Antaki Fares, Hammana Imane, Tessier Marie-Catherine, Boucher Andrée, David Jetté Maud Laurence, Beauchemin Catherine, Hammamji Karim, Ong Ariel Yuhan, Rhéaume Marc-André, Gauthier Danny, Harissi-Dagher Mona, Keane Pearse A, Pomp Alfons
Institute of Ophthalmology, University College London, London, United Kingdom.
Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.
JMIR Diabetes. 2024 Sep 3;9:e59867. doi: 10.2196/59867.
Diabetic retinopathy (DR) affects about 25% of people with diabetes in Canada. Early detection of DR is essential for preventing vision loss.
We evaluated the real-world performance of an artificial intelligence (AI) system that analyzes fundus images for DR screening in a Quebec tertiary care center.
We prospectively recruited adult patients with diabetes at the Centre hospitalier de l'Université de Montréal (CHUM) in Montreal, Quebec, Canada. Patients underwent dual-pathway screening: first by the Computer Assisted Retinal Analysis (CARA) AI system (index test), then by standard ophthalmological examination (reference standard). We measured the AI system's sensitivity and specificity for detecting referable disease at the patient level, along with its performance for detecting any retinopathy and diabetic macular edema (DME) at the eye level, and potential cost savings.
This study included 115 patients. CARA demonstrated a sensitivity of 87.5% (95% CI 71.9-95.0) and specificity of 66.2% (95% CI 54.3-76.3) for detecting referable disease at the patient level. For any retinopathy detection at the eye level, CARA showed 88.2% sensitivity (95% CI 76.6-94.5) and 71.4% specificity (95% CI 63.7-78.1). For DME detection, CARA had 100% sensitivity (95% CI 64.6-100) and 81.9% specificity (95% CI 75.6-86.8). Potential yearly savings from implementing CARA at the CHUM were estimated at CAD $245,635 (US $177,643.23, as of July 26, 2024) considering 5000 patients with diabetes.
Our study indicates that integrating a semiautomated AI system for DR screening demonstrates high sensitivity for detecting referable disease in a real-world setting. This system has the potential to improve screening efficiency and reduce costs at the CHUM, but more work is needed to validate it.
在加拿大，糖尿病视网膜病变（DR）影响着约25%的糖尿病患者。早期发现DR对于预防视力丧失至关重要。
我们评估了一种人工智能（AI）系统在魁北克一家三级护理中心分析眼底图像进行DR筛查的实际性能。
我们在加拿大魁北克省蒙特利尔市的蒙特利尔大学中心医院（CHUM）前瞻性招募成年糖尿病患者。患者接受双途径筛查：首先通过计算机辅助视网膜分析（CARA）AI系统（指标检测），然后通过标准眼科检查（参考标准）。我们在患者层面测量了AI系统检测可转诊疾病的敏感性和特异性，以及在眼部层面检测任何视网膜病变和糖尿病性黄斑水肿（DME）的性能，以及潜在的成本节约。
本研究纳入了115名患者。CARA在患者层面检测可转诊疾病的敏感性为87.5%（95%CI 71.9 - 95.0），特异性为66.2%（95%CI 54.3 - 76.3）。在眼部层面检测任何视网膜病变时，CARA的敏感性为88.2%（95%CI 76.6 - 94.5），特异性为71.4%（95%CI 63.7 - 78.1）。对于DME检测，CARA的敏感性为100%（95%CI 64.6 - 100），特异性为81.9%（95%CI 75.6 - 86.8）。考虑到5000名糖尿病患者，在CHUM实施CARA每年潜在节省的费用估计为245,635加元（截至2024年7月26日为177,643.23美元）。
我们的研究表明，整合用于DR筛查的半自动AI系统在实际环境中检测可转诊疾病具有高敏感性。该系统有可能提高CHUM的筛查效率并降低成本，但需要更多工作来验证它。
