Deep Learning for Cardiovascular Risk Assessment: Proxy Features from Carotid Sonography as Predictors of Arterial Damage
This work addresses early detection of cardiovascular events for patients, but it is incremental as it applies an existing method to a new medical dataset.
The study tackled cardiovascular risk assessment by adapting the VideoMAE deep learning model to classify hypertension from carotid sonography videos, achieving 75.7% validation accuracy as a proxy for arterial damage.
In this study, hypertension is utilized as an indicator of individual vascular damage. This damage can be identified through machine learning techniques, providing an early risk marker for potential major cardiovascular events and offering valuable insights into the overall arterial condition of individual patients. To this end, the VideoMAE deep learning model, originally developed for video classification, was adapted by finetuning for application in the domain of ultrasound imaging. The model was trained and tested using a dataset comprising over 31,000 carotid sonography videos sourced from the Gutenberg Health Study (15,010 participants), one of the largest prospective population health studies. This adaptation facilitates the classification of individuals as hypertensive or non-hypertensive (75.7% validation accuracy), functioning as a proxy for detecting visual arterial damage. We demonstrate that our machine learning model effectively captures visual features that provide valuable insights into an individual's overall cardiovascular health.