Novel AI-Based Quantification of Breast Arterial Calcification to Predict Cardiovascular Risk
- URL: http://arxiv.org/abs/2503.14550v1
- Date: Mon, 17 Mar 2025 19:38:17 GMT
- Title: Novel AI-Based Quantification of Breast Arterial Calcification to Predict Cardiovascular Risk
- Authors: Theodorus Dapamede, Aisha Urooj, Vedant Joshi, Gabrielle Gershon, Frank Li, Mohammadreza Chavoshi, Beatrice Brown-Mulry, Rohan Satya Isaac, Aawez Mansuri, Chad Robichaux, Chadi Ayoub, Reza Arsanjani, Laurence Sperling, Judy Gichoya, Marly van Assen, Charles W. ONeill, Imon Banerjee, Hari Trivedi,
- Abstract summary: Arterial calcification (BAC) on screening mammography can identify women at risk for cardiovascular disease.<n>A transformer-based neural network quantified BAC severity (no BAC, mild, moderate, and severe) on screening mammograms.
- Score: 6.160906607279526
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Women are underdiagnosed and undertreated for cardiovascular disease. Automatic quantification of breast arterial calcification on screening mammography can identify women at risk for cardiovascular disease and enable earlier treatment and management of disease. In this retrospective study of 116,135 women from two healthcare systems, a transformer-based neural network quantified BAC severity (no BAC, mild, moderate, and severe) on screening mammograms. Outcomes included major adverse cardiovascular events (MACE) and all-cause mortality. BAC severity was independently associated with MACE after adjusting for cardiovascular risk factors, with increasing hazard ratios from mild (HR 1.18-1.22), moderate (HR 1.38-1.47), to severe BAC (HR 2.03-2.22) across datasets (all p<0.001). This association remained significant across all age groups, with even mild BAC indicating increased risk in women under 50. BAC remained an independent predictor when analyzed alongside ASCVD risk scores, showing significant associations with myocardial infarction, stroke, heart failure, and mortality (all p<0.005). Automated BAC quantification enables opportunistic cardiovascular risk assessment during routine mammography without additional radiation or cost. This approach provides value beyond traditional risk factors, particularly in younger women, offering potential for early CVD risk stratification in the millions of women undergoing annual mammography.
Related papers
- Cardiovascular Disease Prediction using Machine Learning: A Comparative Analysis [0.0]
This study involves a cardiovascular disease (CVD) dataset comprising 68,119 records.<n>We have performed statistical analyses, including t-tests, Chi-square tests, and ANOVA, to identify strong associations between CVD and elderly people.<n>A logistic regression model highlights age, blood pressure, and cholesterol as primary risk factors, with unexpected negative associations for smoking and alcohol.
arXiv Detail & Related papers (2025-07-29T15:07:32Z) - 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) - Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach [0.0]
This study employs a hybrid machine learning approach to analyze epidemiological data in assessing 13 key heart attack risk factors and their susceptibility.<n>The study reveals strong association between the likelihood of experiencing a heart attack on the 13 risk factors studied.<n>The aggravated risk for postmenopausal patients indicates compromised individual risk factors due to estrogen depletion.
arXiv Detail & Related papers (2025-05-27T12:51:04Z) - HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks [51.95824566163554]
We introduce HACSurv, a survival analysis method that learns Hierarchical Archimedean Copulas structures.<n>By capturing the dependencies between risks and censoring, HACSurv improves the accuracy of survival predictions.
arXiv Detail & Related papers (2024-10-19T18:52:18Z) - 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) - Predicting Cardiovascular Disease Risk using Photoplethysmography and
Deep Learning [9.273651488255036]
Cardiovascular diseases (CVDs) are responsible for a large proportion of premature deaths in low- and middle-income countries.
Here we investigated the potential to use photoplethysmography (), a sensing technology available on most smartphones.
We developed a deep learning PPG-based CVD risk score (DLS) to predict the probability of having major adverse cardiovascular events.
arXiv Detail & Related papers (2023-05-09T17:46:43Z) - 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) - Predicting cardiovascular risk from national administrative databases
using a combined survival analysis and deep learning approach [0.3463527836552467]
This study compared the performance of deep learning extensions of survival analysis models with traditional Cox proportional hazards (CPH) models.
Deep learning models significantly outperformed CPH models on the basis of proportion of explained time-to-event occurrence.
Deep learning models can be applied to large health administrative databases to derive interpretable CVD risk prediction equations.
arXiv Detail & Related papers (2020-11-28T00:10:25Z) - Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer
Screening Low Dose Computed Tomography [23.614559487371935]
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population.
LDCT for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients.
Deep learning can convert LDCT for lung cancer screening into a dual-screening quantitative tool for CVD risk estimation.
arXiv Detail & Related papers (2020-08-16T21:07:01Z) - An early warning tool for predicting mortality risk of COVID-19 patients
using machine learning [0.0]
A retrospective study was conducted on 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020.
A nomogram was developed for predicting the mortality risk among COVID-19 patients.
An integrated score (LNLCA) was calculated with the corresponding death probability.
arXiv Detail & Related papers (2020-07-29T15:16:09Z) - Prediction of the onset of cardiovascular diseases from electronic
health records using multi-task gated recurrent units [51.14334174570822]
We propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records.
The proposed approach is compared to a standard clinical risk predictor (QRISK) and machine learning alternatives using 5-year data from a NHS Foundation Trust.
arXiv Detail & Related papers (2020-07-16T17:43:13Z) - Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist
Clinicians with COVID-19 ECMO Planning [26.25177784899079]
Respiratory complications due to coronavirus disease COVID-19 have claimed tens of thousands of lives in 2020.
Many cases escalate from Severe Acute Respiratory Syndrome (SARS-CoV-2) to viral pneumonia to acute respiratory distress syndrome (ARDS) to death.
Extracorporeal membranous oxygenation (ECMO) is a life-sustaining oxygenation and ventilation therapy that may be used for patients with severe ARDS when mechanical ventilation is insufficient to sustain life.
arXiv Detail & Related papers (2020-06-02T19:30:29Z) - Automated Quantification of CT Patterns Associated with COVID-19 from
Chest CT [48.785596536318884]
The proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions.
The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities.
Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States.
arXiv Detail & Related papers (2020-04-02T21:49:14Z)
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.