Assessment of Postoperative Retinal Complications and the Possibility of Optical Diagnostics Using Support Vector Machine

Evaluación de Complicaciones Retinianas Postoperatorias y Posibilidad de Diagnóstico Óptico Mediante Máquina de Vectores de Soporte
Inkarbekov M. Kulmaganbetov M. Bazarbekova G. Baiyrkhanova A.
1 January 2024 AG Editor (Argentina)

Salud, Ciencia y Tecnologia
2024 #4

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. Method: 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.

Cataract , Machine Learning , Optical Coherence Tomography , Support Vector Machine , Surgery

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Kazakh-Russian Medical University, Ophthalmology Department, Almaty, Kazakhstan
Al-Farabi Kazakh National University, Department of Public Health, Almaty, Kazakhstan
Kazakh Eye Research Institute, Glaucoma Department, Almaty, Kazakhstan
Centre for Eye and Vision Research (CEVR), Quantum Optics Lab., China
Kazakh-Russian Medical University, Department of Oncology with Radiology Course, Almaty, Kazakhstan
Asfendiyarov Kazakh National Medical University, Department of Health Policy and Management, Almaty, Kazakhstan
Yassawi International Kazakh-Turkish University, Department of Medicine, Turkestan, Kazakhstan

Kazakh-Russian Medical University
Al-Farabi Kazakh National University
Kazakh Eye Research Institute
Centre for Eye and Vision Research (CEVR)
Kazakh-Russian Medical University
Asfendiyarov Kazakh National Medical University
Yassawi International Kazakh-Turkish University

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