Evaluation of a deep learning segmentation tool to help detect spinal cord lesions from combined T2 and STIR acquisitions in people with multiple sclerosis

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Spinal cord lesions are prevalent in up to 80% of people with multiple sclerosis and are crucial for diagnosis and prognosis.
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A deep learning segmentation tool was developed to aid in detecting spinal cord lesions from T2 and STIR MRI sequences.
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The study evaluated the tool's impact on clinician sensitivity, precision, interpretation time, and inter-reader variability.
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The segmentation model achieved a lesion-wise sensitivity of 0.89 and a precision of 0.64 on a test set of 40 cases.
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The multi-reader study involved 20 clinicians analyzing MRI scans of 50 patients, assessing the tool's effectiveness in clinical practice.

European Radiology

by Baptiste Lodé, Burhan Rashid Hussein, Cédric Meurée, Ricky Walsh, Malo Gaubert, Nicolas Lassalle, Guilhem Courbon, Agathe Martin, Jeanne Le Bars, Françoise Durand-Dubief, Bertrand Bourre, Adil Maarouf, Olivier Outteryck, Clément Mehier, Alexandre Poulin, Camille Cathelineau, Jeremy Hong, Guillaume Criton, Sophie Motillon-Alonso, Augustin Lecler, Frédérique Charbonneau, Loïc Duron, Alexandre Bani-Sadr, Céline Delpierre, Jean-Christophe Ferré, Gilles Edan, François Cotton, Romain Casey, Francesca Galassi, Benoit Combès, Anne Kerbrat
April 4, 2025

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