In Ophthalmology science
PURPOSE : To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis.
DESIGN : Cross-sectional analysis of a retinal image dataset from a previous prospective OCTA study (ClinicalTrials.govNCT03422965).
PARTICIPANTS : Patients with type 1 DM and controls included in the progenitor study.
METHODS : Radiomic features were extracted from fundus retinographies, OCT, and OCTA images in each study eye. Logistic regression, linear discriminant analysis, support vector classifier (SVC)-linear, SVC-radial basis function, and random forest models were created to evaluate their diagnostic accuracy for DM, DR, and R-DR diagnosis in all image types.
MAIN OUTCOME MEASURES : Area under the receiver operating characteristic curve (AUC) mean and standard deviation for each ML model and each individual and combined image types.
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 3 × 3 mm superficial capillary plexus OCTA scan (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 DM, DR and R-DR diagnosis. The performance of the models was similar in unilateral and bilateral eyes image datasets.
CONCLUSIONS : Radiomics extracted from OCT and OCTA images allow identification of patients with DM, DR, and R-DR 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 DR screening in patients with type 1 DM.
FINANCIAL DISCLOSURES : Proprietary or commercial disclosure may be found after the references.
Carrera-Escalé Laura, Benali Anass, Rathert Ann-Christin, Martín-Pinardel Ruben, Bernal-Morales Carolina, Alé-Chilet Anibal, Barraso Marina, Marín-Martinez Sara, Feu-Basilio Silvia, Rosinés-Fonoll Josep, Hernandez Teresa, Vilá Irene, Castro-Dominguez Rafael, Oliva Cristian, Vinagre Irene, Ortega Emilio, Gimenez Marga, Vellido Alfredo, Romero Enrique, Zarranz-Ventura Javier
2023-Jun
AI, artificial intelligence, AUC, area under the curve, Artificial intelligence, DCP, deep capillary plexus, DM, diabetes mellitus, DR, diabetic retinopathy, Diabetic retinopathy, FR, fundus retinographies, LDA, linear discriminant analysis, LR, logistic regression, ML, machine learning, Machine learning, OCT angiography, OCTA, OCT angiography, R-DR, referable DR, RF, random forest, Radiomics, SCP, superficial capillary plexus, SVC, support vector classifier, rbf, radial basis function
