INTRODUCTION: Bulbar symptoms, including difficulty swallowing and speaking, are common in amyotrophic lateral sclerosis (ALS) and other neurological disorders, such as stroke. The presence of bulbar symptoms provides important information regarding clinical outcomes, such as survival time after diagnosis. Nevertheless, there are currently no easily accessible, quantitative methods to measure bulbar function in patients. METHODS: We developed an open-source tool called Tongue Tracker (TT) to quantify bulbar function by training a neural network to track kinematic tongue features of short video clips of lateral tongue movements. We tested 16 healthy controls and ten patients with ALS, of whom two patients were clinically diagnosed with bulbar-onset type and eight patients were clinically diagnosed with limb-onset type. Of the limb-onset patients, five patients also showed symptoms of bulbar impairment. RESULTS: We validated TT by comparing the results with manual delineation of tongue movements in the clips. We demonstrate an early-stage bulbar-onset patient who showed fewer and slower tongue sweeps compared to healthy controls and limb-onset patients and we show that five bulbar-impaired limb-onset patients have a different tongue kinematic profile compared to healthy controls. DISCUSSION: TT may serve to detect quantitative markers of bulbar dysfunction in ALS and other motor disorders, such as stroke, by identifying signatures of spasticity or muscle weakness that affects tongue movement speed and/or tongue movement topography.
amyotrophic lateral sclerosis, bulbar, neural network, quantification, tongue