[의학신문·일간보사=정광성 기자] A team of domestic teachers is attracting attention by developing an AI-based wearable device model that can screen children for ADHD and sleep disorders early.
Korea University Anam Hospital, Department of Psychiatry, Professor Cheol-Hyun ChoThe team announced on the 28th that it has identified the possibility of an AI model that can screen attention deficit hyperactivity disorder and sleep disorder early through a wearable device in collaboration with ‘Luman Lab’, a digital healthcare company for babies and toddlers .
According to the professor’s team, early diagnosis of attention deficit hyperactivity disorder (ADHD) and sleep disorders in children is very important for the development of children’s mental health and growth.
However, it is difficult to screen early in daily life, and the current diagnosis method through interviews and questionnaires has limitations, so the need for a more convenient and objective early screening technology in daily life is coming to the obvious
Accordingly, the professor’s team used the children’s wearable data and the results of ADHD and sleep disorder diagnoses accumulated through research on adolescent brain and cognitive development conducted in the United States.
Specifically, 21 days of wearable data from 5725 children, such as heart rate, number of steps, sleep time, sleep periods, napping, and calories burned, based on circadian rhythm, and 12,348 data were analyzed for a diagnosis model ADHD, sleep disorder diagnosis. 39,160 data points were used for the model.
As a result of the study, the AUC (the closer to 1, the higher the performance), which evaluates the performance of the ADHD diagnostic model, was 0.798, the sensitivity 0.756, and the specificity 0.716. The specificity was 0.632.
Both models showed performance at a level that enabled early screening using digital phenotypes in everyday life, and the professor’s team explained that this provides the basis for early detection and treatment of ADHD and sleep disorders in children through wearable data.
Professor Chul-Hyun Cho said, “As it is a machine learning diagnosis model that uses digital phenotypes found in everyday life, it will be easy, objective, and early screening and intervention will be possible.” Familiarity with and use of digital devices is increasing. , which will lead to therapeutic effects when associated with personalized digital therapy services in the future.”
Meanwhile, this study was published in the JAMA Network Open (IF = 13.37), the academic journal of the American Medical Association.
