A study led by the University of Western Australia (UWA) indicates machine learning-based algorithms they developed for 3-D facial photography could provide a simple and highly accurate method of predicting the presence of sleep apnoea.
The severity of a person’s sleep apnoea from these photographs.
The complete system is based on machine learning technology, which means researchers feed a machine learning algorithm with features – 3-D distances between certain important landmarks on the face and then train it to learn the difference between the two classes.
Once trained, the machine learning algorithm was able to distinguish between the controls and patients with an accuracy of more than 91%.
Sleep disorders are estimated to cost the Australian health system more than $5 billion annually and said more than half the cost is associated with sleep apnoea, which is associated with snoring and repeated periods of ‘choking’ during sleep.
Despite sleep apnoea being treatable, up to75% of individuals remain undiagnosed.
This is largely because current methods of assessing sleep apnoea are expensive and access to them is limited. The development of this technology will significantly reduce the costs of possible early detection of sleep apnoea.”
The research teams aim to perfect the 3-D mapping technology using more data and robust algorithms to make it a reliable tool for diagnosing obstructive sleep apnoea.
The current study uses only 400 3-D faces they have already collected more than 2,000 faces of sleep apnoea patients. The researchers want to develop a technology that is cheap, easily accessible and reliable.