LLM-Assisted Interpretation of Kinematic Gait Data in Children with Cerebral Palsy: An Expert Agreement Study on Gait Deviation Detection and Surgical Group Recommendations

Authors:
Preprint
Publisher website
Google Scholar
Abstract
Three-dimensional instrumented gait analysis (3D-IGA) is widely used to guide surgical decision-making in children with cerebral palsy (CP), but its interpretation is time-consuming and prone to inter-rater variability. We investigated whether a generative large language model (LLM) could consistently generate gait deviation findings and surgical procedure suggestions that align with expert judgement. Kinematic features for lower-limb joints across the gait cycle, stance, and swing were extracted from eight children with unilateral CP using the open-source GaitSharing Toolkit and a structured prompt, then submitted three times per patient to OpenAI's GPT-5.5 model. The model assessed 28 kinematic deviations and 12 surgical procedure groups using majority voting. One gait analyst rated the deviations, and two paediatric orthopaedic surgeons independently rated the procedures on a 0–2 ordinal scale, blinded to all clinical information beyond the kinematic curves and diagnosis. Agreement with the gait expert averaged 84.7% in the sagittal plane and was lowest at the knee (62.6%). Surgeons’ agreement with the LLM reached 83.9% and 73.5%, with the tibialis anterior procedure showing the lowest concordance. Inter-surgeon agreement was 79.2%. The LLM showed high self-consistency (>90% across runs). This work demonstrates the potential of generative LLMs as assistive tools in clinical gait analysis for deviation detection and future treatment planning.

This publication has 32 references indexed in Scilit: