Multimodal Deep Learning for Pediatric Sleep Breathing
Official title: Development of an Artificial Intelligence-Based Model for Assessing the Severity of Pediatric Obstructive Sleep Apnea
Run by Fu Jen Catholic University · NCT07805486 · Data updated 2026-11-12
This study tests a noninvasive, data-driven model to help gauge sleep-disordered breathing severity in children using signals from a fingertip pulse oximeter, pressure-sensing mattress, and millimeter-wave radar during standard sleep studies.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 4 Years to 18 Years |
| Sex | All |
| Enrollment | 50 (estimated) |
| Sponsor | Fu Jen Catholic University Academic / other |
| Locations | 1 site (New Taipei City) |
| Status | Recruiting |
| Started | October 8, 2026 |
| Main results expected | July 31, 2027 |
| Study ends | July 31, 2027 |
| Registry record updated | November 4, 2026 |
| Trial ID | NCT07805486 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Children with clinical suspicion of obstructive sleep apnea who are referred for polysomnography and can tolerate a fingertip or wrap-around pulse oximeter.
✕Probably not a fit
Children who cannot tolerate noninvasive sensors or have conditions that preclude reliable signal collection may not benefit.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This observational study develops a multimodal deep learning model to assess pediatric obstructive sleep apnea by integrating noninvasive signals collected during routine polysomnography. The model uses clinical sleep study results as a reference to train and validate its ability to quantify sleep-disordered breathing. Noninvasive data such as pulse oximetry, pressure sensing from a smart mattress, and millimeter-wave radar will be combined with standard sleep-study metrics. The goal is to provide a convenient approach for evaluating pediatric sleep health that could be applicable in clinical or home settings. The study aims to demonstrate feasibility and potential accuracy of the data-driven approach in reflecting disease severity.
Why it matters
Potential benefit: If successful, the approach could enable quicker, less invasive assessment of pediatric sleep apnea and support more accessible monitoring.
How similar studies have gone: Previous efforts with multimodal data and noninvasive signals have shown promise, but this specific pediatric, polysomnography-aligned model is an emerging approach with limited prior validation.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Individuals with clinical suspicion of obstructive sleep apnea who are referred for polysomnography Exclusion Criteria: * Intolerance to a fingertip or wrap-around pulse oximeter * Presence of significant structural abnormalities of the upper airway * Cardiac arrhythmia * Neuromuscular disease * Hospitalization within the previous one month
Where it's running
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Fu Jen Catholic University Hospital, Fu Jen Catholic University
New Taipei City, Taiwan Recruiting
Study contacts
- Principal investigator: Ke-Yun Chao, PhD — Fu Jen Catholic University
- Study coordinator: Ke-Yun Chao, PhD
- Email: C00152@mail.fjuh.fju.edu.tw
- Phone: +886-905-301-879
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.