Detection of Scoliosis v1.0
Research type
Research Study
Full title
Early Detection of Adolescent Idiopathic Scoliosis Using Machine Learning on Plantar Pressure Data
IRAS ID
357368
Contact name
chaozong Liu
Contact email
Sponsor organisation
University College London
Duration of Study in the UK
3 years, 1 months, 17 days
Research summary
This research aims to explore a non-invasive method for early detection of adolescent idiopathic scoliosis (AIS) using plantar pressure data collected through a foot pressure sensor. Scoliosis is a condition where the spine curves abnormally, and detecting it early can help prevent serious complications. Traditionally, scoliosis is diagnosed through X-rays, which can be costly and expose children to radiation. This study proposes a safer, more accessible screening method using machine learning algorithms to analyze foot pressure patterns, which could provide an early indication of scoliosis severity.
We will recruit adolescents aged 10 to 18, both with and without scoliosis, to participate in the study. Participants will undergo a non-invasive plantar pressure measurement, where they will simply stand on a pressure mat that records the distribution of weight across their feet. The collected data will be analyzed to determine if it correlates with the presence and severity of scoliosis. No additional treatments or interventions will be required, and the study will not affect participants' medical care.
By using this innovative approach, the study aims to provide an alternative, safe, and cost-effective way to screen for scoliosis in the future. This method could be particularly beneficial for school-based screenings, where it would reduce the need for expensive X-rays and ensure more widespread early detection.
REC name
South Central - Hampshire A Research Ethics Committee
REC reference
25/SC/0250
Date of REC Opinion
3 Oct 2025
REC opinion
Further Information Favourable Opinion