AI-based model for T1-weighted brain MRI diagnoses Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is an incurable deadly motor neuron disease that causes the gradual deterioration of nerve cells in the spinal cord and brain. It impacts voluntary limb control and can result in breathing impairment. ALS diagnosis is often challenging due to its symptoms overlapping with other medical conditions and many tests must be performed to rule out other conditions, as easily identifiable biomarkers are still lacking. In this study, we explore T1-weighted (T1w) brain Magnetic Resonance Imaging (MRI), a non-invasive neuroimaging approach which has shown to be a reliable biomarker in many medical fields. Nonetheless, current literature on ALS diagnosis fails to retrieve evidence on how to identify biomarkers from T1w MRI. In this paper, we leverage Artificial Intelligence (AI) methods to unveil the unexplored potential of T1w brain MRI for distinguishing ALS patients from those who have similar symptoms but different diseases (mimicking). We consider a retrospective single-center dataset of brain T1-weighted MRIs collected from 2010 to 2018 recruited from the Piemonte and Valle dAosta ALS register (PARALS). The collection includes 548 patients with ALS and 106 with mimicking diseases. Our goal is to develop and validate a ML diagnostic model based exclusively on T1w MRI distinguishing the two classes. First, we extract a set of radiomic features and two sets of Deep Learning (DL)-based features from MRI scans. Then, using each representation, we train 8 binary classifiers. The best results were obtained by combining DL-based features with SVM classifier, reaching an F1-score of 0.91, and a Precision of 0.88, a Recall of 0.94, and an AUC of 0.7 considering the ALS group as the positive class in the testing set.