JMIR Publications

ABSTRACT

Background:

Diabetic retinopathy (DR) affects about 25% of people with diabetes in Canada. Early detection of DR is essential for preventing vision loss.

Objective:

We evaluated the real-world performance of an artificial intelligence (AI) system that analyses fundus images for DR screening in a Quebec tertiary care centre.

Methods:

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 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.

Results:

The 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 considering 5,000 diabetic patients.

Conclusions:

Our study indicates that integrating a semi-automated 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.