Computer-aided CT coronary artery stenosis detection: comparison with human reading and quantitative coronary angiography

Diagnostic performance of deep learning-based vessel extraction and stenosis detection on coronary computed tomography angiography for coronary artery disease: a multi-reader multi-case study

BACKGROUND

Post-processing and interpretation of coronary CT angiography (CCTA) imaging are time-consuming and dependent on the reader's experience. An automated deep learning (DL)-based imaging reconstruction and diagnosis system was developed to improve diagnostic accuracy and efficiency.

METHODS

Our study including 374 cases from five sites, inviting 12 radiologists, assessed the DL-based system in diagnosing obstructive coronary disease with regard to diagnostic performance, imaging post-processing and reporting time of radiologists, with invasive coronary angiography as a standard reference. The diagnostic performance of DL system and DL-assisted human readers was compared with the traditional method of human readers without DL system.

RESULTS

Comparing the diagnostic performance of human readers without DL system versus with DL system, the AUC was improved from 0.81 to 0.82 (p < 0.05) at patient level and from 0.79 to 0.81 (p < 0.05) at vessel level. An increase in AUC was observed in inexperienced radiologists (p < 0.05), but was absent in experienced radiologists. Regarding diagnostic efficiency, comparing the DL system versus human reader, the average post-processing and reporting time was decreased from 798.60 s to 189.12 s (p < 0.05). The sensitivity and specificity of using DL system alone were 93.55% and 59.57% at patient level and 83.23% and 79.97% at vessel level, respectively.

CONCLUSIONS

With the DL system serving as a concurrent reader, the overall post-processing and reading time was substantially reduced. The diagnostic accuracy of human readers, especially for inexperienced readers, was improved. DL-assisted human reader had the potential of being the reading mode of choice in clinical routine.

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 powered coronary CT angiography for detecting obstructive coronary artery disease: The effect of reader experience, calcification and image quality

OBJECTIVES

To investigate the effect of reader experience, calcification and image quality on the performance of deep learning (DL) powered coronary CT angiography (CCTA) in automatically detecting obstructive coronary artery disease (CAD) with invasive coronary angiography (ICA) as reference standard.

METHODS

A total of 165 patients (680 vessels and 1505 segments) were included in this study. Three sessions were performed in order: (1) The artificial intelligence (AI) software automatically processed CCTA images, stenosis degree and processing time were recorded for each case; (2) Six cardiovascular radiologists with different experiences (low/ intermediate/ high experience) independently performed image post-processing and interpretation of CCTA, (3) AI + human reading was performed. Luminal stenosis ≥50% was defined as obstructive CAD in ICA and CCTA. Diagnostic performances of AI, human reading and AI + human reading were evaluated and compared on a per-patient, per-vessel and per-segment basis with ICA as reference standard. The effects of calcification and image quality on the diagnostic performance were also studied.

RESULTS

The average post-processing and interpretation times of AI was 2.3 ± 0.6 min per case, reduced by 76%, 72%, 69% compared with low/ intermediate/ high experience readers (all P < 0.001), respectively. On a per-patient, per-vessel and per-segment basis, with ICA as reference method, the AI overall diagnostic sensitivity for detecting obstructive CAD were 90.5%, 81.4%, 72.9%, the specificity was 82.3%, 93.9%, 95.0%, with the corresponding areas under the curve (AUCs) of 0.90, 0.90, 0.87, respectively. Compared to human readers, the diagnostic performance of AI was higher than that of low experience readers (all P < 0.001). The diagnostic performance of AI + human reading was higher than human reading alone, and AI + human readers' ability to correctly reclassify obstructive CAD was also improved, especially for low experience readers (Per-patient, the net reclassification improvement (NRI) = 0.085; per-vessel, NRI = 0.070; and per-segment, NRI = 0.068, all P < 0.001). The diagnostic performance of AI was not significantly affected by calcification and image quality (all P > 0.05).

CONCLUSIONS

AI can substantially shorten the post-processing time, while AI + human reading model can significantly improve the diagnostic performance compared with human readers, especially for inexperienced readers, regardless of calcification severity and image quality.

