Accuracy of automated software-guided detection of significant coronary artery stenosis by CT angiography: comparison with invasive catheterisation

Automated Identification of Coronary Arteries in Assisting Inexperienced Readers: Comparison between Two Commercial Vendors

Background: to assess the performance and speed of two commercially available advanced cardiac software packages in the automated identification of coronary vessels as an aiding tool for inexperienced readers. Methods: Hundred and sixty patients undergoing coronary CT angiography (CCTA) were prospectively enrolled from February until September 2021 and randomized in two groups, each one composed by 80 patients. Patients in group 1 were scanned on Revolution EVO CT Scanner (GE Healthcare), while patients in group 2 had the CCTA performed on Brilliance iCT (Philips Healthcare); each examination was evaluated on the respective vendor proprietary advanced cardiac software (software 1 and 2, respectively). Two inexperienced readers in cardiac imaging verified the software performance in the automated identification of the three major coronary vessels: (RCA, LCx, and LAD) and in the number of identified coronary segments. Time of analysis was also recorded. Results: software 1 correctly and automatically nominated 202/240 (84.2%) of the three main coronary vessels, while software 2 correctly identified 191/240 (79.6%) (p = 0.191). Software 1 achieved greater performances in recognizing the LCx (81.2% versus 67.5%; p = 0.048), while no differences have been reported in detecting the RCA (p = 0.679), and the LAD (p = 0.618). On a per-segment analysis, software 1 outperformed software 2, automatically detecting 942/1062 (88.7%) coronary segments, while software 2 detected 797/1078 (73.9%) (p < 0.001). Average reconstruction and detection time was of 13.8 s for software 1 and 21.9 s for software 2 (p < 0.001). Conclusions: automated cardiac software packages are a reliable and time-saving tool for inexperienced reader. Software 1 outperforms software 2 and might therefore better assist inexperienced CCTA readers in automated identification of the three main vessels and coronaries segments, with a consistent time saving of the reading session.

Do plaque-related factors affect the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system? Comparison with invasive coronary angiography

OBJECTIVE

The aim of this study was to investigate the effects of plaque-related factors on the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system (AI-CADS).

METHODS

Patients who underwent coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) were retrospectively included in this study. The degree of stenosis in each vessel was collected from CCTA and ICA, and the information on plaque-related factors (plaque length, plaque type, and coronary artery calcium score (CAC)) of the vessels with plaques was collected from CCTA.

RESULTS

In total, 1224 vessels in 306 patients (166 men; 65.7 ± 10.1 years) were analyzed. Of these, 391 vessels in 249 patients showed significant stenosis using ICA as the gold standard. Using per-vessel as the unit, the area under the curves of coronary stenosis ≥ 50% for AI-CADS, doctor, and AI-CADS + doctor was 0.764, 0.837, and 0.853, respectively. The accuracies in interpreting the degree of coronary stenosis were 56.0%, 68.1%, and 71.2%, respectively. Seven hundred fifty vessels showed plaques on CCTA; plaque type did not affect the interpretation results by AI-CADS (chi-square test: p = 0.0093; multiple logistic regression: p = 0.4937). However, the interpretation results for plaque length (chi-square test: p < 0.0001; multiple logistic regression: p = 0.0061) and CACs (chi-square test: p < 0.0001; multiple logistic regression: p = 0.0001) were significantly different.

CONCLUSION

AI-CADS has an ability to distinguish ≥ 50% coronary stenosis, but additional manual interpretation based on AI-CADS is necessary. The plaque length and CACs will affect the diagnostic performance of AI-CADS.

KEY POINTS

• AI-CADS can help radiologists quickly assess CCTA and improve diagnostic confidence. • Additional manual interpretation on the basis of AI-CADS is necessary. • The plaque length and CACs will affect the diagnostic performance of AI-CADS.

Deep learning analysis in coronary computed tomographic angiography imaging for the assessment of patients with coronary artery stenosis

BACKGROUND AND OBJECTIVE

Recently, deep convolutional neural network has significantly improved image classification and image segmentation. If coronary artery disease (CAD) can be diagnosed through machine learning and deep learning, it will significantly reduce the burdens of the doctors and accelerate the critical patient diagnoses. The purpose of the study is to assess the practicability of utilizing deep learning approaches to process coronary computed tomographic angiography (CCTA) imaging (termed CCTA-artificial intelligence, CCTA-AI) in coronary artery stenosis.

MATERIALS AND METHODS

A CCTA reconstruction pipeline was built by utilizing deep learning and transfer learning approaches to generate auto-reconstructed CCTA images based on a series of two-dimensional (2D) CT images. 150 patients who underwent successively CCTA and digital subtraction angiography (DSA) from June 2017 to December 2017 were retrospectively analyzed. The dataset was divided into two parts comprising training dataset and testing dataset. The training dataset included the CCTA images of 100 patients which are trained using convolutional neural networks (CNN) in order to further identify various plaque classifications and coronary stenosis. The other 50 CAD patients acted as testing dataset that is evaluated by comparing the auto-reconstructed CCTA images with traditional CCTA images on the condition that DSA images are regarded as the reference method. Receiver operating characteristic (ROC) analysis was used for statistical analysis to compare CCTA-AI with DSA and traditional CCTA in the aspect of detecting coronary stenosis and plaque features.

