Diagnosing Multiple Sclerosis From Magnetic Resonance Imaging Images: Highlights From the Second Isfahan Artificial Intelligence Event 2024 Publisher



Davanian F ; Adibi I ; Tajmirriahi M ; Rejali M ; Heyrati MP ; Ghasemi M ; Alavi SH ; Dizaji KM ; Rad MH ; Ghorbanali Z ; Hajiabadi M ; Sedaghi A ; Khaniki ER ; Sabzevari H Show All Authors
Source: Journal of Medical Signals and Sensors Published:2026

Abstract

Background: Multiple sclerosis (MS) is an autoimmune disease of the central nervous system which is the main reason of disabilities of young adults. MS occurs when the immune system attacks the central nervous system and destroys the myelin sheaths of neurons. Loss of myelin sheaths results in appearing several lesions in different parts of the brain. The place and amount of lesions are important criteria for determining the level and progression of the disease. These parameters are usually determined manually by an expert which can be time-consuming and inaccurate. Methods: Considering the effectiveness of artificial intelligence (AI)-based methods in diagnosing and predicting different diseases, and the increasing need for driving new and effective diagnostic methods, this challenge, entitled “Diagnosing MS from magnetic resonance imaging (MRI) Images,” has been organized by Isfahan Province Elites Foundation in collaboration with Medical Image and Signal Processing Research Center of Isfahan University of Medical Sciences, as a part of Isfahan AI 2024 event, held in October 2024 in Isfahan, Iran. The challenge has been dedicated to find new AI-based methods for the segmentation and localization of lesions in MRI images of patients with MS. The challenge had three steps, where in the first and second steps, the teams received the train and test datasets, respectively. Finally, the selected teams were invited to the last round of the competition, held in person, and received the last test dataset. Results: Based on the received results, the best achieved dice score was 0.33, best sensitivity was 0.349, best precision was 0.3, and the lowest centroid distance was 53.025. In addition, the best accuracy for lesion detection in periventricular, deep white matter, juxtacortical, and infratentorial parts of the brain was 80.282%, 74%, 63.492%, and 62.5%, respectively. Conclusion: Several methods, mostly based on deep learning, have been submitted. The results show that AI has the ability for the segmentation and localization of lesions. However, the received results are still far from the desired accuracy, which shows a need for further improvement and studies in this field. © 2026 Journal of Medical Signals & Sensors.