We’re excited to share our latest R&D project: GAID. Together with Amsterdam UMC, we aim to develop and validate an AI-assisted clinical decision-support system that supports clinicians in the complex and error-prone workflows required for clinical gait analysis. The system will help healthcare professionals make informed and personalized treatment decisions, aiming to improve the quality and structure of clinical reasoning while reducing the effort and time required. The project is an industry-led public-private collaboration co-funded through a PPP Subsidy from Health~Holland (project HH-PPS-25169-MKB). It aims to advance impactful new technologies for gait analysis in children with cerebral palsy (CP) towards future clinical use.
Difficulties with walking are common and can be due to conditions such as brain injury, muscular diseases, or orthopedic disorders. Specialized gait laboratories use detailed measurements, including video recordings, muscle activity measurements and physical examinations, to understand movement patterns and underlying impairments (e.g. spasticity, muscle weakness, or bone deformities). This information is used to select the optimal treatment. For children with CP, treatment may include, for example, botulinum toxin injections or surgery. However, interpreting these data is complex and time-consuming, and levels of expertise can vary considerably between centers. As a result, the best treatment is not always selected, and some patients may undergo unnecessary treatments.
The project combines healthcare and technology expertise to develop software that supports clinicians in the clinical reasoning process: from classifying abnormal movement patterns to identifying their underlying impairments and recommending suitable treatment options. The system will be developed and evaluated with healthcare professionals to ensure it fits clinical practice and supports personalized care.
The project will deliver four key outcomes:
- A structured dataset containing gait assessments before and after treatment.
- An interactive workflow and user interface to support clinical decision-making.
- Intelligent methods to detect gait abnormalities, identify underlying impairments and suggest suitable treatments.
- A tested proof-of-concept system evaluated in a demonstration environment.
These results will provide the foundation for further development and future clinical use.
Do you want to know more?
Interested in hearing more about Moveshelf, our R&D activities related to clinical decision support, or how Moveshelf can help improve your workflow?
Get in touch