Artificial intelligence may boost diagnosis, treatment of sleep disorders

Artificial intelligence (AI) may help improve the efficiency and precision in sleep medicine, resulting in more patient-centred care and better outcomes, according to researchers.

The data collected during polysomnography - the most comprehensive type of sleep study - is well-positioned for enhanced analysis through AI and machine-assisted learning, according to a statement from the American Academy of Sleep Medicine.

"When we typically think of AI in sleep medicine, the obvious use case is for the scoring of sleep and associated events," said lead author and committee Chair Cathy Goldstein.

"This would streamline the processes of sleep laboratories and free up sleep technologist time for direct patient care," said Goldstein, an associate professor at the University of Michigan in the US.

Because of the vast amounts of data collected by sleep centres, AI and machine learning could advance sleep care, resulting in more accurate diagnoses, according to the statement published in the Journal of Clinical Sleep Medicine.

AI could also boost prediction of disease and treatment prognosis, characterisation of disease subtypes, precision in sleep scoring, and optimisation and personalisation of sleep treatments, the statement said. Goldstein noted that AI could be used to automate sleep scoring while identifying additional insights from sleep data.

"AI could allow us to derive more meaningful information from sleep studies, given that our current summary metrics, for example, the apnea-hypopnea index, are not predictive of the health and quality of life outcomes that are important to patients," she said.

"Additionally, AI might help us understand mechanisms underlying obstructive sleep apnea, so we can select the right treatment for the right patient at the right time, as opposed to one-size-fits-all or trial and error approaches," Goldstein said.

The researchers noted that important considerations for the integration of AI into the sleep medicine practice include transparency and disclosure, testing on novel data, and laboratory integration.

The statement recommends that manufacturers disclose the intended population and goal of any programme used in the evaluation of patients.

They should also test programmes intended for clinical use on independent data, and aid sleep centres in evaluation of AI-based software performance, the researchers said.

"AI tools hold great promise for medicine in general, but there has also been a great deal of hype, exaggerated claims and misinformation," Goldstein explained.