Previously submitted to: JMIR Aging (no longer under consideration since Feb 11, 2025)
Date Submitted: Nov 12, 2024
Open Peer Review Period: Nov 22, 2024 - Jan 17, 2025
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The need for data harmonization, multimodal integration and multi-objective loss functions to identify longitudinal changes in aging
ABSTRACT
Age-related cardiovascular changes, such as arterial stiffening, increased blood pressure, and decreased heart rate variability, contribute to a higher risk of cardiovascular diseases (CVDs), including hypertension, heart failure, atrial fibrillation and stroke. Detecting these changes early is essential for timely intervention and improved outcomes, but current monitoring methods often fall short in detecting subtle age-related changes before overt disease symptoms appear. Non-invasive tools like photoplethysmography (PPG), integrated into wearable devices, offer a promising approach for continuous cardiovascular monitoring. However, significant challenges remain in developing generalizable models that can handle diverse and noisy physiological data from various devices and populations. Addressing these limitations requires advanced, multimodal frameworks that harmonizes data from diverse sources and leverages state-of-the-art deep learning techniques to detect age-related cardiovascular changes.
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