Comparison of Manual vs Artificial Intelligence–Based Muscle MRI Segmentation for Evaluating Disease Progression in Patients With CMT1A
qMRI combined with AI-based segmentation can be considered as a process ready for assessing longitudinal FF changes in CMT1A patients. Given the slow FF progression at a thigh level and the large heterogeneity between muscles and individuals, FF should be quantified from a 3D volume at the leg level for longitudinal analyses. A QC performed after the AI-based segmentation is still advised given the increased SRM value.
Authors
- David Bendahan (ORCID: https://orcid.org/0000-0002-1502-0958)
- Marc‐Emmanuel Bellemare (ORCID: https://orcid.org/0000-0001-9913-3097)
- Étienne Fortanier (ORCID: https://orcid.org/0000-0003-2797-9389)
- Constance Michel (ORCID: https://orcid.org/0000-0001-8826-8797)
- Émilien Delmont (ORCID: https://orcid.org/0000-0002-5591-2774)
- Marc‐Adrien Hostin (ORCID: https://orcid.org/0000-0003-3649-2054)
- Shahram Attarian (ORCID: https://orcid.org/0000-0002-7211-4694)
- Maxime Guye (ORCID: https://orcid.org/0000-0002-4435-2257)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- Aix-Marseille Université (FR)
- Laboratoire d’Informatique et Systèmes (FR)
Publication Details
- Journal
- Neurology
- Published
- 2024-10-24
- DOI
- https://doi.org/10.1212/wnl.0000000000210013
- Citations
- 4
- Primary Topic
- Hereditary Neurological Disorders
- Type
- article
- Field-Weighted Citation Impact
- 1.07