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Abstract #1374

AI-driven segmentation of the anterior visual pathway from high-resolution MR images: development and clinical validation in MS patients.

Andrea Diociasi1, Emanuele Pravatà2,3, Luca Carmisciano4, Oliver C. Kiersnowski1, Luca Roccatagliata1,5, and Andrea Chincarini6
1IRCCS Ospedale Policlinico San Martino, Genova, Italy, 2Department of Neuroscience, Imaging and Clinical Sciences, "G. D'Annunzio" University of Chieti-Pescara, Chieti, Italy, 3Neurocenter of Southern Switzerland, Lugano, Switzerland, 4Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy, 5Department of Health Sciences (DISSAL), University of Genoa, Genova, Italy, 6National Institute of Nuclear Physics (INFN), Genova, Italy

Synopsis

Keywords: Diagnosis/Prediction, Neuroinflammation

Motivation: Anterior Visual Pathway (aVP) abnormalities are linked to various etiologies, however the current lack of a standardized and automated framework renders analysis laborious and inconsistent.

Goal(s): To develop an AI-driven tool for automated aVP segmentation, and clinically validate in healthy controls (HC) and multiple sclerosis (MS) patients.

Approach: 0.6 mm isotropic 3D constructive interference steady-state images from 40 HC and 49 MS patients were segmented using a high-resolution 3D V-Net model.

Results: Segmentations’ spatial similarity between AI and both neuroradiologists was good (average DSC 0.8, CI 95% CI 0.77 – 0.83). Both AI and neuroradiologists’ results could discriminate between HC and MS.

Impact: This study demonstrates the clinical potential of AI-driven segmentation, enhancing the efficiency and preserving the accuracy, of MRI-based structural integrity of the anterior visual pathway in multiple sclerosis, thus paving the way for more consistent and reliable diagnostic workflows.

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