Objective:

To determine sensitivity to change in ALS progression by CaptureProof’s AI-enhanced video capture application compared with standard clinical assessments.

Background:

Amyotrophic Lateral Sclerosis (ALS) is a progressive degenerative neuromuscular disorder causing muscle weakness and impaired mobility. Clinical evaluations occurring at 3-month intervals include rating scales, such as ALS Functional Rating Scale (ALSFRS-R) and Rasch Overall ALS Disability Score (ROADS). As disease progresses, in-person evaluations become increasingly difficult. Digital biomarkers offer an innovative solution to remotely monitor and more sensitively detect changes between visits. CaptureProof, an AI-enhanced video capture application, may identify visual biomarkers to remotely monitor ALS progression.

Design/Methods:

Participants were asked to complete motor tasks involving speech, face movement, upper and lower limb movements, balance and gait. CaptureProof’s algorithms generate biometric markers for each task analyzing symmetry, fluidity, speed, range and rate of tasks. Markers were compared between PALS and healthy controls. Markers were also compared against ALSFRS-R and ROADS.

Results:

Data from 6 participants (age 62.32+/-6.11 years, 4 male and 2 female) for Timed up-and-go (TUG) and ALSFRS-R were completed. Participants had a median disease duration of 25 months and average ALSFRS-R score of 40. Compared to normative reference values for 60+ year-olds (8.1+/-0.9), preliminary data shows an increased average TUG time in PALS (15.02+/-5.69 sec). Despite having an abnormal TUG, 2 participants had no self-reported ambulation difficulties on ALSFRS-R or ROADS scores. Longitudinal data collection and analysis is ongoing.

Conclusions:

Our preliminary data suggest that CaptureProof records and extracts features associated with ALS progression. While decreased function was detected by TUG video assessments in all subjects, few functional scores reflect difficulty. This may suggest a greater sensitivity of CaptureProof’s AI-enriched analysis and potential to provide novel and valuable digital biomarkers for ALS progression. Continued data collection will explore other motor tasks and longitudinal monitoring of ALS progression.