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Development of a Multimodal Deep Learning Model for Pediatric Patients

NCT07805486 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 50

Last updated 2026-09-04

No results posted yet for this study

Summary

This study aims to develop a multimodal data-driven model integrating multiple noninvasive physiological signals to assess the severity of pediatric sleep-disordered breathing, using standard clinical sleep study results as the reference.

Conditions

  • Pediatric Obstructive Sleep Apnea
  • Polysomnography

Interventions

DEVICE

fingertip pulse oximeter

a small device placed on the finger to measure blood oxygen saturation and pulse rate noninvasively

DEVICE

pressure-sensing mattresses

using ballistocardiography for monitoring respiration and heart rate

DEVICE

millimeter-wave radar

using millimeter-wave radar technology based on the Doppler effect, the device continuously monitors respiratory-related chest wall movements

Sponsors & Collaborators

  • Fu Jen Catholic University

    lead OTHER

Principal Investigators

  • Ke-Yun Chao, PhD · Fu Jen Catholic University

Eligibility

Min Age
4 Years
Max Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-09-01
Primary Completion
2027-07-31
Completion
2027-07-31

Countries

  • Taiwan

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07805486 on ClinicalTrials.gov