Artificial Intelligence in Sleep Medicine
In recent years there has been a rapid emergence of artificial intelligence (AI) in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally thought to require human intelligence, such as
Sleep medicine is well positioned to benefit from advances that use big data to create artificially intelligent computer programs. There are three main areas where sleep medicine benefits from AI.
➤The first application is modernizing the scoring process of polysomnography (PSG). Currently, the sleep technologists play a major role on judicating the 30-second sleep epochs as awake or asleep and/or the stage of sleep. The AI has potential to improve this lengthy process, and expedite sleep reports.
[polysomnography (PSG) Meaning➤a type of sleep study, is a multi-parameter study of sleep and a diagnostic tool in sleep medicine. The test result is called a polysomnogram, also abbreviated PSG].
➤The second application of AI in sleep is leveraging the longitudinal data accumulated within electronic medical records (EMR). Sleep medicine is primed to benefit from AI used in population health research. Researchers have successfully leveraged “big data” to offer new insights into sleep physiology, improve the accuracy of diagnosis of sleep disorders, predict response and adherence to treatment, and use sleep parameters as predictors of future physical and mental health. This leads to treatment optimization and personalization.
➤The third application of AI in sleep medicine is the use of wearable sensors. The wearable sensors show promise in tracking health records and linking several digital biomarkers to the overall health condition of patients. They provide an opportunity to elevate the traditional EMR to the new level of electronic health records (EHR) by measuring several parameters and synchronizing multilevel bio-potentials and health time series.
These applications will likely become more widely available, empowering people to improve their sleep, through the possibility of better understanding their sleeping patterns. Ultimately this can result in a reduction in sleep health disparities.
The goal of this research topic is to inform advances in the use of artificial intelligence (AI) in the field of sleep medicine.
Topics of interest include (but are not limited to) Artificial Intelligence and Machine Learning related to:
1. Assisting and enhancing polysomnography scoring
2. Supporting clinical decision
3. Providing new insights to inform the clinical care of sleep disorders
3. Leveraging the electronic medical records (EMR)
4. Managing and monitoring population health
5. Advancing our understanding of the integral role sleep plays in human health
6. Harnessing the potential of wearable devices (i.e. wrist actigraphy) into sleep medicine
7. Integrating wearables devices into EMR for remote health monitoring and at-home health application
8. Understanding and addressing sleep health disparities
These applications will likely become more widely available, empowering people to improve their sleep, through the possibility of better understanding their sleeping patterns. Ultimately this can result in a reduction in sleep health disparities.
The goal of this research topic is to inform advances in the use of artificial intelligence (AI) in the field of sleep medicine.
Topics of interest include (but are not limited to) Artificial Intelligence and Machine Learning related to:
1. Assisting and enhancing polysomnography scoring
2. Supporting clinical decision
3. Providing new insights to inform the clinical care of sleep disorders
3. Leveraging the electronic medical records (EMR)
4. Managing and monitoring population health
5. Advancing our understanding of the integral role sleep plays in human health
6. Harnessing the potential of wearable devices (i.e. wrist actigraphy) into sleep medicine
7. Integrating wearables devices into EMR for remote health monitoring and at-home health application
8. Understanding and addressing sleep health disparities
