Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population

We aimed to improve the diagnostic accuracy of myocardial perfusion SPECT (MPS) by integrating clinical data and quantitative image features with machine learning (ML) algorithms. 1,181 rest 201Tl/stress 99mTc-sestamibi dual-isotope MPS studies [713 consecutive cases with correlating invasive corona...

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Bibliographic Details
Published in:Journal of nuclear cardiology 2013-08, Vol.20 (4), p.553-562
Main Authors: Arsanjani, Reza, Xu, Yuan, Dey, Damini, Vahistha, Vishal, Shalev, Aryeh, Nakanishi, Rine, Hayes, Sean, Fish, Mathews, Berman, Daniel, Germano, Guido, Slomka, Piotr J.
Format: Article
Language:English
Subjects:
Aged
Algorithms
Artificial Intelligence
automated quantification
Cardiology
Coronary Angiography - methods
coronary artery disease
Coronary Artery Disease - diagnostic imaging
Electrocardiography - methods
Female
Humans
Imaging
machine learning
Male
Medicine
Medicine & Public Health
Middle Aged
Myocardial Ischemia - pathology
Myocardial Perfusion Imaging - methods
Myocardial perfusion imaging: SPECT
Nuclear Medicine
Observer Variation
Original Article
Pattern Recognition, Automated
Perfusion
Radiology
Radiopharmaceuticals
Reproducibility of Results
ROC Curve
Technetium Tc 99m Sestamibi
Tomography, Emission-Computed, Single-Photon - methods
total perfusion deficit
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Summary:We aimed to improve the diagnostic accuracy of myocardial perfusion SPECT (MPS) by integrating clinical data and quantitative image features with machine learning (ML) algorithms. 1,181 rest 201Tl/stress 99mTc-sestamibi dual-isotope MPS studies [713 consecutive cases with correlating invasive coronary angiography (ICA) and suspected coronary artery disease (CAD) and 468 with low likelihood (LLk) of CAD
ISSN:1071-3581
1532-6551
DOI:10.1007/s12350-013-9706-2