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