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

J.A. van Dalen*, S.S. Koenders, R.J. Metselaar, B.N. Vendel, D.J. Slotman, M. Mouden, C.H. Slump, J.D. van Dijk

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
57 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)1504-1513
Number of pages10
JournalJournal of nuclear cardiology
Volume30
Issue number4
Early online date9 Jan 2023
DOIs
Publication statusPublished - Aug 2023

Keywords

  • 2023 OA procedure
  • Machine learning
  • PET myocardial perfusion imaging
  • Coronary artery disease

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