Precision medicine in ALS: Identification of new acoustic markers for dysarthria severity assessment

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease affecting motorneurons of the bulbar, cervical, thoracic, or lumbar segments. Bulbar presentation is a devastating characteristic that impairs patients' ability to communicate and is linked to shorter survival. Early acoustic manifestation of voice symptoms, such as dysarthria, is very variable, making its detection and classification challenging, both by human specialists and automatic systems. In this context, precision medicine, defined as prevention and treatment strategies that take individual variability into account, has gained a great interest in the ALS community. Specifically, the use of innovative Artificial Intelligence techniques, such as Machine Learning, plays a pivotal role in finding specific patterns in the data set to help neurologists in clinical decision-making. Therefore, the main objective of this study was to find new markers, and new patterns, to promptly detect the possible presence of dysarthria and to correctly classify its severity. We have performed an acoustic analysis on different voice signals of various degrees of impairment acquired during outpatient visits at the ALS center of the Federico IIUniversity Hospital. From the collected signals, a new database containing different acoustic parameters was realized, on which several experiments were performed. The study led us to the discovery of markers that helped to develop a decision tree that separated healthy subjects from patients and, among patients, those with different severity of dysarthria. This model achieved good results in terms of dysarthria classification accuracy, 86.6%, which is excellent considering the small number of subjects in the data set.

Automated summary

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that affects motor neurons and impairs communication. By using innovative machine learning techniques, this study successfully discovered markers and patterns to promptly detect and classify the severity of speech difficulties.