Development of Novel Artificial Intelligence to Detect the Presence of Clinically Meaningful Coronary Atherosclerotic Stenosis in Major Branch from Coronary Angiography Video

Aim: The clinically meaningful coronary stenosis is diagnosed by trained interventional cardiologists. Whether artificial intelligence (AI) could detect coronary stenosis from CAG video is unclear.

Methods: The 199 consecutive patients who underwent coronary arteriography (CAG) with chest pain between December 2018 and May 2019 was enrolled. Each patient underwent CAG with multiple view resulting in total numbers of 1,838 videos. A multi-layer 3-dimensional convolution neural network (CNN) was trained as an AI to detect clinically meaningful coronary artery stenosis diagnosed by the expert interventional cardiologist, using data from 146 patients (resulted in 1,359 videos) randomly selected from the entire dataset (training dataset). This training dataset was further split into 109 patients (989 videos) for derivation and 37 patients (370 videos) for validation. The AI developed in derivation cohort was tuned in validation cohort to make final model.

Results: The final model was selected as the model with best performance in validation dataset. Then, the predictive accuracy of final model was tested with the remaining 53 patients (479 videos) in test dataset. Our AI model showed a c-statistic of 0.61 in validation dataset and 0.61 in test dataset, respectively.

Conclusion: An artificial intelligence applied to CAG videos could detect clinically meaningful coronary atherosclerotic stenosis diagnosed by expert cardiologists with modest predictive value. Further studies with improved AI at larger sample size is necessary.

Evaluation of a High Concentrated Contrast Media Injection Protocol in Combination with Low Tube Current for Dose Reduction in Coronary Computed Tomography Angiography: A Randomized, Two-center Prospective Study

RATIONALE AND OBJECTIVES

The study aimed to prospectively evaluate the radiation dose reduction potential and image quality (IQ) of a high-concentration contrast media (HCCM) injection protocol in combination with a low tube current (mAs) in coronary computed tomography angiography.

MATERIALS AND METHODS

Eighty-one consecutive patients (mean age: 62 years; 34 females; body mass index: 18-31) were included and randomized-assigned into two groups. All computed tomography (CT) examinations were performed in two groups with the same tube voltage (100 kV), flow rate of contrast medium (5.0 mL/s), and iodine dose (22.8 g). An automatic mAs and low concentration contrast medium (300 mgI/mL) were used in group A, whereas effective mAs was reduced by a factor 0.6 along with HCCM (400 mgI/mL) in group B. Radiation dose was assessed (CT dose index [CTDI_vol] and dose length product), and vessel-based objective IQ for various regions of interest (enhancement, noise, signal-to-noise ratio, and contrast-to-noise ratio), subjective IQ, noise, and motion artifacts were analyzed overall and vessel-based with a 5-point Likert scale.

RESULTS

The CT attenuation of coronary arteries and image noise in group B were significantly higher than those in group A (ranges: 507.5-548.1 Hounsfield units vs 407.5-444.5 Hounsfield units; and 20.3 ± 8.6 vs 17.7 ± 8.0) (P ≤ 0.0166). There was no significant difference between the two groups in signal-to-noise ratio, contrast-to-noise ratio, and subjective IQ of coronary arteries (29.4-31.7, 30.0-37.0, and medium score of 5 in group A vs 29.4-32.4, 27.7-36.3, and medium score of 5 in group B, respectively, P ≥ 0.1859). Both mean CTDI_vol and dose length product in group B were 58% of those of group A.

CONCLUSIONS

HCCM combined with low tube current allows dose reduction in coronary computed tomography angiography and does not compromise IQ.

Plaque assessment by coronary CT

Coronary CT angiography (CTA) has emerged as a highly reliable and non-invasive modality for the exclusion of coronary artery disease. Recent technological advancements in coronary CTA imaging allow for robust qualitative and quantitative assessment of atherosclerotic plaques. Furthermore, CTA is a promising modality for functional evaluation of coronary lesions. Individual plaque features, the extent and severity of atherosclerotic plaque burden were proposed to improve cardiovascular risk stratification. It has been suggested that total atherosclerotic plaque burden is a stronger predictor of coronary events than total ischemia burden. The quest to noninvasively detect individual vulnerable plaques still remains. In the current review we sought to summarize state-of-the-art coronary artery plaque assessment by CTA.