A double-blinded study for quantifiable assessment of the diagnostic accuracy of AI tool "ADVEN-i" in identifying diseased fundus images including diabetic retinopathy on a retrospective data.
Indian J Ophthalmol 2024;
72:S46-S52. [PMID:
38131542 PMCID:
PMC10833153 DOI:
10.4103/ijo.ijo_3342_22]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 04/15/2023] [Accepted: 07/28/2023] [Indexed: 12/23/2023] Open
Abstract
PURPOSE
To quantifiably assess the diagnostic accuracy of Adven-I, a proprietary artificial intelligence (AI)-driven diagnostic system that automatically detects diseases from fundus images. The purpose is to quantify the performance of Adven-i in differentiating a nonreferable (within normal limits) image from a referable (diseased fundus) image and further segregating diabetic retinopathy (DR) from the rest of the abnormalities (non-DR) encompassing the wide spectrum of abnormal pathologies. The assessment is carried out in comparison to manual reading as the reference gold standard. Adven-i is the only AI system classifying retinal abnormalities into DR and non-DR classes separately, apart from predicting nonreferable fundus, while most existing systems classify fundus images into referable and nonreferable DR.
METHODS
The double-blinded study was conducted on retrospective data collected over the course of a year in the ophthalmology outpatient department (OPD) at a top Tier II eyecare hospital in Chandigarh, India. Three vitreoretina specialists who were blinded to one another read the images. The ground-truth was generated on the basis of majority agreement among the readers. An arbitrator's decision was regarded final if all three readers disagreed.
RESULTS
2261 fundus images were analyzed by Adven-i. The sensitivity and specificity of Adven-i in diagnosing images with abnormalities were 95.12% and 85.77%, respectively, and for segregating DR from rest of the retinal abnormalities were 91.87% and 85.12%, respectively.
CONCLUSIONS AND RELEVANCE
Adven-i shows definite promise in automated screening for early diagnosis of referable fundus images including DR. Adven-i can be adopted to scale for mass screening in resource-limited settings.
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