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.
Disclaimer
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This is a summary of
the original study
.
Summaries may miss details or leave out important information. Before applying or accepting participation, make sure you have read and understood the full study. Curemydisease.com takes no responsibility whatsoever for anything missed, misunderstood, or acted upon as a result of our summary — we know it does not capture everything.
This is a summary of the original study . Summaries may miss details or leave out important information. Before applying or accepting participation, make sure you have read and understood the full study. Curemydisease.com takes no responsibility whatsoever for anything missed, misunderstood, or acted upon as a result of our summary — we know it does not capture everything.
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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.