This study aims to develop an artificial intelligence (AI) model for more accurately diagnosing obstructive sleep apnea (OSA) by collecting blood oxygen saturation and other health information during sleep using a smartwatch. OSA is common but often underdiagnosed, and the gold-standard diagnostic test, polysomnography, is costly and time-consuming. Smartwatches can provide a variety of health data, such as sleep patterns, blood oxygen saturation, and heart rate, which can help detect key symptoms and signs of OSA. By developing an AI model that uses smartwatch data to screen for OSA, this study seeks to offer a cost-effective and accessible diagnostic method, ultimately contributing to the early detection and improved treatment rates of OSA.
Study Type
OBSERVATIONAL
Enrollment
147
Use of the Galaxy Watch 4 during sleep for approximately two weeks prior to the polysomnography test, including the night of the test.
Seoul National University Hospital
Seoul, South Korea
RECRUITINGPredictive Accuracy of the AI Model for Moderate-to-Severe Obstructive Sleep Apnea
Evaluation of how well the AI model, developed using clinical data and smartwatch-recorded information including nocturnal oxygen saturation, predicts moderate-to-severe obstructive sleep apnea (defined as apnea-hypopnea index ≥15/hour) diagnosed by polysomnography.
Time frame: Up to 2 weeks prior to the polysomnography test.
Predictive Accuracy of the Galaxy Watch Sleep Apnea Feature (SAF)
Assessment of the accuracy of the Galaxy Watch's built-in sleep apnea feature (SAF) in predicting moderate-to-severe obstructive sleep apnea diagnosed by polysomnography.
Time frame: Up to 2 weeks prior to the polysomnography test.
Comparison of AI Model and Galaxy Watch Sleep Apnea Feature (SAF) Performance
Comparison of the predictive performance between the AI model developed in this study and the Galaxy Watch's built-in sleep apnea feature (SAF) for detecting moderate-to-severe obstructive sleep apnea.
Time frame: Up to 2 weeks prior to the polysomnography test.
Comparison of AI Model and STOP-Bang Questionnaire Performance
Comparison of the predictive performance between the AI model developed in this study and the STOP-Bang questionnaire for detecting moderate-to-severe obstructive sleep apnea.
Time frame: Up to 2 weeks prior to the polysomnography test.
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