Machine learning based model to diagnose obstructive coronary artery disease using calcium scoring, PET imaging, and clinical data

J Nucl Cardiol. 2023 Aug;30(4):1504-1513. doi: 10.1007/s12350-022-03166-3. Epub 2023 Jan 9.

Abstract

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.

Keywords: Coronary artery disease; Machine learning; PET myocardial perfusion imaging.

MeSH terms

  • Calcium
  • Computed Tomography Angiography / methods
  • Coronary Angiography / methods
  • Coronary Artery Disease*
  • Humans
  • Machine Learning
  • Positron-Emission Tomography / methods
  • Predictive Value of Tests
  • Retrospective Studies

Substances

  • Calcium