AI Detects Early Stroke Signs at Home: A Revolutionary Breakthrough in Healthcare
The world of healthcare is on the brink of a revolutionary shift, thanks to a groundbreaking development from the Korea Advanced Institute of Science and Technology (KAIST). Researchers at KAIST have developed an AI technology that can detect early signs of cerebrovascular disease, a leading cause of serious aftereffects if treatment is delayed, by analyzing daily activity and environmental data from older adults in their homes.
This innovative approach, detailed in a recent study published in npj Digital Medicine, uses long-term lifelog data collected in real residential environments to identify subtle changes in daily life that may indicate the prodromal phase of cerebrovascular disease. The research team, led by Professor Lisa Lim from the Department of Civil and Environmental Engineering at KAIST, in collaboration with experts from Sungkyunkwan University and Korea University Anam Hospital, has developed an AI framework that can assess the imminent diagnostic risk of cerebrovascular disease.
The study involved analyzing 13,362 two-week lifelog samples from 1,224 older adults, collected by LivOn Care Co., Ltd. in real residential environments. The research team found that subtle changes in daily life, such as irregular daily rhythms and low indoor humidity, can serve as important clues for detecting early risk signals of cerebrovascular disease.
One of the key findings of the study is that older adults in the prodromal phase of cerebrovascular disease tend to show frequent continuous activity between 10 p.m. and 2 a.m., a time when the body would normally be preparing for sleep. This suggests that irregular daily rhythms, such as delayed sleep onset and a reduced distinction between day and night activity, are closely associated with prodromal signals of cerebrovascular disease.
As the time of diagnosis approached, the frequency of continuous activity during the evening period from 6 p.m. to 10 p.m. noticeably decreased, while inactive time increased. Low indoor humidity, indicating a dry indoor environment, also emerged as an important factor in identifying an imminent diagnostic risk.
The research team expects this technology to be used as a digital healthcare tool that can objectively monitor the health status of older adults who may have difficulty clearly describing their own condition, while providing useful early warning indicators to medical professionals and caregivers.
However, the team explained that this study does not predict the exact onset of cerebrovascular disease or replace clinical diagnosis. Rather, it is a supportive technology intended to aid prevention and early medical consultation, and prospective validation in larger patient groups will be necessary before actual clinical application.
Professor Lisa Lim emphasized the importance of this study, stating that the key point is not that AI should replace a hospital diagnosis, but that it can first detect risk signals in small lifestyle changes at home and help connect patients to medical care at the right time. She added that this technology has the potential to contribute to a shift from a healthcare system that treats disease after it occurs to one that supports prevention and early intervention.
This study, with KAIST Dr. Jeongyeop Baek as the first author, was published on June 2 in npj Digital Medicine, a leading international journal in digital healthcare published by Nature Portfolio, with an impact factor of 15.1 and ranked in the top 0.3% of JCR journals.
The research team's work was also supported by the National Research Foundation (NRF) grant funded by the Korea government (Ministry of Science and ICT) (RS-2025-16068234).
This breakthrough in AI-driven healthcare has the potential to revolutionize the way we detect and treat cerebrovascular disease, offering a promising future for early intervention and prevention.
