Work

Machine learning and wearable sensors in early cognitive change detection in CADASIL

Year: 2025

Type: article

Abstract: Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) is a hereditary small vessel disease that often leads to progressive cognitive decline. Early detection of cognitive changes remains a clinical challenge, as conventional neuroimaging and neuropsychological assessments may lack sensitivity in the prodromal phase. Recent advances in machine learning and wearable sensor technologies offer promising possibilities for noninvasive, real-time monitoring of subtle cognitive shifts. By analyzing multimodal data, such as gait patterns, speech dynamics, sleep metrics, and physiological signals, machine learning algorithms can identify predictive markers of early cognitive impairment in CADASIL patients. This approach enables continuous, personalized assessment outside clinical settings, potentially transforming disease monitoring and intervention timing. We propose integrating wearable-based digital phenotyping with artificial intelligence-driven analytics to enhance early detection, risk stratification, and longitudinal tracking in CADASIL care.

Source: Annals of Medicine and Surgery

DOI: 10.1097/ms9.0000000000004346

Authors: Erum Habib, Fatima Hajj

Institutions: Dow University of Health Sciences, Lebanese University

Language: English

Topic: Cerebrovascular and genetic disorders

Subfield: Neurology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,850