The Korea Advanced Institute of Science and Technology (KAIST), Sungkyunkwan University, and Korea University Anam Hospital analyzed real-world lifelog data from 1,224 older adults, identifying imminent diagnostic risk of cerebrovascular disease with 96.5% accuracy.
The research team has developed an AI framework that uses long-term lifelog data collected in the homes of older adults to identify the prodromal phase of cerebrovascular disease and assess imminent diagnostic risk.
The research team developed AI technology that identifies cerebrovascular disease risk stages by analyzing daily activity, sleep, circadian rhythm, and indoor environmental information, together with age and chronic disease data.
This shows that changes in everyday living patterns, which are difficult to capture through hospital examinations alone, can serve as important clues for detecting early risk signals of cerebrovascular disease.
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