Massachusetts General Hospital / Harvard Medical School (Weiss, Raghu, Lu, Aerts et al.)
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.
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
Framework
PyTorch
Added to catalog
2026-08-13
PLCO Cancer Screening Trial Chest Radiographs (CXR CVD-Risk)
10-year risk of major adverse cardiovascular events (MACE) estimated from a single chest radiograph