Abstract
AbstractBackgroundDiabetic retinopathy is a leading cause of blindness in adults worldwide. AI with autonomous deep learning algorithms has been increasingly used in the analysis of retinal images particularly for the screening of referrable DR. An established treatment for proliferative DR is pan-retinal or focal laser photocoagulation. Training AI autonomous models to discern laser patterns can be important in disease management and follow-up.MethodsA deep learning model was trained for laser treatment detection using the EyePACs dataset. Data was randomly assigned, by participant, into development (n= 18,945) and validation (n= 2,105) sets. Analysis was conducted at the single image, eye, and patient levels. The model was then used to filter input images for three independent AI models for various retinal indications, and changes in model efficacy were measured using AUC and MAE.FindingsOn the task of laser photocoagulation detection: AUC of 0.981 (CI 95% 0.971-0.87) was achieved at the patient level. AUC of 0.950 (CI 95% 0.943-0.956) was achieved at the image level. AUC of 0.979 (CI 95% 0.972-0.984) was achieved at the eye level.When analyzing independent AI models, efficacy was shown to improve across the board on images of untreated eyes. DME detection on images with artifacts was AUC 0.932 (CI 95% 0.905-0.951) vs. AUC 0.955 (CI 95% 0.948-0.961) on those without. Participant sex detection on images with artifacts was AUC 0.872 (CI 95% 0.830-0.903) compared to AUC 0.922 (CI 95% 0.916-0.927) on those without. Participant age detection on images with artifacts was MAE 5.33 vs. MAE 3.81 on those without.InterpretationThe proposed model for laser treatment detection achieved high performance on all analysis metrics and has been demonstrated to positively affect the efficacy of different AI models, suggesting that laser detection can generally improve AI powered applications for fundus images.FundingProvided by AEYE Health Inc.
Publisher
Cold Spring Harbor Laboratory