The CMT Research Foundation has invested in a research project with Dr. Wolfgang Pernice at Columbia University to develop a novel tool for clinical endpoint monitoring of Charcot-Marie-Tooth (CMT) disease progression entitled DANCER: Digital Assessment of Natural Motion for Clinical Endpoint Research.
“One of the greatest barriers to developing treatments for CMT has been the lack of sensitive, reliable tools to capture how the disease changes over time,” said Laura MacNeill, CEO of the CMT Research Foundation. “By investing in AI-powered endpoint technology, we have the potential to transform how CMT progression is measured in clinical trials—creating precise, scalable clinical assessments of patients and, in doing so, hopefully accelerating the development of effective therapies.”
Due to progressive muscle weakness and sensory deficits, patients with CMT exhibit characteristic movement patterns, such as impaired gait. Although impaired mobility can be a defining feature of the disease, our ability to accurately detect subtle changes in how patients with CMT move has thus far been limited.
How Do We Track CMT Progression Over Time?
Despite decades of effort to develop responsive clinical outcome assessments (COAs) for CMT, the current portfolio of measures potentially remains insufficiently sensitive to support clinical trials for periods of less than 2 years of evaluation. Recent progress in magnetic resonance imaging (MRI)–based biomarker methods suggests that more sensitive measures are possible. These findings align with earlier results from 3D motion capture–based (MoCap) studies, which indicated that certain features of a person’s gait may serve as highly responsive indicators of disease progression in CMT.
While conventional 3D MoCap systems are exceedingly expensive and labor-intensive, recent technological advances have made it possible to unlock comprehensive 3D analysis of whole-body gait and motion outside specialized laboratory settings. Building on advances at the frontier of artificial intelligence (AI), the DANCER platform enables accurate recovery of complete 3D whole-body pose and shape from single-camera videos collected under real-world conditions using any consumer smartphone.
How Can AI Be Used to Better Monitor Changes in CMT?
The DANCER platform is based on advanced AI technology, designed to derive complete and highly accurate 3D whole-body representations from simple smartphone videos. DANCER enables comprehensive analysis of gait and motion patterns and is sufficiently robust that videos could eventually be collected by patients themselves in non-clinical settings.
“Our goal is to move beyond the limitations of traditional motion capture and bring precise, quantitative movement analysis into everyday environments,” said Dr. Wolfgang Pernice. “By using AI to extract rich biomechanical data from simple videos, we can create sensitive measures of disease progression that are both scalable and patient-centered.”
In this study, the research team led by Dr. Pernice will enroll a large cohort of patients with CMT to validate DANCER as a new clinical outcome assessment. This approach may allow researchers to track disease progression with unprecedented accuracy—anywhere in the world—and help remove critical roadblocks in clinical trials for CMT therapeutics.
Join us for a live webinar with Dr. Pernice to hear more.
Register: https://us02web.zoom.us/webinar/register/WN_vaRfT7CFRMe7i_ZJC0lF6w
