Higher effect sizes for the detection of accelerated brain volume loss and disability progression in multiple sclerosis using deep-learning
BrainLossNet can provide added value in clinical routine and MS therapy trials regarding the detection of accelerated BVL in MS and the differentiation between MS patients with versus without disability progression.
Authors
- Carola Gocke
- Julia Krüger (ORCID: https://orcid.org/0000-0001-9963-4588)
- Lothar Spies (ORCID: https://orcid.org/0000-0002-5012-9406)
- Roland Opfer (ORCID: https://orcid.org/0000-0002-9911-5478)
- Ralph Buchert (ORCID: https://orcid.org/0000-0002-0945-0724)
- Thomas Buddenkotte (ORCID: https://orcid.org/0000-0003-4872-4246)
- Matthias Schwab
- Tjalf Ziemssen (ORCID: https://orcid.org/0000-0001-8799-8202)
Institutions
- Universität Hamburg (DE)
- MSH Medical School Hamburg – University of Applied Sciences and Medical University (DE)
- University Medical Center Hamburg-Eppendorf (DE)
- Jena University Hospital (DE)
- University Hospital Carl Gustav Carus (DE)
- Technische Universität Dresden (DE)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2024-10-18
- DOI
- https://doi.org/10.1016/j.compbiomed.2024.109289
- Citations
- 2
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.49