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NCT Number: NCT07805486

Development of a Multimodal Deep Learning Model for Pediatric Patients

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.

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Key information

Conditions

Pediatric Obstructive Sleep Apnea Polysomnography

Age range

4 year–18 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Fu Jen Catholic University Hospital, Fu Jen Catholic University

New Taipei City, 24352, Taiwan

Location contact

Ke-Yun Chao, PhD

CONTACT

[email protected]

+886-905-301-879

About this study

Pediatric obstructive sleep apnea may affect growth, development, cognitive function, and overall health. Although polysomnography is commonly used for clinical assessment, its application may be limited by time, cost, and accessibility. Recent advances in noninvasive monitoring technologies have provided new possibilities for sleep-related assessment. This study will collect and integrate multiple physiological signals from pediatric participants undergoing routine sleep examinations and to develop a data-driven model for evaluating sleep-related respiratory conditions. Clinical examination results will be used as the reference for model development and validation. The findings of this study are expected to support the development of a convenient and noninvasive approach for pediatric sleep assessment and may provide a reference for future clinical and home-based applications.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

fingertip pulse oximeter

Device

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

pressure-sensing mattresses

Device

using ballistocardiography for monitoring respiration and heart rate

millimeter-wave radar

Device

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

Primary outcomes

  1. the correlation among the apnea-hypopnea index, millimeter-wave radar signals, and ballistocardiography waveforms

    Time frame: one night

Study contacts

Contact information is provided by the study sponsor or research team.

Ke-Yun Chao, PhD

CONTACT

[email protected]

+886-905-301-879

Sponsors and collaborators

Lead sponsor

Fu Jen Catholic University

Other

Registry information

Official study title

Development of an Artificial Intelligence-Based Model for Assessing the Severity of Pediatric Obstructive Sleep Apnea

Important dates

Study start
2026
Primary completion
2027
Study completion
2027
First posted
Sep 4, 2026
Registry last updated
Sep 4, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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