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Abstract #1300

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

Federico Spagnolo1,2,3,4, Aarushi Bhardwaj1,5, Pedro M. Gordaliza6,7, Po-Jui Lu1,2,3, Mario Ocampo-Pineda1,2,3, Meritxell Bach Cuadra6,7, Xinjie Chen1,2,3, Batuhan Ayci1,8, Alessandro Cagol1,2,3,9, Vincent Andrearczyk4, Adrien Depeursinge4,10, and Cristina Granziera1,2,3
1Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland, 2Department of Neurology, University Hospital Basel, Basel, Switzerland, 3Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland, 4MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland, 5University of Guelph, Guelph, ON, Canada, 6CIBM Center for Biomedical Imaging, Lausanne, Switzerland, 7Radiology Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland, 8Istanbul University-Cerrahpasa, Cerrahpasa Medical School, Istanbul, Turkey, 9Dipartimento di Scienze della Salute, Università degli Studi di Genova, Genova, Italy, 10Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland

Synopsis

Keywords: AI/ML Software, AI/ML Software, Paramagnetic Rim Lesion

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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Abstract

Keywords