Health
How new artificial intelligence algorithms can treat sleep disorders
In a new study, researchers from the School of Computer Science at the University of Copenhagen are working with the Danish Sleep Medicine Center in the Danish Hospital Rigsho Spitalet to improve the diagnosis, treatment, and our overall understanding of sleep disorders. We have developed an artificial intelligence algorithm that can be used.
Sleep disorders, sleep apnea, and narcolepsy are one of a variety of sleep disorders that thousands of Danes suffer from. In addition, sleep apnea is estimated to be undiagnosed in as many as 200,000 Danes.
“The algorithm is very accurate. We completed a variety of tests and its performance was comparable to the best doctors in this field around the world,” said a PhD in Computer Science. The lead author of the study, Matthias Perthrev, is a journal npj Digital Medicine (link).
Testing for sleep disorders today usually begins with admission to a sleep clinic. Here, we use a variety of measuring instruments to monitor a person’s night sleep. Sleep disorder specialists review the 7-8 hour measurements obtained from the patient’s nighttime sleep.
Your doctor will manually divide these 7-8 hours of sleep into 30-second intervals. All of these should be categorized into different sleep stages, such as REM (rapid eye movement) sleep, light sleep, and deep sleep. A consumer job that the algorithm can run in seconds.
“This project proved that machine learning could be used to make these measurements very safely. This makes a lot of sense. By saving a lot of time, more patients Can be effectively evaluated and diagnosed, “explains Professor Poul Jennum. Director of Neurophysiology and Danish Center for Sleep Medicine.
In the Capital Region of Denmark alone, more than 4,000 polysomnography tests (known as PSGs or sleep studies) are conducted annually in patients with sleep apnea and more complex sleep disorders.
It takes 1.5 to 3 hours for a doctor to analyze a PSG study. Therefore, in the Capital Region of Denmark alone, the introduction of new algorithms could free up 6,000 to 12,000 medical hours.
By collecting data from a variety of sources, the researchers behind the algorithm were able to guarantee optimal functionality. In total, 20,000 nights of sleep were collected from the United States and many European countries and used to train the algorithm.
“We collected sleep data from the entire continent, sleep clinics, and patient groups. The fact that the algorithm works well under these diverse conditions is groundbreaking,” said a project on the computer science side. Led Mathias Perslev and Christian Igel explain. ::
“Achieving this kind of generalization is one of the biggest challenges in medical data analysis.”
They hope that this algorithm will help doctors and researchers around the world learn more about sleep disorders in the future.
“This algorithm requires a small amount of measurement with common clinical instruments, so the use of this software may be particularly relevant in developing countries where the latest instruments and specialists are not available,” he said. Mathias Perslev says.
Researchers are currently working with Danish physicians to obtain approval for clinical use of the software and algorithms.
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