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Patients suffering from neurodegenerative diseases (e.g., amyotrophic lateral sclerosis (ALS) and stroke) may develop dysarthria, a neurological disorder that causes pervasive and unrelenting speech troubles. Dysarthria-evolution monitoring, via the assessments of oro-facial musculature, is crucial for allowing clinicians to promptly implement corrective and compensatory strategies to tackle communicative disability. To support clinicians, we propose a deep learning-based approach to facial landmarks detection as useful prior for assessing the oro-facial muscles impairments caused by ALS and stroke. When tested on the Toronto NeuroFace dataset, which is the largest publicly available annotated dataset in the field, the proposed approach reached a normalized mean error equal to 1.79, promptly the possibility of translating such technology in the actual clinical practice.
Università Politecnica delle Marche, Italy
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