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Updated: Aug 14, 2025
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Published on: September 22, 2023
Machine learning based model to diagnose obstructive coronary artery disease using calcium scoring, PET imaging, and
J A van Dalen1, S S Koenders2,3, R J Metselaar2,3
1Department of Medical Physics, Isala Hospital, PO Box 10400, 8000 GK, Zwolle, The Netherlands. jo.van.dalen@isala.nl.
Insights
A new machine learning (ML) model accurately diagnoses obstructive coronary artery disease (CAD) with performance comparable to expert readers. This tool shows promise for improved risk stratification in patients with suspected stable CAD.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Accurate risk stratification is crucial for managing stable coronary artery disease (CAD).
- Machine learning (ML) offers potential for improving diagnostic accuracy in cardiology.
- Obstructive CAD (oCAD) diagnosis requires reliable risk assessment tools.
Purpose of the Study:
- To develop and validate a machine learning (ML) model for diagnosing obstructive coronary artery disease (oCAD).
- To compare the diagnostic performance of the ML model against expert readers.
Main Methods:
- Retrospective analysis of 1007 patients without prior CAD history.
- Utilized CT-based calcium scoring (CACS) and Rubidium-82 PET scan data.
- Developed an ML model using stratified cross-validation; compared performance against expert readers using invasive coronary angiography (ICA) as the gold standard.
Main Results:
- The ML model achieved an AUC of 0.92 on the training set and 0.89 on the test set.
- Diagnostic performance was similar to expert readers, with no significant differences in accuracy (89% vs. 88%), sensitivity (68% vs. 69%), and specificity (92% vs. 90%).
Conclusions:
- The ML model demonstrates comparable diagnostic performance to expert readers for oCAD.
- This ML-based tool holds promise as a risk stratification method for obstructive CAD.
- Machine learning utilization is a promising approach for the diagnosis of obstructive CAD.
Introduction:
Accurate risk stratification in patients with suspected stable coronary artery disease is essential for choosing an appropriate treatment strategy. Our aim was to develop and validate a machine learning (ML) based model to diagnose obstructive CAD (oCAD).
Method:
We retrospectively have included 1007 patients without a prior history of CAD who underwent CT-based calcium scoring (CACS) and a Rubidium-82 PET scan. The entire dataset was split 4:1 into a training and test dataset. An ML model was developed on the training set using fivefold stratified cross-validation. The test dataset was used to compare the performance of expert readers to the model. The primary endpoint was oCAD on invasive coronary angiography (ICA).
Results:
ROC curve analysis showed an AUC of 0.92 (95% CI 0.90-0.94) for the training dataset and 0.89 (95% CI 0.84-0.93) for the test dataset. The ML model showed no significant differences as compared to the expert readers (p ≥ 0.03) in accuracy (89% vs. 88%), sensitivity (68% vs. 69%), and specificity (92% vs. 90%).
Conclusion:
The ML model resulted in a similar diagnostic performance as compared to expert readers, and may be deployed as a risk stratification tool for obstructive CAD. This study showed that utilization of ML is promising in the diagnosis of obstructive CAD.
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