Cross-modal deep learning to predict multiple sclerosis cervical spinal cord damage with digital monitoring of motor function

Motivation: People with multiple sclerosis (PwMS) might suffer from cervical spinal cord atrophy, which can only be assessed through regular MRI follow-up. Goal(s): Our goal was to explore using deep learning if the smartphone sensor signal can capture structural changes on MRI in PwMS' cervical spinal cord. Approach: MPRAGE-based atrophy measures and ten-second signal segments of 166 PwMS were derived. PwMS were split for five-fold cross-validation. A neural network was adapted from a ResNet pretrained on the UK-Biobank sensor dataset. Segment predictions were grouped for the prediction on PwMS. Results: For the PwMS prediction, the NN achieved sensitivity=0.74, specificity=0.81, and F1=0.80. Impact: The performance of the proposed cross-modal neural network indicates that smartphone walking tests correlate with structural changes in cervical spinal cord and might assist clinical assessments in multiple sclerosis.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/5029
Primary Topic
Medical Imaging and Analysis
Type
article
Field-Weighted Citation Impact
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article

Cross-modal deep learning to predict multiple sclerosis cervical spinal cord damage with digital monitoring of motor function

Federico Spagnolo, Alessandro Cagol, Johannes Lorscheider, Óscar Reyes et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Medical Imaging and Analysis
article

Cross-modal deep learning to predict multiple sclerosis cervical spinal cord damage with digital monitoring of motor function

Federico Spagnolo, Alessandro Cagol, Johannes Lorscheider, Óscar Reyes, Jens Kühle, Po‐Jui Lu, Ludwig Kappos, Matthias Weigel, Mario Ocampo‐Pineda, Tim Woelfle, Cristina Granziera, Lester Melie‐García
article en

Abstract

Motivation: People with multiple sclerosis (PwMS) might suffer from cervical spinal cord atrophy, which can only be assessed through regular MRI follow-up. Goal(s): Our goal was to explore using deep learning if the smartphone sensor signal can capture structural changes on MRI in PwMS' cervical spinal cord. Approach: MPRAGE-based atrophy measures and ten-second signal segments of 166 PwMS were derived. PwMS were split for five-fold cross-validation. A neural network was adapted from a ResNet pretrained on the UK-Biobank sensor dataset. Segment predictions were grouped for the prediction on PwMS. Results: For the PwMS prediction, the NN achieved sensitivity=0.74, specificity=0.81, and F1=0.80. Impact: The performance of the proposed cross-modal neural network indicates that smartphone walking tests correlate with structural changes in cervical spinal cord and might assist clinical assessments in multiple sclerosis.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 76%
Medical Imaging and Analysis
DOISourceOpenAlex
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