Ability of artificial intelligence to diagnose coronary artery stenosis using hybrid images of coronary computed tomography angiography and myocardial perfusion ... View Full Text


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Article Info

DATE

2019-12

AUTHORS

Hiroto Yoneyama, Kenichi Nakajima, Junichi Taki, Hiroshi Wakabayashi, Shinro Matsuo, Takahiro Konishi, Koichi Okuda, Takayuki Shibutani, Masahisa Onoguchi, Seigo Kinuya

ABSTRACT

Detecting culprit coronary arteries in patients with ischemia using only myocardial perfusion single-photon emission computed tomography (SPECT) can be challenging. This study aimed to improve the detection of culprit regions using an artificial neural network (ANN) to analyze hybrid images of coronary computed tomography angiography (CCTA) and myocardial perfusion SPECT. This study enrolled 59 patients with stable coronary artery disease (CAD) who had been assessed by coronary angiography within 60 days of myocardial perfusion SPECT. Two nuclear medicine physicians interpreted the myocardial perfusion SPECT and hybrid images with four grades of confidence, then drew regions on polar maps to identify culprit coronary arteries. The gold standard was determined by the consensus of two other nuclear cardiology specialist based on coronary angiography findings and clinical information. The ability to detect culprit coronary arteries was compared among experienced nuclear cardiologists and the ANN. Receiver operating characteristics (ROC) curves were analyzed and areas under the ROC curves (AUC) were determined. Using hybrid images, observer A detected CAD in the right (RCA), left anterior descending (LAD), and left circumflex (LCX) coronary arteries with 83.6%, 89.3%, and 94.4% accuracy, respectively and observer B did so with 72.9%, 84.2%, and 89.3%, respectively. The ANN was 79.1%, 89.8%, and 89.3% accurate for each coronary artery. Diagnostic accuracy was comparable between the ANN and experienced nuclear medicine physicians. The AUC was significantly improved using hybrid images in the RCA region (observer A: from 0.715 to 0.835, p = 0.0031; observer B: from 0.771 to 0.843, p = 0.042). To detect culprit coronary arteries in perfusion defects of the inferior wall without using hybrid images was problematic because the perfused areas of the LCX and RCA varied among individuals. Hybrid images of CCTA and myocardial perfusion SPECT are useful for detecting culprit coronary arteries. Diagnoses using artificial intelligence are comparable to that by nuclear medicine physicians. More... »

PAGES

4

References to SciGraph publications

  • 2010-06. An open-source framework of neural networks for diagnosis of coronary artery disease from myocardial perfusion SPECT in JOURNAL OF NUCLEAR CARDIOLOGY
  • 2014-06. Computer-aided diagnosis system outperforms scoring analysis in myocardial perfusion imaging in JOURNAL OF NUCLEAR CARDIOLOGY
  • 2017-12. Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2015-10. Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population in JOURNAL OF NUCLEAR CARDIOLOGY
  • 2016-06. ASNC imaging guidelines for SPECT nuclear cardiology procedures: Stress, protocols, and tracers in JOURNAL OF NUCLEAR CARDIOLOGY
  • 2009-06. The importance of population-specific normal database for quantification of myocardial ischemia: comparison between Japanese 360 and 180-degree databases and a US database in JOURNAL OF NUCLEAR CARDIOLOGY
  • 2018-01. Feasibility of combined risk stratification with coronary CT angiography and stress myocardial SPECT in patients with chronic coronary artery disease in ANNALS OF NUCLEAR MEDICINE
  • 2007-11. Creation and characterization of Japanese standards for myocardial perfusion SPECT: database from the Japanese Society of Nuclear Medicine Working Group in ANNALS OF NUCLEAR MEDICINE
  • 2018-06. Artificial neural network retrained to detect myocardial ischemia using a Japanese multicenter database in ANNALS OF NUCLEAR MEDICINE
  • 2013-08. Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population in JOURNAL OF NUCLEAR CARDIOLOGY
  • 2010-02. The validity of multi-center common normal database for identifying myocardial ischemia: Japanese Society of Nuclear Medicine working group database in ANNALS OF NUCLEAR MEDICINE
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    35 schema:description Detecting culprit coronary arteries in patients with ischemia using only myocardial perfusion single-photon emission computed tomography (SPECT) can be challenging. This study aimed to improve the detection of culprit regions using an artificial neural network (ANN) to analyze hybrid images of coronary computed tomography angiography (CCTA) and myocardial perfusion SPECT. This study enrolled 59 patients with stable coronary artery disease (CAD) who had been assessed by coronary angiography within 60 days of myocardial perfusion SPECT. Two nuclear medicine physicians interpreted the myocardial perfusion SPECT and hybrid images with four grades of confidence, then drew regions on polar maps to identify culprit coronary arteries. The gold standard was determined by the consensus of two other nuclear cardiology specialist based on coronary angiography findings and clinical information. The ability to detect culprit coronary arteries was compared among experienced nuclear cardiologists and the ANN. Receiver operating characteristics (ROC) curves were analyzed and areas under the ROC curves (AUC) were determined. Using hybrid images, observer A detected CAD in the right (RCA), left anterior descending (LAD), and left circumflex (LCX) coronary arteries with 83.6%, 89.3%, and 94.4% accuracy, respectively and observer B did so with 72.9%, 84.2%, and 89.3%, respectively. The ANN was 79.1%, 89.8%, and 89.3% accurate for each coronary artery. Diagnostic accuracy was comparable between the ANN and experienced nuclear medicine physicians. The AUC was significantly improved using hybrid images in the RCA region (observer A: from 0.715 to 0.835, p = 0.0031; observer B: from 0.771 to 0.843, p = 0.042). To detect culprit coronary arteries in perfusion defects of the inferior wall without using hybrid images was problematic because the perfused areas of the LCX and RCA varied among individuals. Hybrid images of CCTA and myocardial perfusion SPECT are useful for detecting culprit coronary arteries. Diagnoses using artificial intelligence are comparable to that by nuclear medicine physicians.
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