RESULTS

AI significantly reduces time for post-processing and diagnosis comparing to the traditional methods. In identifying various degrees of coronary stenosis, the diagnostic accuracy of CCTA-AI is better than traditional CCTA (AUC_AI = 0.870, AUC_CCTA = 0.781, P < 0.001). In identifying ≥ 50% stenotic vessels, the accuracy, sensitivity, specificity, positive predictive value and negative predictive value of CCTA-AI and traditional method are 86% and 83%, 88% and 59%, 85% and 94%, 73% and 84%, 94% and 83%, respectively. In the aspect of identifying plaque classification, accuracy of CCTA-AI is moderate compared to traditional CCTA (AUC = 0.750, P < 0.001).

CONCLUSION

The proposed CCTA-AI allows the generation of auto-reconstructed CCTA images from a series of 2D CT images. This approach is relatively accurate for detecting ≥50% stenosis and analyzing plaque features compared to traditional CCTA.

Should computed tomography angiography supersede invasive coronary angiography for the evaluation of graft patency following coronary artery bypass graft surgery?

Invasive coronary angiography (ICA) has long been the established gold standard in assessing graft patency following coronary artery bypass graft (CABG). Over the past decade or so however, improvements in computed tomography angiography (CTA) technology have allowed its emergence as a useful clinical tool in graft assessment. The recent introduction of 64-slice and now 128-slice scanners into widespread distribution, and the development of 320-detector row technology allowing volumetric imaging of the entire heart at single points in time within one cardiac cycle, has increased the potential of CTA to supersede ICA in this capacity. This study sought to examine the evidence surrounding this potential. A best evidence topic was constructed according to a structured protocol. The enquiry: In [patients who have undergone coronary artery bypass graft surgery] is [computed tomography angiography or invasive coronary angiography] superior in terms of [graft patency assessment, stenosis detection, radiation exposure and complication rate]? Four hundred and twenty-four articles were identified from the search strategy. Four additional articles were identified from references of key articles. Seventeen articles selected as best evidence were tabulated. The reliability of CTA as a tool in the detection of graft patency and stenosis has continued to improve with each successive generation of multislice technology. The latest 64- and 128-slice CTA techniques are able to detect graft patency and stenosis with very high sensitivities and specificities comparable with ICA, while remaining non-invasive procedures associated with fewer complications (ICA carries a 0.08% risk of myocardial infarction and 0.7% risk of minor complications in clinically stable patients). Present limitations of the technology include the accurate visualization of distal anastomoses and clip artefacts. In addition, the capacity of diagnostic ICA to be combined simultaneously with percutaneous coronary interventions is an important advantage and a further limitation of CTA alone. Recent developments, however, including the derivation of fractional flow reserve and perfusion assessment from CTA as functional measures of stenosis severity have given CTA at present the capacity to become a first-line tool in the assessment of patients with suspected graft dysfunction. Novel computer-automated diagnostic software, though currently in infancy, has shown promise in facilitating and speeding image interpretation. With further improvements in scanning technologies, CTA is likely to supersede ICA for graft assessment in the near future.

Post-stress left ventricular ejection fraction drop in patients with diabetes: a gated myocardial perfusion imaging study

BACKGROUND

To evaluate the relevance of stress-induced decrease in left ventricular ejection fraction (LVEF) in patients with type-2 diabetes.

METHODS

A total of 684 diabetic patients with available rest and post-stress gated myocardial perfusion single-photon emission computed tomography (MPS) data were enrolled. An automated algorithm was used to determine the perfusion scores using a 17-segment model. LVEF drop was considered significant if the post-stress LVEF was ≥5% below the rest value. Follow-up data were available in 587 patients that were followed for the occurrence of cardiac death, nonfatal myocardial infarction, or unstable angina requiring revascularization.

RESULTS

A post-stress LVEF drop ≥5% was observed in 167 (24%) patients. Patients with LVEF drop had higher summed stress score (p < 0.05), summed difference score (p < 0.001), and rest LVEF (p < 0.001) compared to patients without. Conversely, summed rest score, a measure of infarct size, was comparable between the two groups. At multivariable analysis, summed difference score and rest LVEF were independent predictors (both p < 0.001) of post-stress LVEF drop. Myocardial perfusion was abnormal in 106 (63%) patients with post-stress LVEF drop and in 296 (57%) of those without (p = 0.16). The overall event-free survival was lower in patients with post-stress LVEF drop than in those without (log rank χ2 7.7, p < 0.005). After adjusting for clinical data and MPS variables, the hazard ratio for cardiac events for post-stress LVEF drop was 1.52 (p < 0.01).

CONCLUSIONS

In diabetic patients stress-induced ischemia is an independent predictor of post-stress LVEF drop; however, a reduction in LVEF is detectable also in patients with normal perfusion. Finally, post-stress LVEF drop increases the risk of subsequent cardiac events in diabetic patients.