A deep learning pipeline for detecting vestibular schwannoma patients with unilateral vestibular loss based on kinematic data.

ABSTRACT: Detecting subtle balance and coordination impairments in individuals with vestibular schwannomas (VS) remains a challenge, particularly when traditional clinical assessments appear normal. Focusing on patients prior to surgical intervention, this study offers a unique opportunity to capture early motor adaptations before permanent vestibular loss. We developed a deep learning-based classifier to distinguish VS patients from age-matched healthy controls using kinematic data collected during standardized gait tasks. Participants performed a short-duration (< 10 s) straight-path walk at a normal pace (an item from the Functional Gait Assessment) and a 30-second walk with intermittent eye closure. Six inertial measurement units (IMUs) were placed at different body locations, and models were tr

SUBMITTER: Kohler Voinov LC 

PROVIDER: S-EPMC12748630 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature