Ability of artificial intelligence to diagnose coronary artery stenosis using hybrid images of coronary computed tomography angiography and myocardial perfusion SPECT

Abstract

Background 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. Methods 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. Results 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. Conclusions 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.

Deep Learning Analysis of Polar Maps from SPECT Myocardial Perfusion Imaging for Prediction of Coronary Artery Disease

“…Visual assessment of SPECT heart perfusion images faces several challenges, including the lack of reproducibility due to the intra-observer and interobserver variability, dependency on nuclear medicine expert or radiologist experience, and increased time and cost of the interpretation process. Machine learning and Deep learning techniques exhibited promising potential for the detection and classi cation of coronary artery disease from SPECT MPI images compared with other approaches, including human observer diagnosis and quantitative analysis of perfusion defects [4,6,[9][10][11][12][13][14][15][18][19][20][21]. A comparison to the related studies is listed in Table 5.…”

Artificial intelligence in atherosclerotic disease: Applications and trends

“…For example, Kang et al developed a ML algorithm that identified coronary stenosis of 25% or more with an accuracy of 94% compared to visual identification of lesions with stenosis by expert readers using consensus reading (46). Yoneyama et al developed a neural network for the detection of coronary stenoses from CTA images that cause perfusion defects on single-photon emission CT, achieving comparable results with physician experts (47). Several studies have reported neural networks that automatically grade CAD severity from CTA images according to the Coronary Artery Disease Reporting and Data System (CAD-RADS) (48)(49)(50)(51), with satisfactory results (46,47,50).…”

Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis

“…Yoneyama et al evaluated the possibility to identify the grading of coronary stenosis and its impact in terms of ischemia using a cohort of patients who underwent CCTA and perfusion single photon emission computed tomography (SPECT) [ 37 ]. The authors focused on the application of an artificial neural network (ANN) with hybrid imaging obtained by the combination of CCTA and myocardial perfusion SPECT [ 37 ]. Using this algorithm, the specificity, sensitivity, and accuracy to identify coronary artery stenosis >70% were 31%, 78%, and 67%, respectively [ 37 ].…”

“…The authors focused on the application of an artificial neural network (ANN) with hybrid imaging obtained by the combination of CCTA and myocardial perfusion SPECT [ 37 ]. Using this algorithm, the specificity, sensitivity, and accuracy to identify coronary artery stenosis >70% were 31%, 78%, and 67%, respectively [ 37 ].…”