Harnessing Remote Speech Tasks for Early ALS Biomarker Identification

Abstract

Biomarkers are fundamental for improving early diagnosis, monitoring treatment response, and deepening our understanding of disease mechanisms. The lack of effective biomarkers is particularly detrimental in diseases like amyotrophic lateral sclerosis (ALS), where delays in diagnosis can span 12-18 months, significantly affecting conditions marked by rapid disability progression and reduced lifespan. In this work, we analyzed recordings from 291 participants, including 135 people with ALS (pALS), who performed nine different speech tasks during each session, totaling 6,276 sessions. These recordings were processed using OpenSMILE to extract acoustic features, which were input into three classifiers. We aimed to discriminate pALS from controls and identify different stages of ALS (bulbar manifest and bulbar pre-manifest). We achieved an Area Under the Curve (AUC) of up to 66% (with a recall rate of 79%) and up to 90% (with a recall rate of 91%) for discriminating pre-manifest and manifest ALS from controls, respectively. This work represents a significant step toward identifying reliable biomarkers for ALS, offering new insights into early detection and a better understanding of disease progression.