Actively Recruiting
Multimodal Deep Learning Model for Predicting the Apnea-Hypopnea Index in Obstructive Sleep
Led by Fu Jen Catholic University · Updated on 2026-03-05
150
Participants Needed
1
Research Sites
47 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
This study aims to develop a multimodal deep learning model that integrates noninvasive signals to predict the severity of obstructive sleep apnea. By establishing a clinically viable and user-friendly monitoring tool, the study seeks to enhance early screening accessibility and support the development of home-based sleep care systems.
CONDITIONS
Official Title
Multimodal Deep Learning Model for Predicting the Apnea-Hypopnea Index in Obstructive Sleep
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Age between 30 and 75 years
- Clinically suspected obstructive sleep apnea and scheduled for polysomnography
- Willing and able to provide written informed consent
You will not qualify if you...
- Intolerance to the electronic stethoscope or fingertip pulse oximeter
- Significant structural airway abnormalities
- Arrhythmia
- Neuromuscular disorders
- Pregnancy
- Hospitalization within the past 1 month
- Inability to provide informed consent or requiring legal guardian consent
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
Fu Jen Catholic University Hospital, Fu Jen Catholic University
New Taipei City, Taiwan, 24352
Actively Recruiting
Research Team
K
Ke-Yun Chao, PhD
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
0
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