Home > Neurology > EAN 2022 > Multiple Sclerosis > Estimating brain age in MS: machine learning versus deep learning

Estimating brain age in MS: machine learning versus deep learning

Presented By
Dr Lars Skattebøl, Oslo University Hospital, Norway
Conference
EAN 2022
Doi
https://doi.org/10.55788/ca311818
A Norwegian study compared traditional machine learning with deep learning methods to estimate brain age in a large, longitudinal cohort of people with multiple sclerosis (MS). Both brain age models revealed significant associations with Expanded Disability Status Scale (EDSS) score and duration of disease. The authors concluded that deep learning is an ideal analytic tool to process complex imaging data. Brain age is a numerical estimate of biological age, which is calculated by combining structural T1 MRI images and information about chronological age, in conjunction with artificial intelligence-based computation [1]. Dr Lars Skattebøl (Oslo University Hospital, Norway) aimed to validate a deep learning simple fully convoluted neural network (DL-SFCN) model for brain age in MS. The main objective was to compare DL-SFCN with an established feature-based machine learning model. To this end, eligible MRI data of 1,516 MS patients was analysed [2]. Since the...


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