Researchers at Mount Sinai have developed an advanced AI algorithm that analyzes video recordings from sleep tests, achieving a groundbreaking 92% accuracy in diagnosing REM sleep behavior disorder (RBD)
Mount Sinai researchers have improved an AI tool for diagnosing a major sleep disorder from video sleep tests.
Their approach, which captures the subtleties of sleep movements with enhanced accuracy, promises earlier detection of diseases like Parkinson’s, ushering in more tailored treatments.
AI-Powered Sleep Disorder Diagnosis
A team of researchers led by Mount Sinai has improved an artificial intelligence (AI)-powered algorithm designed to analyze video recordings from clinical sleep tests. This advancement significantly enhances the accuracy of diagnosing a common sleep disorder that affects over 80 million people worldwide. The study was published today (January 9) in the journal Annals of Neurology.
REM sleep behavior disorder (RBD) is a condition characterized by abnormal movements or the physical acting out of dreams during the rapid eye movement (REM) phase of sleep. When RBD occurs in otherwise healthy adults, it is referred to as “isolated” RBD, which affects more than one million people in the United States. Nearly all cases of isolated RBD serve as an early indicator of Parkinson’s disease or dementia.
Challenges in Diagnosing RBD
RBD is extremely difficult to diagnose because its symptoms can go unnoticed or be confused with other diseases. A definitive diagnosis requires a sleep study, known as a video-polysomnogram, to be conducted by a medical professional at a facility with sleep-monitoring technology. The data are also subjective and can be difficult to universally interpret based on multiple and complex variables including sleep stages and amount of muscle activity. Although video data is systematically recorded during a sleep test, it is rarely reviewed and is often discarded after the test has been interpreted.
Previous limited work in this area had suggested that research-grade 3D cameras may be needed to detect movements during sleep because sheets or blankets would cover the activity. This study is the first to outline the development of an automated machine-learning method that analyzes video recordings routinely collected with a 2D camera during overnight sleep tests. This method also defines additional “classifiers” or features of movements, yielding an accuracy rate for detecting RBD of nearly 92 percent.
Clinical Integration and Future Applications
“This automated approach could be integrated into clinical workflow during the interpretation of sleep tests to enhance and facilitate diagnosis, and avoid missed diagnoses,” said corresponding author Emmanuel During, MD, Associate Professor of Neurology (Movement Disorders), and Medicine (Pulmonary, Critical Care and Sleep Medicine), at the Icahn School of Medicine at Mount Sinai.
“This method could also be used to inform treatment decisions based on the severity of movements displayed during the sleep tests and, ultimately, help doctors personalize care plans for individual patients.”
Expanding AI Capabilities in Sleep Studies
The Mount Sinai team replicated and expanded a proposal for an automated machine-learning analysis of movements during sleep studies that was created by researchers at the Medical University of Innsbruck in Austria. This approach uses computer vision, a field of artificial intelligence that allows computers to analyze and understand visual data including images and videos.
Building on this framework, Mount Sinai experts used 2D cameras, which are routinely found in clinical sleep labs, to monitor patient slumber overnight. The dataset included an analysis of recordings at a sleep center of about 80 RBD patients and a control group of about 90 patients without RBD who had either another sleep disorder or no sleep disruption. An automated algorithm that calculated the motion of pixels between consecutive frames in a video was able to detect movements during REM sleep.
The experts reviewed the data to extract the rate, ratio, magnitude, and velocity of movements, and ratio of immobility. They analyzed these five features of short movements to achieve the highest accuracy to date by researchers, at 92 percent.
Reference: “Automated detection of isolated REM sleep behavior disorder using computer vision” 9 January 2025, Annals of Neurology.
Researchers from the Swiss Federal Technology Institute of Lausanne (École Polytechnique Fédérale de Lausanne) in Lausanne, Switzerland contributed to the study by sharing their expertise in computer vision.
