Abstract
Introduction: Cataract is a prevalent eye condition that affects millions of individuals worldwide, leading to visual impairment and reduced quality of life. Cataract surgery is the most effective treatment, but post-surgical complications can arise, impacting the success of the intervention. Optical coherence tomography (OCT) has emerged as a valuable imaging technique for evaluating these complications, but the manual interpretation of OCT images is time-consuming and subjective.Objective: In this study, we aimed to assess the performance of a machine learning (ML) tool specifically developed for detecting post-surgical complications in cataract patients.Methods: We employed a support vector machine (SVM) algorithm to analyze a comprehensive dataset of OCT images. The dataset comprised 700 OCT images obtained post-surgery, including patients with cystoid macular oedema (CMO), retinal detachment (RD), and healthy individuals. The ML tool utilized pre-processed OCT images with annotations provided by expert ophthalmologists, undergoing retinal layer segmentation using intensity-based features.Results: The SVM algorithm demonstrated high sensitivity and specificity in detecting and classifying post-surgical complications. It achieved a sensitivity of 92.5% in detecting CMO and 90.9% in identifying RD. The specificity of the algorithm was 90.9% and 96.2% for these complications, respectively. The overall accuracy of the ML tool in correctly identifying and classifying post-surgical complications was 92%.Conclusions: The integration of ML algorithms in OCT imaging shows promise in enhancing the accuracy and efficiency of assessing post-surgical complications in cataract patients. The ML tool developed in this study provides reliable and objective assessments, reducing the subjectivity and variability associated with the manual interpretation of OCT images