of staffing and screening capacity in primary care. This case study highlights our experience with developing a medical artificial intelligence (AI) software-as-a-medical-device (SaMD) solution for DR screening and implementing it at a national level to provide the capacity needed for DR screening in Singapore. Our approach involved two broad phases. First, we established a national telemedicine screening program, Singapore Integrated Diabetic Retinopathy Program (SiDRP), for population screening of DR in primary care run by trained, nonclinician human graders. Second, we deployed a deep learning–based AI solution, Singapore Eye Lesion Analyzer (SELENA+), into the SiDRP to scale-up the DR screening process by the human graders. We demonstrated the cost-effectiveness of this solution, and obtained medical device regulatory approval for clinical use in health care settings. We report the prospective evaluation of SELENA+ in SiDRP using real-world pilot data from the first 1712 patients consecutively recruited. Sensitivity and specificity of SELENA+ in detection of referable DR cases were 94.7% (95% confidence interval [CI] 88.0% to 98.3%) and 82.2% (95% CI 80.8% to 83.5%), respectively. In comparison, sensitivity and specificity of human graders were 98.9% (95% CI 94.0% to 99.9%) and 97.2% (95% CI 96.6–97.8%), respectively. For patients with VTDR, SELENA+ demonstrated a substantial advantage of higher sensitivity compared with human performance, reflecting the benefit of the fine-tuning of SELENA+ that we performed to enhance the AI solution’s ability to detect VTDR. We outline the clinical, technical, operational, regulatory, and governance challenges encountered as well as the lessons learnt in this AI algorithm implementation journey. We also present a conceptual framework with considerations and strategies for the broader adoption of medical AI SaMD solutions in the field of ophthalmology and beyond.

DOI: 10.1056/AIcs2400404

全文链接：https://ai.nejm.org/doi/abs/10.1056/AIcs2400404

新加坡全国使用人工智能进行眼部筛查

D.V. Gunasekeran 等人

摘要： 糖尿病是一个主要的健康护理挑战，影响着全球10%的人口。三分之一的糖尿病患者有一种称为糖尿病视网膜病变（DR）的眼部并发症。DR发展到如威胁视力的糖尿病视网膜病变（VTDR）等表现仍然是工作年龄成人失明的主要原因。每年在初级保健设置中为糖尿病患者进行DR筛查是普遍推荐的做法，但由于初级保健中缺乏人员和筛查能力，往往难以实施。这个案例研究突出了我们开发医疗人工智能（AI）软件即医疗设备（SaMD）解决方案进行DR筛查并在新加坡全国范围内实施以提供DR筛查所需能力的经验。我们的方法包括两个广泛的阶段。首先，我们建立了一个全国远程医疗筛查计划，新加坡综合糖尿病视网膜病变计划（SiDRP），由训练有素的非临床医生人工评分员运行，用于在初级保健中对DR进行人群筛查。其次，我们将基于深度学习的AI解决方案，新加坡眼部病变分析器（SELENA+），部署到SiDRP中，以扩大人类评分员的DR筛查流程。我们展示了这种解决方案的成本效益，并获得了医疗设备监管批准，用于在健康护理设置中的临床使用。我们报告了在SiDRP中使用SELENA+的前瞻性评估，使用连续招募的前1712名患者的现实世界试点数据。SELENA+在检测可参考DR病例中的敏感性和特异性分别为94.7%（95%置信区间[CI] 88.0%至98.3%）和82.2%（95% CI 80.8%至83.5%）。相比之下，人工评分员的敏感性和特异性分别为98.9%（95% CI 94.0%至99.9%）和97.2%（95% CI 96.6–97.8%）。对于VTDR患者，SELENA+显示出与人类表现相比更高的敏感性优势，反映了我们对SELENA+进行微调以增强AI解决方案检测VTDR的能力的好处。我们概述了在这次AI算法实施旅程中遇到的临床、技术、运营、监管和治理挑战以及学到的经验教训。我们还提出了一个概念框架，考虑和策略用于在眼科领域及更广泛地采用医疗AI SaMD解决方案。

NEJM AI, Volume 1 No. 12 December 2024

译文来自于AI工具Kimi