How Can AI Aid to Better Assessment and Treatment of Sleep Disorder?

Healthcare Tech Outlook | Wednesday, September 30, 2020

According to reports, artificial intelligence (AI) has the potential to enhance efficiencies and precision in sleep medication.

Fremont, CA: AI is suggested to improve patient-centered care and better outcomes for patients who are suffering from sleep disorders. According to a statement, the enectrophysiological data gathered during polysomnography, which is the most comprehensive kind of sleep study, is well-positioned for an enhanced analysis via AI and machine-assisted learning.

According to the researchers, When AI is associated with sleep medicine, the most obvious use case is for the scoring of sleep and associated events; this may streamline the process of sleep laboratories and free up the sleep technologists time for direct patient care.

Optimization and personalization of sleep treatments

Because of the vast amounts of data collected by sleep centers, AI and machine learning could advance sleep care, leading to a more accurate diagnosis, prediction of disease and treatment prognosis, characterization of disease subtypes, precision in sleep scoring, and optimization and personalization of sleep treatments. AI could be used to automate sleep scoring and also identifying additional insights from sleep data.

AI could enable the doctors to derive more meaningful information from sleep studies, given that the current summary metrics. Additionally, AI can help to understand mechanisms underlying obstructive sleep apnea so that the doctors can select the right treatment for the right patient at the right time instead of one-size-fits-all or trial and error approaches.

Integrating AI in sleep medicine practices

The essential considerations for the integration of AI into the sleep medicine practice include transparency and disclosure, testing on novel data, and laboratory integration. The statement suggests that the manufacturers disclose the intended population and goal of any program used in the evaluation of patients, test programs intended for clinical use on independent data, and aid sleep centers on the assessment of AI-based software performance.