AI Model Uses ECG Data to Predict Cognitive Decline and Biological Aging
TL;DR
Early detection of premature aging and cognitive decline through AI and ECG data provides a competitive advantage in maintaining cognitive health.
AI model analyzes ECG data to predict biological age, revealing insights into aging and health status at the tissue level.
Using ECG data and AI to assess cognitive performance could lead to early diagnosis, timely intervention, and improved quality of life.
ECG-age linked to cognitive performance highlights the potential of AI in predicting future cognitive decline, leading to valuable treatments and improved brain health.
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Researchers have developed an artificial intelligence model that uses electrocardiogram (ECG) data to predict biological aging and its potential correlation with cognitive performance. The study, to be presented at the American Stroke Association's International Stroke Conference in 2025, analyzed data from over 63,000 UK Biobank participants to explore the relationship between ECG-derived biological age and cognitive function.
The AI model, termed a deep neural network (DNN), examined ECG data to estimate participants' biological age, distinguishing it from chronological age. By categorizing participants into groups of normal, accelerated, and decelerated aging, researchers discovered significant cognitive performance variations across these groups.
Participants with ECG ages younger than their chronological age performed better on six of eight cognitive tests, while those with accelerated ECG aging demonstrated worse cognitive test scores. This finding suggests a potential link between heart health indicators and cognitive capabilities.
Lead researcher Bernard Ofosuhene emphasized the study's significance, noting that ECG-age reflects the functional status of the heart and potentially the entire organism at the tissue level. The research opens promising avenues for early detection of cognitive decline using readily available medical data.
However, the study acknowledges several limitations. The analysis focused on participants aged 43-85 of predominantly European descent, which may restrict the generalizability of results. Future research aims to investigate potential gender differences and explore the findings' applicability across diverse populations.
Neurologist Fernando D. Testai highlighted the study's potential implications, suggesting that ECG data collected in medical offices or through wearable devices could provide an accessible, quick, and objective method for assessing cognitive health, particularly in areas with limited neuropsychiatric specialist access.
While preliminary, this research represents a significant step in understanding the intricate connections between heart health, aging, and cognitive function, potentially offering new diagnostic and preventive strategies for age-related cognitive decline.
Curated from NewMediaWire
