Utilizing Long Short-Term Memory for Detecting Multiple Sclerosis Based on Vessel Analysis

Background: multiple sclerosis (ms) is a chronic immune-mediated disease affecting the central nervous system, leading to various disturbances, including visual impairment. early and accurate diagnosis of ms is critical for effective treatment and management. scanning laser ophthalmoscopy (slo) is a non-invasive technique that provides high-quality retinal images, serving as a promising resource for the early detection of ms. this research investigates a vessel-based approach for ms detection in slo images using long short-term memory (lstm) networks. material and methods: a total of 106 healthy controls (hcs) and 39 ms patients (78 eyes) were enrolled. after implementing quality control measures and removing poor-quality or damaged images, the research utilized a total of 265 photos (73 ms and 192 hc). an approach for the early detection of ms in slo images using lstm network is introduced. this approach involves two steps: 1.it involves preprocessing and extracting vessels and then pretraining a deep neural network using the source dataset, and 2. tuning the network on the target dataset of slo images. the significance of vessel segmentation in ms detection is examined, and the application of the proposed method in improving diagnostic models is explored. the proposed approach achieves an accuracy rate of 97.44% when evaluated on a test dataset consisting of slo pictures. through experiments on slo datasets and employing the proposed vessel-based approach with lstm, empirical results demonstrate that this approach contributes to the early detection of ms with high accuracy. these models exhibit the capability to accurately detect the disease with high precision and appropriate sensitivity.