In:
Journal of Neurology, Neurosurgery & Psychiatry, BMJ, Vol. 94, No. 1 ( 2023-01), p. 31-37
Abstract:
To evaluate the clinical significance of deep learning-derived brain age prediction in neuromyelitis optica spectrum disorder (NMOSD) relative to relapsing-remitting multiple sclerosis (RRMS). Methods This cohort study used data retrospectively collected from 6 tertiary neurological centres in China between 2009 and 2018. In total, 199 patients with NMOSD and 200 patients with RRMS were studied alongside 269 healthy controls. Clinical follow-up was available in 85 patients with NMOSD and 124 patients with RRMS (mean duration NMOSD=5.8±1.9 (1.9–9.9) years, RRMS=5.2±1.7 (1.5–9.2) years). Deep learning was used to learn ‘brain age’ from MRI scans in the healthy controls and estimate the brain age gap (BAG) in patients. Results A significantly higher BAG was found in the NMOSD (5.4±8.2 years) and RRMS (13.0±14.7 years) groups compared with healthy controls. A higher baseline disability score and advanced brain volume loss were associated with increased BAG in both patient groups. A longer disease duration was associated with increased BAG in RRMS. BAG significantly predicted Expanded Disability Status Scale worsening in patients with NMOSD and RRMS. Conclusions There is a clear BAG in NMOSD, although smaller than in RRMS. The BAG is a clinically relevant MRI marker in NMOSD and RRMS.
Type of Medium:
Online Resource
ISSN:
0022-3050
,
1468-330X
DOI:
10.1136/jnnp-2022-329680
Language:
English
Publisher:
BMJ
Publication Date:
2023
detail.hit.zdb_id:
1480429-3
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