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CXR CVD-Risk

Massachusetts General Hospital / Harvard Medical School (Weiss, Raghu, Lu, Aerts et al.)

Chest Radiography

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Other Imaging

Major adverse cardiovascular events (MACE)

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Prognosis & Aging

Regression

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Regression

CNN (2D)

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Convolutional (CNN)

PyTorch

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PyTorch

Deep-learning model that estimates a patient's 10-year risk of major adverse cardiovascular events (MACE) directly from a single routine chest radiograph (CXR), intended as an opportunistic risk-assessment tool when the inputs needed for the standard ASCVD risk calculator (lipids, blood pressure, smoking status, etc.) are missing. A 2D convolutional network takes the CXR image alone as input and outputs a continuous 10-year MACE risk estimate. Developed on 147,801 CXRs from 40,718 participants in the PLCO cancer-screening trial and externally validated in 8,869 outpatients with unknown ASCVD risk and 2,132 with known risk at Mass General Brigham, CXR CVD-Risk identified people at elevated MACE risk (adjusted hazard ratio 1.73 for a >=7.5% predicted risk) and provided added discrimination beyond the traditional ASCVD score.

memory Specifications

category

Architecture

CNN (2D)

2D convolutional neural network (fastai/PyTorch, cadene pretrained-model backbone) taking a single chest radiograph as input and regressing a continuous 10-year MACE risk score

code

Framework

PyTorch

calendar_month

Added to catalog

2026-08-13

description Publication

Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs: A Risk Prediction Study open_in_new

Weiss J, Raghu VK, Paruchuri K, Zinzuwadia A, Natarajan P, Aerts HJWL, Lu MT

Annals of Internal Medicine · 2024 · original paper

DOI: 10.7326/M23-1898

database Training & evaluation data

PLCO Cancer Screening Trial Chest Radiographs (CXR CVD-Risk)

train

public 40,718 subjects · USA · 1993-2001

science Capabilities & performance

10-year risk of major adverse cardiovascular events (MACE) estimated from a single chest radiograph

Regression Major adverse cardiovascular events (MACE)