Cacciaguerra, Laura (57185733400)Laura (57185733400)CacciaguerraStorelli, Loredana (57188565274)Loredana (57188565274)StorelliRadaelli, Marta (25947736800)Marta (25947736800)RadaelliMesaros, Sarlota (7004307592)Sarlota (7004307592)MesarosMoiola, Lucia (57190092602)Lucia (57190092602)MoiolaDrulovic, Jelena (55886929900)Jelena (55886929900)DrulovicFilippi, Massimo (7202268530)Massimo (7202268530)FilippiRocca, Maria A. (34973365100)Maria A. (34973365100)Rocca2025-06-122025-06-122022https://doi.org/10.1007/s00415-021-10727-yhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85111602170&doi=10.1007%2fs00415-021-10727-y&partnerID=40&md5=3957531a65595ecd5420cdaf4f412938https://remedy.med.bg.ac.rs/handle/123456789/3605Objectives: To apply a deep-learning algorithm to brain MRIs of seronegative patients with neuromyelitis optica spectrum disorders (NMOSD) and NMOSD-like manifestations and assess whether their structural features are similar to aquaporin-4-seropositive NMOSD or multiple sclerosis (MS) patients. Patients and methods: We analyzed 228 T2- and T1-weighted brain MRIs acquired from aquaporin-4-seropositive NMOSD (n = 85), MS (n = 95), aquaporin-4-seronegative NMOSD [n = 11, three with anti-myelin oligodendrocyte glycoprotein antibodies (MOG)], and aquaporin-4-seronegative patients with NMOSD-like manifestations (idiopathic recurrent optic neuritis and myelitis, n = 37), who were recruited from February 2010 to December 2019. Seventy-three percent of aquaporin-4-seronegative patients with NMOSD-like manifestations also had a clinical follow-up (median duration of 4 years). The deep-learning neural network architecture was based on four 3D convolutional layers. It was trained and validated on MRI scans of aquaporin-4-seropositive NMOSD and MS patients and was then applied to aquaporin-4-seronegative NMOSD and NMOSD-like manifestations. Assignment of unclassified aquaporin-4-seronegative patients was compared with their clinical follow-up. Results: The final algorithm differentiated aquaporin-4-seropositive NMOSD and MS patients with an accuracy of 0.95. All aquaporin-4-seronegative NMOSD and 36/37 aquaporin-4-seronegative patients with NMOSD-like manifestations were classified as NMOSD. Anti-MOG patients had a similar probability of being NMOSD or MS. At clinical follow-up, one unclassified aquaporin-4-seronegative patient evolved to MS, three developed NMOSD, and the others did not change phenotype. Conclusions: Our findings support the inclusion of aquaporin4-seronegative patients into NMOSD and suggest a possible expansion to aquaporin-4-seronegative unclassified patients with NMOSD-like manifestations. Anti-MOG patients are likely to have intermediate brain features between NMOSD and MS. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.Artificial intelligenceIdiopathicMRIMultiple sclerosisNeuromyelitis optica spectrum disordersSeronegative