AI and footprints: a new way to detect scoliosis?
NCT ID NCT07581015
First seen Jun 27, 2026 · Last updated Jun 27, 2026
Summary
This study explores whether foot pressure patterns, measured while standing and walking, can be used with machine learning to detect adolescent idiopathic scoliosis early. Researchers will collect data from 500 teens aged 10-18, including those with and without scoliosis. The goal is to create a simple, non-invasive screening tool that could complement current methods.
What this could mean
Our plain-language read of the trial. This is informational only — not medical advice or a prediction.
- What this could lead to
- If successful, this could lead to a simple, non-invasive screening tool for early scoliosis detection, reducing the need for X-rays.
- What could go wrong
- This is an early-stage study with no treatment involved. The machine learning model may not be accurate enough for real-world use, and results may not apply to all populations.
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Conditions
The condition(s) this trial relates to.
As listed by the trial registrant
The condition terms exactly as the trial's registrant entered them.
More trials for these conditions
Other studies related to the condition(s) this trial covers.
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