Melissa Jones, MD, shares insights into recent artificial intelligence (AI) innovations in the realm of sleep studies.
Melissa Jones, MD, professor of psychiatry, Baylor College of Medicine, joined The American Journal of Managed Care® (AJMC®) to discuss notable innovations with artificial intelligence (AI) and sleep research, as well as to give her perspective on the validity and accuracy of wearable devices that track sleep data.
Jones presented at SLEEP 2024, the American Academy of Sleep Medicine and Sleep Research Society, Annual Meeting with her session covering “Artificial Intelligence and Big Data in Sleep Medicine.
Transcript
What do you believe are the most significant advancements in AI-driven sleep research over the past few years?
I think one of the major advancements has been the development of technologies that enable sleep staging with ambulatory technology, or wearable technology. I think this has the opportunity to enhance access to sleep medicine care. I think it also may help facilitate physicians who are facing reimbursement cuts, staff shortages, and burnout. It may help them get more sleep studies performed and more patients treated.
Can you explain how AI algorithms analyze sleep data from wearable devices? How accurate are these analyses compared with traditional methods?
The wearable devices collect raw data using various sensors; for instance, heart rate monitors or accelerometers that detect motion. And then this raw data is preprocessed and specific features are typically extracted from it such as heart rate variability, for instance. And then the machine-learning algorithm is trained on expertly-labelled data. So, in the case of sleep study staging, the 30-second Epics are manually labeled by sleep medicine physicians and that is what the machine learning algorithm is trained on. And then the algorithm detects patterns in the data. And then this machine learning algorithm is fed a validation set of unseen data, and it is validated against this expertly labelled data. So, the accuracy varies.
There's still a ways to go. One study, in the case of REM sleep behavior disorder, did report an accuracy of 96% to 100% in classifying patients with Parkinson disease and REM sleep behavior disorder vs no REM sleep behavior disorder; that was using actigraphy watch monitoring for 14 days and a support vector machine. But again, there's still a ways to go. And there needs to be more testing with large, diverse datasets.
Neurologists Share Tips for Securing Patient Access to Gene Therapies
March 19th 2025Tenacious efforts at every level, from the individual clinician to the hospital to the state to Congress, will be needed to make sure patients can access life-saving gene therapies for neuromuscular diseases.
Read More
EMBARK Data Show Continued Improvements With DMD Gene Therapy
March 19th 2025Data from the EMBARK trial of delandistrogene moxeparvovec in patients with Duchenne muscular dystrophy (DMD) show that benefits in functional outcomes, gene expression, and muscle imaging persist 2 years after receiving the gene therapy.
Read More
How Access to SMA Treatment Varies Globally and by Insurance Type
March 18th 2025Posters presented at the 2025 Muscular Dystrophy Association (MDA) Clinical & Scientific Conference show that therapeutic advances in treating spinal muscular atrophy (SMA) are not uniformly making it into the hands of patients who could benefit.
Read More