The Rise of Artificial Intelligence in Sleep Medicine with Nox Medical


Nox Medical’s vice president of AI and data science is featured in a recent Sleep Review magazine article on how artificial intelligence could shape the diagnosis and treatment of sleep disorders.

We at Nox Medical incorporate a range of artificial intelligence (AI) algorithms into our sleep medicine software, Noxturnal, including those for sleep staging, respiratory flow analysis, apnea-hypopnea index (AHI), and periodic limb movement. In a recent Sleep Review magazine article, Jon Agustsson, PhD, vice president of AI and data science for Nox Medical, explains how AI could shape the future of sleep medicine by ushering in a new era of precision care.

“In the future, AI can have wide applications in the field of sleep medicine, potentially driving precision medicine by helping clinicians identify the best possible, personalized treatment plans for each patient’s endophenotype,” says Jon Agustsson, PhD, vice president of AI and data science for Nox Medical.

“We are not there yet, but we have high hopes for the development of AI that can help us better understand each individual patient’s needs and how to best serve them,” he says.

According to the Sleep Review magazine article, written by Ann H. Carlson, a single polysomnogram (PSG) provides invaluable information to help diagnose and treat one specific patient, but scientists have long understood that comparing hundreds—or even thousands—of tests can help identify otherwise undiscovered patterns across the general population.

“Using AI solutions to analyze sleep testing data for new patterns is a logical step for researchers looking for connections to help predict sleep markers and even related health risks to promote better patient outcomes,” Carlson writes.

The use of AI in sleep medicine has grown significantly in recent years. For example, deep neural networks—AI solutions based on a network of connected nodes inspired by the human brain—have been incorporated into some sleep medicine software solutions. This layered network approach helps AI detect patterns to more accurately predict sleep apnea markers and score sleep studies.

According to Agustsson, the most important ingredient for a successful AI application is the data itself. “The quality of the AI heavily relies on the quality of the data used for training and validation,” Agustsson says. “It is important to collect a large and representative dataset that aligns with the intended application of the AI.”

As a result, AI solutions learn best from manual data that provides consensus as well as clear definitions—why AI performs well when scoring sleep stages from EEG, EOG, and EMG data in sleep studies, Agustsson says.

“Generally, AI predicts sleep markers well where there is a good agreement among human scorers,” he says. “Additionally, AI has shown impressive performance in scoring sleep for other sleep disorders like narcolepsy, where consensus among sleep technologists is not as high. The classification of each 30-second period into wake, rapid eye movement (REM) sleep, or one of the three non-REM sleep stages (N1, N2, or N3) makes sleep stage scoring more manageable.”

However, Agustsson adds, AI progress has been focused on language and image processing rather than the unique physiological signals recorded in sleep studies. “These markers pose challenges in training AI models because the scoring of such markers can vary between individual scorers, hospitals, and geographical areas,” he says. “Even a single scorer may exhibit inconsistency in scoring over time, further complicating the learning process for AI.”

For this reason, AI can make errors in detecting respiratory events, desaturation events, arousals, limb movements, and hypopneas. “Refinement of AI algorithms, larger and more diverse training datasets, and continued collaboration between experts and AI developers will contribute to the enhanced capability of AI in accurately detecting these challenging markers,” Agustsson says.

Despite current limitations, AI models positively impact sleep medicine overall by helping to standardize results, according to Agustsson. AI is already making a huge impact on the efficiency and workflows of sleep labs across the globe, he says. “I think it will continue to shape the future of sleep diagnostics as we develop new algorithms that provide new insights and inspire the evolution of our understanding of sleep patterns and physiology.”

For the best outcomes for patients, it is also important for all stakeholders to have open and transparent communication to ensure the safe and effective use of AI, Agustsson notes.

“Overall, the collaboration and synergy between scientists, sleep technologists, medical doctors, and engineers are vital for the development of medical AI algorithms that are clinically relevant, reliable, and impactful in improving patient care in sleep medicine,” he says.

In the next decade, Agustsson predicts that AI will integrate more with electronic medical records. “Predicting patient outcomes and assessing the risk of certain outcomes may become more accurate and accessible through AI algorithms,” he says. “Additionally, AI can uncover subtle patterns and abnormalities in sleep data that might otherwise be challenging to identify, leading to more precise diagnoses.”

To learn more about the rise of AI in the science of treatment of sleep disorders, read the full Sleep Review article by following this link. For more information about the latest research emerging from scientists at Nox Medical, visit our research webpage.

Topic: Industry News