Diabetic retinopathy screening with confocal fundus camera and artificial intelligence - assisted grading. in European journal of ophthalmology / Eur J Ophthalmol. 2024 Aug 7:11206721241272229. doi: 10.1177/11206721241272229.

2024
ASL Torino 5

Tipo pubblicazione

Journal Article

Autori/Collaboratori (9)Vedi tutti...

Nada E
Metabolism and Diabetes Unit, ASLTO 5, Regione Piemonte, Italy.
Doglio M
Metabolism and Diabetes Unit, ASLTO 5, Regione Piemonte, Italy.
Manti R
Metabolism and Diabetes Unit, ASLTO 5, Regione Piemonte, Italy.

et alii...

Abstract

PURPOSE: Screening for diabetic retinopathy (DR) by ophthalmologists is costly and labour-intensive. Artificial Intelligence (AI) for automated DR detection could be a clinically and economically alternative. We assessed the performance of a confocal fundus imaging system (DRSplus, Centervue SpA), coupled with an AI algorithm (RetCAD, Thirona B.V.) in a real-world setting. METHODS: 45° non-mydriatic retinal images from 506 patients with diabetes were graded both by an ophthalmologist and by the AI algorithm, according to the International Clinical Diabetic Retinopathy severity scale. Less than moderate retinopathy (DR scores 0, 1) was defined as non-referable, while more severe stages were defined as referable retinopathy. The gradings were then compared both at eye-level and patient-level. Key metrics included sensitivity, specificity all measured with a 95% Confidence Interval. RESULTS: The percentage of ungradable eyes according to the AI was 2.58%. The performances of the AI algorithm for detecting referable DR were 97.18% sensitivity, 93.73% specificity at eye-level and 98.70% sensitivity and 91.06% specificity at patient-level. CONCLUSIONS: DRSplus paired with RetCAD represents a reliable DR screening solution in a real-world setting. The high sensitivity of the system ensures that almost all patients requiring medical attention for DR are referred to an ophthalmologist for further evaluation.

Accesso banca dati bibliografica

PMID : 39109554

DOI : 10.1177/11206721241272229

Keywords

artificial intelligence; accuracy study; Diabetic retinopathy screening; confocal fundus camera; ophthalmologist referral;