Publication:
Application of deep-learning to the seronegative side of the NMO spectrum

dc.contributor.authorCacciaguerra, Laura (57185733400)
dc.contributor.authorStorelli, Loredana (57188565274)
dc.contributor.authorRadaelli, Marta (25947736800)
dc.contributor.authorMesaros, Sarlota (7004307592)
dc.contributor.authorMoiola, Lucia (57190092602)
dc.contributor.authorDrulovic, Jelena (55886929900)
dc.contributor.authorFilippi, Massimo (7202268530)
dc.contributor.authorRocca, Maria A. (34973365100)
dc.date.accessioned2025-07-02T11:59:28Z
dc.date.available2025-07-02T11:59:28Z
dc.date.issued2022
dc.description.abstractObjectives: 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.
dc.identifier.urihttps://doi.org/10.1007/s00415-021-10727-y
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85111602170&doi=10.1007%2fs00415-021-10727-y&partnerID=40&md5=3957531a65595ecd5420cdaf4f412938
dc.identifier.urihttps://remedy.med.bg.ac.rs/handle/123456789/12105
dc.subjectArtificial intelligence
dc.subjectIdiopathic
dc.subjectMRI
dc.subjectMultiple sclerosis
dc.subjectNeuromyelitis optica spectrum disorders
dc.subjectSeronegative
dc.titleApplication of deep-learning to the seronegative side of the NMO spectrum
dspace.entity.typePublication

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