The Use of Artificial Intelligence in the Evaluation of Multiple Sclerosis Brain Lesions Through the Processing of MRI Images

This is the authors' abstract. We don't add key points for this paper.

Introduction:In recent years, artificial intelligence (AI) techniques are rapidly becoming clinical practices such as diagnosis and prognosis processes, treatment effectiveness evaluation, and disease monitoring.Previous studies have shown interesting results regarding the diagnostic efficiency of artificial intelligence methods in differentiating patients with multiple sclerosis (MS) from healthy individuals or other demyelinating diseases.There is a lack of a comprehensive systematic review on the role of AI in MS diagnosis.Our aim was to conduct a systematic review to document the performance of artificial intelligence in MS diagnosis.In this study, we conducted a comprehensive and systematic search using the PubMed database.All original studies that focused on deep learning or artificial intelligence to analyze methods aimed at diagnosing MS using MRI images were included in our study.Materials and Methods: For this review, we searched PubMed for studies on the application of artificial intelligence in MS using MRI images published in English during the period 2010-2023.The search strategy was based on the words Mesh and their combinations.All studies were reviewed, but only the most relevant ones were used in this review.Results: Artificial intelligence, using deep learning methods, can predict the incidence of MS and its complications based on the risk factors of the disease and reduces the cost and time spent for various medical tests.Artificial intelligence makes this possible by extracting information and performing the necessary processing using methods such as CNN.Conclusion: MS diagnosis based on new markers and artificial intelligence is a growing field of research with MRI images.All these results show that with advances in artificial intelligence, the way MS patients are monitored and diagnosed can change.However, several challenges remain, including better understanding of information selected by AI algorithms, appropriate multicenter and longitudinal validation of results, and practical aspects related to hardware and software integration.In general, the critical importance of human supervision to optimize and fully utilize the potential of artificial intelligence approaches cannot be ignored.