Longitudinal automated assessment of paramagnetic rim lesions in multiple sclerosis using RimNet
(2021) ISMRM — Location: En ligne (15.May.2021)
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- Abstract
- The automatic assessment of paramagnetic rim lesions in multiple sclerosis is important, and a deep learning-based algorithm called RimNet has recently been proposed. This work evaluates the generalizability of RimNet and its longitudinal performance on MRI data acquired at different clini-cal centers. We found that RimNet’s performance was nearly as good on totally unseen data as in the original paper (receiver-operating-characteristic area-under-the-curve (AUC) 0.88 vs. 0.94, pre-cision-recall AUC 0.69 vs. 0.70), and it made consistent predictions on longitudinal data (binary con-sistency 82%, probability consistency 93%).
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Citations
Wynen, M., & et al. (2021). Longitudinal automated assessment of paramagnetic rim lesions in multiple sclerosis using RimNet. https://hdl.handle.net/2078.5/213723