Implementation of Artificial Intelligence-Based Diabetic Retinopathy Screening in a Tertiary Care Hospital in Quebec: Prospective Validation Study.

Autor:	Antaki F; Institute of Ophthalmology, University College London, London, United Kingdom.; Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.; Department of Ophthalmology, Université de Montréal, Montreal, QC, Canada.; The CHUM School of Artificial Intelligence in Healthcare, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.; Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom., Hammana I; Health Technology Assessment Unit, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada., Tessier MC; Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada., Boucher A; Division of Endocrinology, Department of Medicine, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada., David Jetté ML; Direction du soutien à la transformation, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada., Beauchemin C; Faculty of Pharmacy, University of Montreal, Montreal, QC, Canada., Hammamji K; Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.; Department of Ophthalmology, Université de Montréal, Montreal, QC, Canada., Ong AY; Institute of Ophthalmology, University College London, London, United Kingdom.; Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom.; Oxford Eye Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom., Rhéaume MA; Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.; Department of Ophthalmology, Université de Montréal, Montreal, QC, Canada., Gauthier D; Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.; Department of Ophthalmology, Université de Montréal, Montreal, QC, Canada., Harissi-Dagher M; Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.; Department of Ophthalmology, Université de Montréal, Montreal, QC, Canada., Keane PA; Institute of Ophthalmology, University College London, London, United Kingdom.; Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom.; NIHR Moorfields Biomedical Research Centre, London, United Kingdom., Pomp A; Health Technology Assessment Unit, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.; Department of Surgery, University of Montréal, Montreal, QC, Canada.
Jazyk:	angličtina
Zdroj:	JMIR diabetes [JMIR Diabetes] 2024 Sep 03; Vol. 9, pp. e59867. Date of Electronic Publication: 2024 Sep 03.
DOI:	10.2196/59867
Abstrakt:	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 analyzes fundus images for DR screening in a Quebec tertiary care center. 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 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. Results: 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. Conclusions: 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. (©Fares Antaki, Imane Hammana, Marie-Catherine Tessier, Andrée Boucher, Maud Laurence David Jetté, Catherine Beauchemin, Karim Hammamji, Ariel Yuhan Ong, Marc-André Rhéaume, Danny Gauthier, Mona Harissi-Dagher, Pearse A Keane, Alfons Pomp. Originally published in JMIR Diabetes (https://diabetes.jmir.org), 03.09.2024.)
Databáze:	MEDLINE
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