Effect of simulated cataract on the accuracy of artificial intelligence in detecting diabetic retinopathy in color fundus photos.
Indian J Ophthalmol
; 72(Suppl 1): S42-S45, 2024 Jan 01.
Article
in En
| MEDLINE
| ID: mdl-38131541
ABSTRACT
PURPOSE:
Artificial intelligence (AI) is often trained on images without ocular co-morbidities, limiting its generalizability. This study aims to evaluate the accuracy of a convolutional neural network (CNN) applied to color fundus photos (CFPs) with simulated cataracts (SCs) in detecting diabetic retinopathy (DR).METHODS:
A database of 3662 CFPs (from Asia Pacific Tele-Ophthalmology Society (APTOS) 2019) was used. Using transfer learning, a CNN was trained to classify the training images as either DR or non-DR. The CNN was then applied to classify the testing images after an SC was applied, using varying degrees of Gaussian blur.RESULTS:
Accuracy without SC was 97.0%, sensitivity (Sn) 95.7%, specificity (Sp) 98.3%. For mild SC, accuracy was 93.1%, Sn 91.8%, Sp 94.3%. For moderate SC, accuracy was 62.8%, Sn 31.4%, Sp 95.2%. For severe SC, accuracy was 53.5%, Sn 11.8%, Sp 96.5%.CONCLUSION:
SCs significantly impaired AI accuracy. To prepare AI for clinical use, cataracts and other real-world clinical challenges affecting image quality must be accounted for.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Cataract
/
Diabetes Mellitus
/
Diabetic Retinopathy
Limits:
Humans
Language:
En
Journal:
Indian J Ophthalmol
Year:
2024
Document type:
Article
Affiliation country:
Estados Unidos
Country of publication:
India