1. Background
Obstructive Sleep Apnea (OSA) is a common sleep-related breathing disorder characterized by recurrent partial or complete obstruction of the upper airway during sleep, leading to reduced oxygen saturation, poor sleep quality, and daytime sleepiness. Traditional OSA diagnosis mainly relies on polysomnography (PSG) and clinical evaluations, but these methods can be time-consuming, subjective, and less accurate for diagnosing mild or moderate cases of OSA.
With the development of deep learning techniques, multimodal data-based methods present new opportunities for improving OSA diagnosis. By combining various physiological signals (e.g., airflow, blood oxygen saturation, electroencephalography) with imaging data (e.g., CT or MRI scans of the upper airway), deep learning models can significantly enhance diagnostic accuracy and efficiency.
2. Objectives
This study aims to develop a deep learning-based system for the auxiliary diagnosis of OSA, using multimodal data sources from the UK Biobank, including sleep monitoring data, imaging data, clinical data, and genetic data. The goal is to improve early OSA diagnosis and provide.
3.Scientific Rationale:
OSA is a complex condition influenced by various factors, including anatomical, physiological, and genetic components. Current diagnostic methods, such as polysomnography (PSG), are often costly and inefficient, particularly for diagnosing mild or moderate cases. Integrating multiple data sources through deep learning can address these limitations. By using data from the UK Biobank, which includes sleep monitoring, imaging, clinical, and genetic information, this project aims to enhance diagnostic accuracy and provide insights into the genetic predispositions of OSA.
