The Stanford researchers first told six techs to analyze and score sleep data, looking for changes in sleep staging that could indicate narcolepsy. “Right now is done by technicians, and clearly, there is no reason why it couldn’t be done by a computer,” says sleep specialist Emmanuel Mignot, MD, PhD, an author of the study and the director of the Stanford Center for Sleep Sciences and Medicine. Results were published in a 2018 paper in Nature Communications. For instance, researchers from the Stanford Center for Sleep Sciences and Medicine developed an AI system to analyze sleep stages to diagnose narcolepsy, finding that AI could use datasets to pinpoint unusual sleep staging more accurately than a human sleep tech. “Our goal is to bring valuable information to physicians early, so it can be used for proactive patient care management,” says Raj Misra, PhD, chief data scientist and vice president of marketing at Somnoware.ĪI could also change the diagnostic process. This way, clinicians can track trends in compliance and determine the likely impact of certain interventions on patient outcomes. This data includes demographic information and comorbidities, which all help to build a machine learning model to predict short- and long-term CPAP compliance even before the patient is put on therapy, according to a release.Īs more data comes into the program, the model automatically updates its predictions. The software works by mining patient data from electronic medical records, patient questionnaires, and lab visits. 1Īnother healthcare tech company, Somnoware, has deployed a cloud-based platform that uses machine learning to help physicians estimate the likelihood of 90-day CPAP therapy compliance. Nox researchers are making progress on automated scoring of arousals, and their results from the latest version of the arousal scorer will be presented as an abstract at SLEEP 2019. This is much more difficult to deal with than classifying each 30-second epoch.” To make things even worse is that arousals can occur at any time and can last for various amounts of time. In this case it is difficult for the AI tool to learn what is an arousal (since it learns from the data it sees), and it is difficult to measure the performance of the AI tool since it will not always agree with the human-scored labels. “To further complicate things, when arousals are typically scored there is usually not concern with exact timing….The timing of the manually scored event may vary by multiple seconds from the actual timing of the event. Arousals are only a small part of EEG signals. An example of this is AI to detect arousals in EEG. “The identification of arousals is important for the diagnosis of sleep disorders, but manual scoring is time consuming and requires expertise,” says Halla Helgadottir, co-director of Nox Research.Īgustsson adds, “A very difficult project to apply AI to is detecting rare events, which either are not well defined, where humans do not agree well on the scoring, or the scored labels do not overlap exactly over the periods where the event occurs. The Nox researchers are also working on AI tools to detect arousals during sleep. “We see this as an opportunity to empower the sleep techs and doctors to improve diagnosis or serve larger patient populations without adding to their workload,” says Jon S. Since the program began, Nox has released an AI-powered automatic sleep detector as part of the company’s sleep analysis software Noxturnal. One Iceland-based sleep technology company, Nox Medical, created a branch in 2015 called Nox Research, which focuses on developing AI tools to automate sleep study scoring and extract new insights from sleep study data. This is why sleep technology companies across the globe are taking the initiative to develop new programs that will one day make artificial intelligence (AI) a mainstay in sleep clinics. “I think is a field that is ripe for disruption in really being able to apply big data and big data tools to our field and perhaps even be a leading force in terms of how machine learning and artificial intelligence can really impact patients in the way that we deliver care,” says sleep specialist Dennis Hwang, MD, director of Kaiser Permanente’s San Bernardino County Sleep Center. Machine learning could revolutionize sleep medicine by taking over the diagnostic process, identifying gaps in care, and helping predict CPAP adherence even before therapy begins. Right now it’s typical for sleep technologists to mull over pages of polysomnography (PSG) data related to eye movements, respiration, brain activity, and more, to look for indicators of sleep disorders, such as sleep apnea or narcolepsy. How will AI and machine learning change how sleep medicine providers treat their patients?
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |