What type of study is this?

This is a Quantitative Study study.

September 17, 2025

Deep-PRL: a deep learning network for the identification of paramagnetic rim lesions in multiple sclerosis

FSFederico SpagnoloUniversity of Basel ABAshutosh BhardwajUniversity of Delhi PGPedro M. GordalizaUniversity Hospital of Lausanne

Key Points

The approach achieved a mean F1 score of 0.860, indicating high accuracy in identifying paramagnetic rim lesions.
AI-based analysis outperformed traditional methods, showing an AUC of 0.982 in diagnostic performance.
Using a nested cross-validation method ensured robust model training and evaluation among 185 multiple sclerosis patients.
Integrating AI tools could significantly improve clinical management and diagnostic efficiency for multiple sclerosis patients.

Abstract

Motivation: PRLs are an important diagnostic biomarker in people with multiple sclerosis (pwMS). Their identification on MRI is time-consuming and subject to high inter-rater variability. However, the use of AI could support this identification process. Goal(s): We leverage multi-contrast MRI to improve the identification of PRLs. Approach: Deep-PRL is an attention-based CNN, fusing features of T1-w and unwrapped phase images from 185 pwMS. The approach consists in a nested cross-validation with patient stratification. Results: The test performance outperformed state-of-the-art methods, achieving a mean F1 score of 0.860 ± 0.048 and an AUC of 0.982 ± 0.007. Impact: These results represent a significant step towards the integration of an AI tool to assist clinicians in the identification of PRLs, thereby improving the clinical management of pwMS.

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