Why the study?
Predicting obstructive atherosclerotic disease has significant clinical meaning for decision making.
Does a machine learning predictive model combining imaging and non-imaging data accurately predict CAD risk in patients with suspected CAD?
Does a machine learning predictive model combining imaging and non-imaging data accurately predict CAD risk in patients with suspected CAD?
A machine learning model combining imaging and non-imaging data achieved an overall predictive accuracy of 0.81 for identifying CAD risk in patients with suspected coronary artery disease.
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May support CAD risk stratification in suspected patients; hypothesis-generating for ML models integrating imaging and non-imaging data.
Kigka et al. (2022) studied this question.
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