Neuromyelitis Optica and Anti-MOG Disease Oral Presentation

PS16.05 - Application of deep-learning to NMOSD and unclassified seronegative patients

  • L. Cacciaguerra
  • L. Storelli
  • M. Radaelli
  • S. Mesaros
  • L. Moiola
  • J. Drulovic
  • M. Rocca
  • M. Filippi
Presentation Number
Presentation Topic
Neuromyelitis Optica and Anti-MOG Disease
Lecture Time
13:39 - 13:51



Current diagnostic criteria of neuromyelitis optica spectrum disorders (NMOSD) allow the diagnosis of aquaporin-4 (AQP4) seropositive patients with limited manifestations, whereas seronegative patients with limited phenotypes remain unclassified and are usually considered as prodromal phases of multiple sclerosis (MS) or different entities themselves. Nowadays, there is great effort to perform an automatic diagnosis of different neurological diseases using deep-learning-based imaging diagnostics, which is a form of artificial intelligence, allowing predicting or making decisions without a priori human intervention.


To provide a deep-learning classification of NMOSD patients with different serological profiles and to compare these results with their clinical evolution.


228 T2- and T1-weighted brain MRIs were acquired from patients with AQP4-seropositive NMOSD (n=85), early MS (n=95), AQP4-seronegative NMOSD (n=11, 3 with anti-myelin oligodendrocyte glycoprotein antibodies) and unclassified double-seronegative limited phenotypes (n=17 idiopathic recurrent optic neuritis [IRON], n=20 idiopathic recurrent myelitis [IRM]). The latter had a clinical re-evaluation after 4-year follow-up. The neural network architecture was based on four 3D convolutional layers. It was trained and validated on MRI scans (n=180) from AQP4-seropositive NMOSD and MS patients. Then, it was applied to AQP4-seronegative NMOSD and double-seronegative patients with limited phenotypes to evaluate their classification as NMOSD or MS in comparison with their clinical follow-up.


The final algorithm discriminated between AQP-4-seropositive NMOSD and MS with an accuracy of 0.95. Forty-seven/48 (97.9%) seronegative patients were classified as NMOSD (one patient with IRON was classified as MS). Clinical follow-up was available in 27/37 (73%) double-seronegative limited phenotypes: one patient evolved to MS, three developed NMOSD and the others did not change phenotype.


Deep-learning may help in the diagnostic work-up of NMOSD. Our findings support the inclusion of AQP4-seronegative patients to the spectrum of NMO and suggest its enlargement to double-seronegative limited phenotypes.