Actively Recruiting
Led by National Cheng-Kung University Hospital · Updated on 2024-08-06
150
Participants Needed
1
Research Sites
12 weeks
Total Duration
Obstructive sleep apnea syndrome (OSA) is marked by repeated upper airway obstructions during sleep, affecting approximately 14% of men and 5% of women aged 30-70 years. However, precise clinical prediction tools for selecting optimal treatment strategies are lacking. This study aims to develop an automated treatment clustering system using artificial intelligence to classify patients based on etiology into (i) anatomical factors, (ii) reduced muscle responsiveness, and (iii) other non-anatomical factors. This system will analyze physiological sleep assessments, such as electromyography (EMG) and pneumotachographs, from a retrospective polysomnography (PSG) database. Cross-validation will be conducted on new OSA patients undergoing various management strategies, including surgical intervention, CPAP therapy, and oropharyngeal training (delivered face-to-face or via telerehabilitation). This system aims to enhance clinicians' ability to predict treatment success rates and improve patient outcomes.
CONDITIONS
Developing a Treatment Clustering System for Obstructive Sleep Apnea Using Polysomnographic Physiological Signals
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Total: 1 location
1
National Cheng Kung University Hospital
Tainan, Taiwan, 701
Actively Recruiting
J
Jun-Hui Ong, MS
C
Ching-Hsia Hung, PhD
Study Type
INTERVENTIONAL
Masking
NONE
Allocation
NON_RANDOMIZED
Model
PARALLEL
Primary Purpose
OTHER
Number of Arms
5
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J H Ong, C Y Liu, M H Chooi...
https://pubmed.ncbi.nlm.nih.gov/41734638