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In Journal of neurology, neurosurgery, and psychiatry

OBJECTIVE : 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.

Wei Ren, Xu Xiaolu, Duan Yunyun, Zhang Ningnannan, Sun Jie, Li Haiqing, Li Yuxin, Li Yongmei, Zeng Chun, Han Xuemei, Zhou Fuqing, Huang Muhua, Li Runzhi, Zhuo Zhizheng, Barkhof Frederik, H Cole James, Liu Yaou


MRI, multiple sclerosis, neuroimmunology, neuroradiology