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

• URL: http://arxiv.org/abs/2602.17321v1
• Date: Thu, 19 Feb 2026 12:37:48 GMT
• Title: The Sound of Death: Deep Learning Reveals Vascular Damage from Carotid Ultrasound
• Authors: Christoph Balada, Aida Romano-Martinez, Payal Varshney, Vincent ten Cate, Katharina Geschke, Jonas Tesarz, Paul Claßen, Alexander K. Schuster, Dativa Tibyampansha, Karl-Patrik Kresoja, Philipp S. Wild, Sheraz Ahmed, Andreas Dengel,
• Abstract summary: We present a machine learning framework that extracts clinically meaningful representations of vascular damage from carotid ultrasound videos.<n>Our approach provides a scalable, non-invasive, and cost-effective tool for population-wide cardiovascular risk assessment.
• Score: 31.950422391211607
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• VAMPIRE: Uncovering Vessel Directional and Morphological Information from OCTA Images for Cardiovascular Disease Risk Factor Prediction [7.586356391440063]
  We propose a novel multi-purpose paradigm of CVD risk assessment that jointly performs CVD risk and CVD-related condition prediction.<n>We introduce OCTA-CVD, the first OCTA dataset for CVD risk assessment, and a Vessel-Aware Mamba-based Prediction model with Informative Enhancement (VAMPIRE) based on OCTA enface images.
  arXiv  Detail & Related papers  (2025-07-26T17:12:44Z)
• MOSCARD -- Causal Reasoning and De-confounding for Multimodal Opportunistic Screening of Cardiovascular Adverse Events [3.206697649226124]
  Major Adverse Cardiovascular Events (MACE) remain the leading cause of mortality globally, as reported in the Global Disease Study 2021.<n>Opportunistic screening leverages data collected from routine health check-ups and multimodal data can play a key role to identify at-risk individuals.<n>We propose a novel predictive modeling framework - MOSCARD, multimodal causal reasoning with co-attention to align two distinct modalities and simultaneously mitigate bias and confounders in opportunistic risk estimation.
  arXiv  Detail & Related papers  (2025-06-23T22:28:37Z)
• Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models [70.64969663547703]
  AdaCVD is an adaptable CVD risk prediction framework built on large language models extensively fine-tuned on over half a million participants from the UK Biobank.<n>It addresses key clinical challenges across three dimensions: it flexibly incorporates comprehensive yet variable patient information; it seamlessly integrates both structured data and unstructured text; and it rapidly adapts to new patient populations using minimal additional data.
  arXiv  Detail & Related papers  (2025-05-30T14:42:02Z)
• Deep Learning for Cardiovascular Risk Assessment: Proxy Features from Carotid Sonography as Predictors of Arterial Damage [34.74294456536736]
  The VideoMAE deep learning model was adapted by finetuning for application in the domain of ultrasound imaging.<n>The model was trained and tested using a dataset comprising over 31,000 carotid sonography videos sourced from the Gutenberg Health Study.<n>We demonstrate that our machine learning model effectively captures visual features that provide valuable insights into an individual's overall cardiovascular health.
  arXiv  Detail & Related papers  (2025-04-09T08:38:17Z)
• Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers [43.17768785084301]
  We train an amortized neural posterior estimator on a newly built large dataset of cardiac simulations.<n>We incorporate elements modeling effects to better align simulated data with real-world measurements.<n>The proposed framework can further integrate in-vivo data sources to refine its predictive capabilities on real-world data.
  arXiv  Detail & Related papers  (2024-12-23T13:05:17Z)
• A Joint Representation Using Continuous and Discrete Features for Cardiovascular Diseases Risk Prediction on Chest CT Scans [12.652540031719571]
  We propose a novel joint representation that integrates discrete quantitative biomarkers and continuous deep features extracted from chest CT scans. Our method substantially improves CVD risk predictive performance and offers individual contribution analysis of each biomarker.
  arXiv  Detail & Related papers  (2024-10-24T10:06:45Z)
• Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction [47.7045293755736]
  Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients' quality of life. This study demonstrates the potential of retinal optical coherence tomography ( OCT) imaging combined with fundus photographs for identifying future adverse cardiac events. We propose a novel binary classification network based on a Multi-channel Variational Autoencoder (MCVAE), which learns a latent embedding of patients' fundus and OCT images to classify individuals into two groups: those likely to develop CVD in the future and those who are not.
  arXiv  Detail & Related papers  (2024-10-18T12:37:51Z)
• Interpretable Survival Analysis for Heart Failure Risk Prediction [50.64739292687567]
  We propose a novel survival analysis pipeline that is both interpretable and competitive with state-of-the-art survival models. Our pipeline achieves state-of-the-art performance and provides interesting and novel insights about risk factors for heart failure.
  arXiv  Detail & Related papers  (2023-10-24T02:56:05Z)
• CARNA: Characterizing Advanced heart failure Risk and hemodyNAmic phenotypes using learned multi-valued decision diagrams [6.599394944440605]
  CARNA is a hemodynamic risk stratification and phenotyping framework for advanced heart failure. It takes advantage of the explainability and expressivity of machine learned Multi-Valued Decision Diagrams (MVDDs) It incorporates invasive hemodynamics and can make predictions on missing data.
  arXiv  Detail & Related papers  (2023-06-11T22:56:59Z)
• Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients [42.09584755334577]
  Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis to quantify the mortality risks of lung cancer patients. We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the SCAD penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model.
  arXiv  Detail & Related papers  (2023-03-09T15:38:16Z)
• SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Deep Vein Thrombosis (DVT) prediction [68.8204255655161]
  We propose a generative time-to-event model, SurvLatent ODE, which parameterizes a latent representation under irregularly sampled data. Our model then utilizes the latent representation to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function. SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying DVT risk groups.
  arXiv  Detail & Related papers  (2022-04-20T17:28:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.