  Background and objectives: Patients suffering from neurological diseases may
develop dysarthria, a motor speech disorder affecting the execution of speech.
Close and quantitative monitoring of dysarthria evolution is crucial for
enabling clinicians to promptly implement patient management strategies and
maximizing effectiveness and efficiency of communication functions in term of
restoring, compensating or adjusting. In the clinical assessment of orofacial
structures and functions, at rest condition or during speech and non-speech
movements, a qualitative evaluation is usually performed, throughout visual
observation. Methods: To overcome limitations posed by qualitative assessments,
this work presents a store-and-forward self-service telemonitoring system that
integrates, within its cloud architecture, a convolutional neural network (CNN)
for analyzing video recordings acquired by individuals with dysarthria. This
architecture, called facial landmark Mask RCNN, aims at locating facial
landmarks as a prior for assessing the orofacial functions related to speech
and examining dysarthria evolution in neurological diseases. Results: When
tested on the Toronto NeuroFace dataset, a publicly available annotated dataset
of video recordings from patients with amyotrophic lateral sclerosis (ALS) and
stroke, the proposed CNN achieved a normalized mean error equal to 1.79 on
localizing the facial landmarks. We also tested our system in a real-life
scenario on 11 bulbar-onset ALS subjects, obtaining promising outcomes in terms
of facial landmark position estimation. Discussion and conclusions: This
preliminary study represents a relevant step towards the use of remote tools to
support clinicians in monitoring the evolution of dysarthria.
