OUaR LaB Inc.

CEO : Hyun-Woo Shin

Personal and social interest in sleep is on the rise. According to the global sleep health survey conducted by Philips, 50% of the general public considered sleep as the highest contributing factor on health. Globally, 44% of the population perceives that their sleep quality has deteriorated over the past five years. Among them, the prevalence of sleep apnea is increasing every year, and it is estimated that there are about 1 billion patients worldwide. The sleep apnea treatment device market is expected to grow to $8.8 billion by 2023, and the growth rate of the oral device in particular is expected to be high. In the current global market, there are two types of sleep apnea therapeutic devices- continuous positive airway pressure (CPAP) devices and mandibular advancement device (MAD). Although the airway is well secured for the continuous positive pressure device, half of the patients give up treatment within a year due to the device’s large size and inconvenience. As for the mandibular advancement device, it is less difficult to use. However, MAD cannot be used long term due to common side-affects such as jaw pain and excessive salivation. Therefore, OUaR LaB has developed a novel mandibular advancement device that can reduce unnecessary pain. This is done by detecting the patient’s sleep position and advancing the lower jaw only in the supine position where sleep apnea usually occurs, and returning the lower jaw to its original position in the lateral position where sleep apnea is less common. This is the world’s first auto-titrating mandibular advancement device. It is a new concept of the mandibular advancement device that can solve the current MAD device’s side effects and disadvantages, including jaw joint pain. Furthermore, the device includes a back-and-forth jaw movement control technology based on patients’ sleep information. Our web-based reporting system can trace the use history, distance and pressure, so it can provide patient-specific and night-specific algorithm based on machine learning analysis.