Radiomics-based assessment of Fundus Retinography (FR), optical coherence tomography (OCT) and OCT angiography (OCTA) images for Diabetes Mellitus and Diabetic Retinopathy diagnosis

Abstract

Purpose : To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from fundus retinography (FR), optical coherence tomography (OCT) and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR) and referable DR (R-DR) diagnosis, in a dataset from a previous prospective OCTA study (ClinicalTrials.gov NCT03422965).

Methods : Radiomic features were extracted from FR, OCT and OCTA images in each study eye. Logistic regression (LR), linear discriminant analysis (LDA), support vector classifier (SVC)-linear, SVC-rbf and random forest (RF) models were created to evaluate their diagnostic accuracy for DM, DR and R-DR diagnosis in all images type. Models performance was described by their Area-under-Curve (AUC) mean and standard deviation (SD). Box-plots were generated to represent the mean performance of the models.

Results : A dataset of 726 eyes (439 individuals) were included. For DM diagnosis, the greatest AUC was observed for OCT (0.82, 0.03). For DR detection the greatest AUC was observed for OCTA (0.77, 0.03), especially in the 3x3 mm superficial capillary plexus OCTA scan (AUC 0.76, 0.04). For R-DR diagnosis, the greatest AUC was observed for OCTA (0.87, 0.12) and the deep capillary plexus OCTA scan (0.86, 0.08). The addition of clinical variables (age, sex, etc.) improved most models AUC for both DM and DR diagnosis.

Conclusions : Radiomics extracted from FR, OCT and OCTA images allow identification of DM, DR and R-DR patients using standard ML classifiers. OCT was the best test for DM diagnosis, OCTA for DR and R-DR diagnosis and the addition of clinical variables improved most models. This pioneer study demonstrates that radiomics-based ML techniques applied to OCT and OCTA images may be an option for screening in the community.

This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.

 

Receiver operating characteristic (ROC) curves of models performance for Diabetes Mellitus (DM), Diabetic Retinopathy (DR) and Referable DR (R-DR) diagnosis. Left column: ROC curves for DM diagnosis detailed by models. Central column: ROC curves for DR diagnosis detailed by models. Right column: ROC curves for R-DR diagnosis detailed by models. Top row: example of a linear model (Linear discriminant analysis, LDA). Bottom row: example of a non-linear model (Support Vector Classifier – RBF).