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

Authors :
van Dalen, J. A.
Koenders, S. S.
Metselaar, R. J.
Vendel, B. N.
Slotman, D. J.
Mouden, M.
Slump, C. H.
van Dijk, J. D.
Source :
Journal of Nuclear Cardiology; Aug2023, Vol. 30 Issue 4, p1504-1513, 10p
Publication Year :
2023

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10713581
Volume :
30
Issue :
4
Database :
Complementary Index
Journal :
Journal of Nuclear Cardiology
Publication Type :
Academic Journal
Accession number :
167307661
Full Text :
https://doi.org/10.1007/s12350-022-03166-3