On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images

Abstract

Optical coherence tomography (OCT) is a noninvasive imaging technique that provides high-resolution cross-sectional retina images, enabling ophthalmologists to gather crucial information for diagnosing various retinal diseases. Despite its benefits, manual analysis of OCT images is time-consuming and heavily dependent on the personal experience of the analyst. This paper focuses on using machine learning to analyse OCT images in the clinical interpretation of retinal diseases. The complexity of understanding the biomarkers present in OCT images has been a challenge for many researchers, particularly those from nonclinical disciplines. This paper aims to provide an overview of the current state-of-the-art OCT image processing techniques, including image denoising and layer segmentation. It also highlights the potential of machine learning algorithms to automate the analysis of OCT images, reducing time consumption and improving diagnostic accuracy. Using machine learning in OCT image analysis can mitigate the limitations of manual analysis methods and provide a more reliable and objective approach to diagnosing retinal diseases. This paper will be of interest to ophthalmologists, researchers, and data scientists working in the field of retinal disease diagnosis and machine learning. By presenting the latest advancements in OCT image analysis using machine learning, this paper will contribute to the ongoing efforts to improve the diagnostic accuracy of retinal diseases.

Advancing Ocular Imaging: A Hybrid Attention Mechanism-Based U-Net Model for Precise Segmentation of Sub-Retinal Layers in OCT Images

“…The PR comprises rods and cones responsible for converting light into electrical signals that the brain can interpret. The NFL contains the axons of the ganglion cells, which transmit visual information from the retina to the brain [ 2 ]. The illustrative diagram of a healthy retina and OCT is given in Figure 1 .…”

“…Another avenue for improvement involves incorporating multi-modal data for CNN training. Beyond OCT images, complementary information from diverse medical images, such as fundus photographs or fluorescein angiography images, enhances CNN performance and bolsters the model’s overall robustness [ 2 ].…”

Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture

“…Retinal image diagnosis has shown an increased interest recently from various research groups. A large volume of research work has shown promising results in improving the accuracy and efficiency of OCT-based image analysis [4]. The accuracy of OCT image classification has shown considerable promise when using machine learning (ML) techniques.…”