Development of a Multimodal Deep Learning Model for Pediatric Patients
Development of an Artificial Intelligence-Based Model for Assessing the Severity of Pediatric Obstructive Sleep Apnea
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.
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
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.
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Ke-Yun Chao, PhD
- Phone Number: +886-905-301-879
- Email: C00152@mail.fjuh.fju.edu.tw
Study Locations
-
-
-
New Taipei City, Taiwan, 24352
- Recruiting
- Fu Jen Catholic University Hospital, Fu Jen Catholic University
-
Contact:
- Ke-Yun Chao, PhD
- Phone Number: +886-905-301-879
- Email: C00152@mail.fjuh.fju.edu.tw
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
patient of affiliated university hospital
Description
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
Study Plan
How is the study designed?
Design Details
- Observational Models: COHORT
- Time Perspectives: PROSPECTIVE
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
the correlation among the apnea-hypopnea index, millimeter-wave radar signals, and ballistocardiography waveforms
Time Frame: up to 12 hours
|
up to 12 hours
|
Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Ke-Yun Chao, PhD, Fu Jen Catholic University
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Other Study ID Numbers
Other Study ID Numbers
- FJUH115604
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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