The Sound of Death: Deep Learning Reveals Vascular Damage from Carotid Ultrasound

TL;DR

This study addresses the limited ability of current diagnostics to detect early cardiovascular risk by leveraging routinely acquired carotid ultrasound videos. A transformer-based framework learns vascular-damage representations ($VD$) using hypertension as a noisy proxy label, uncovering biologically plausible, interpretable features that correlate with cardiovascular risk factors and adverse events. The $VD$ marker stratifies risk for myocardial infarction, stroke, cardiac death, and all-cause mortality, performing on par with or surpassing SCORE2 in several endpoints, and providing additive value when combined with SCORE2 in hazard models. Explainable AI reveals that vessel-wall morphology and surrounding perivascular tissue drive predictions, suggesting the ultrasound modality contains rich prognostic information beyond conventional metrics and offering a scalable, non-invasive tool for population-level cardiovascular risk assessment.

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, yet early risk detection is often limited by available diagnostics. Carotid ultrasound, a non-invasive and widely accessible modality, encodes rich structural and hemodynamic information that is largely untapped. Here, we present a machine learning (ML) framework that extracts clinically meaningful representations of vascular damage (VD) from carotid ultrasound videos, using hypertension as a weak proxy label. The model learns robust features that are biologically plausible, interpretable, and strongly associated with established cardiovascular risk factors, comorbidities, and laboratory measures. High VD stratifies individuals for myocardial infarction, cardiac death, and all-cause mortality, matching or outperforming conventional risk models such as SCORE2. Explainable AI analyses reveal that the model relies on vessel morphology and perivascular tissue characteristics, uncovering novel functional and anatomical signatures of vascular damage. This work demonstrates that routine carotid ultrasound contains far more prognostic information than previously recognized. Our approach provides a scalable, non-invasive, and cost-effective tool for population-wide cardiovascular risk assessment, enabling earlier and more personalized prevention strategies without reliance on laboratory tests or complex clinical inputs.

Figures (7)

• Figure 1: Classification accuracy by age at initial assessment. Left: male participants; right: female participants. Dark colours denote correct classifications, with true positives (high vascular damage (VD) and hypertensive) shown in red and true negatives (low VD and non-hypertensive) in blue. Light shades indicate misclassifications, specifically false positives (light red; high VD and non-hypertensive) and false negatives (light blue; low VD and non-hypertensive). Overall, classification patterns between males and females are comparable above 55 years of age. However, among younger individuals (<45 years), males exhibit a higher proportion of false negatives, accompanied by a reduced rate of true negatives compared with females.
• Figure 2: Average, median, top-5% positive and negative regions of interest (ROIs) of 50 videos. ROIs highlight which regions have been found most important for the model to predict an example as high vascular damage (green) or as low vascular damage (red). Attributions and background images have been averaged over 50 videos, selected around the global dataset mean. Progress in course of the different assessments is illustrated per row. In all cases the vessel walls are rendered most important. However, in particular in older ages (e.g. 10-year follow-up), ROIs are in particular pronounced in the perivascular tissue.
• Figure 3: Comparison of clinical parameters in the Gutenberg Health Study cohort. Higher values generally indicate poorer cardiovascular status. Individuals with high vascular damage, irrespective of hypertension, consistently exhibit the poorest overall condition.
• Figure 4: Kaplan–Meier estimates for vascular damage (VD). Top: (Event-free) survival for incident myocardial infarction, stroke, and cardiac death over a 5-year period (left) and for cardiac and all-cause death over 10 years (right), comparing participants with high VD (solid lines) versus low VD (dashed lines). Bottom: 15-year all-cause death stratified by VD and hypertension label used during model training (left) and by VD versus SCORE2 baseline risk model (right). VD consistently outperformed hypertension status. High VD at the predefined threshold of $0.67$ exhibited risk-stratification characteristics, closely comparable to a high SCORE2.
• Figure 5: Cox proportional hazards models incorporating different covariates. (a) Hazard ratios (left) and corresponding concordance and significance (right) The shaded regions highlight the difference between the lowest and highest C-index. Combining high and low vascular damage score and SCORE2, shows the best performance. (b) Differences in concordance index (C-index) (left) and statistical significance (right).