Quantitative muscle <scp>MRI</scp> combined with <scp>AI</scp>‐based segmentation as a follow‐up biomarker for <scp>ATTRv</scp> patients: A longitudinal pilot study
AI-based CNN network segmentation combined with qMRI can be used to obtain quantitative metrics for longitudinal studies in ATTRv patients. Global FF at the leg level seems to be the most sensitive MRI biomarker to track disease progression in a 1-year period. Larger studies with treatment-specific groups will now be necessary to determine the place of qMRI markers compared to the current clinical and electrophysiological scores.
Authors
- Annie Verschueren (ORCID: https://orcid.org/0000-0003-1266-9062)
- 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)
- Centre de Résonance Magnétique Biologique et Médicale (FR)
Publication Details
- Journal
- European Journal of Neurology
- Published
- 2024-11-27
- DOI
- https://doi.org/10.1111/ene.16574
- Citations
- 2
- Primary Topic
- Amyloidosis: Diagnosis, Treatment, Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.47