This systematic review and meta-analysis seeks to assess the diagnostic accuracy of artificial intelligence (AI) algorithms in the early detection of multiple sclerosis (MS) and neuromyelitis optica (NMO) utilizing neuroimaging data.

We conducted a comprehensive search of the PubMed, MEDLINE, and Embase databases for research published between 2015 and 2024 that used AI algorithms to analyze neuroimaging data for early detection of MS and NMO. A total of 28 studies were included, encompassing 4,350 patients (2,750 with MS, 1,100 with NMO, and 500 healthy controls) with an age range of 18 to 65 years. Neuroimaging modalities investigated included MRI, diffusion tensor imaging , and optical coherence tomography , alongside AI models such as convolutional neural networks , support vector machines, and random forest methods. 

AI-driven neuroimaging analysis has enormous potential for the early diagnosis of MS and NMO, with high sensitivity and specificity. CNN models applied to MRI data produced the most accurate results. These results suggest that AI might be a valuable tool in clinical practice for improving early identification and treatment of MS and NMO.