Summary: Researchers have developed an AI-powered algorithm that analyzes 2D video recordings from routine sleep studies to improve the diagnosis of REM sleep behavior disorder. The disorder, often an early indicator of Parkinson’s disease or dementia, causes abnormal movements during REM sleep and is challenging to diagnose due to subtle and variable symptoms. The AI method achieved a 92% accuracy rate by detecting movement patterns during REM sleep, offering a streamlined, cost-effective tool that could enhance clinical workflows and enable personalized treatment plans.
Key Takeaways:
- Improved Diagnosis with AI: The AI algorithm analyzes standard 2D video recordings from sleep studies, achieving 92% accuracy in detecting REM sleep behavior disorder without the need for specialized 3D cameras.
- Streamlined Clinical Workflow: By integrating this automated tool into sleep test analysis, clinicians could potentially reduce missed diagnoses and improve diagnostic efficiency for REM sleep behavior disorder.
- Broader Implications for Care: The method not only facilitates accurate diagnoses but also provides movement data to help doctors tailor treatment plans based on the severity of the disorder.
A Mount Sinai-led team of researchers has enhanced an artificial intelligence (AI)-powered algorithm to analyze video recordings of clinical sleep tests, ultimately improving accurate diagnosis of REM sleep behavior disorder.
The study findings were published in the journal Annals of Neurology.
REM sleep behavior disorder causes abnormal movements, or the physical acting out of dreams, during the rapid eye movement (REM) phase of sleep. REM sleep behavior disorder that occurs in otherwise healthy adults is called “isolated” REM sleep behavior disorder. It affects more than 1 million people in the United States and, in nearly all cases, is an early sign of Parkinson’s disease or dementia.
REM sleep behavior disorder is extremely difficult to diagnose because its symptoms can go unnoticed or be confused with other diseases. A definitive diagnosis requires 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.
Leveraging AI for Better Detection
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 outlines 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 REM sleep behavior disorder of nearly 92%.
“This automated approach could be integrated into clinical workflow during the interpretation of sleep tests to enhance and facilitate diagnosis, and avoid missed diagnoses,” says 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, in a release. “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.”
A Collaborative Effort
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 analysis of recordings at a sleep center of about 80 REM sleep behavior disorder patients and a control group of about 90 patients without REM sleep behavior disorder who had either another sleep disorder or no sleep disruption.
Promising Results
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%.
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.
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