Coronary Tomography Angiography Research Articles

Coronary atherosclerosis among symptomatic patients with zero coronary artery calcium score in computed tomography coronary angiography

BackgroundComputed tomographic coronary angiography has been recognized as a reliable imaging modality with excellent negative predictive value and a good negative likelihood ratio to exclude coronary artery disease in stable, symptomatic patients with intermediate or high risk. 1) Coronary calcium scoring has been extensively shown to be an invaluable tool to exclude the presence of coronary artery disease in low-risk patients. 2) Our aim was to identify the presence and extent of coronary atherosclerosis in computed tomographic coronary angiography in stable symptomatic patients with a zero Coronary Calcium score. ResultsThree hundred and eighty-three (383) consecutive patients aged ≥ 18 years fulfilling the criteria were enrolled as of January 1, 2021; 165 (43.1%) were male and 218 (56.9%) were female, with a mean age of 57.8 ± 4.9 years and a zero coronary artery calcium score. Two hundred and twenty-six (226) (59.0%) were hypertensive, followed by 125 (32.6%) who were smokers, and 117 (30.5%) who were diabetic. The frequency of atherosclerotic plaque in coronary arteries was 34 (8.9%), with 16 (47.1%) being male and 18 (52.9%) being female. The mean age of patients with atherosclerosis was 54.9 ± 3.3 years; among them, 13 (38.2%) were between the ages of 45 and 54, and 10 (29.4%) were between the ages of 55 and 64. Nineteen (19) (55.9%) were hypertensive, followed by 10 (29.4%) with dyslipidemia. Twenty-three (23) (67.6%) had non-obstructive plaque, and 11 (32.3%) had obstructive plaque. In the subgroup of patients with non-obstructive plaque, 13 (56.5%) were hypertensive, 8 (34.8%) were diabetic, and 16 (69.6%) had single vessel disease, while among patients with obstructive plaque, 6 (54.5%) were hypertensive, 5 (45.5%) were smokers, and all of them had single vessel disease. The most affected artery was the left anterior descending artery. ConclusionAs the frequency of atherosclerotic plaque in patients with a zero coronary calcium score is relatively high, computed tomographic coronary angiography is indicated in stable, symptomatic patients with a lower likelihood of coronary artery disease.

Anomalous origin of the coronary artery: prevalence and coronary artery disease in adults undergoing coronary tomographic angiography

BackgroundAnomalous aortic origin of a coronary artery (AAOCA) is a rare congenital coronary anomaly with the potential to cause adverse cardiac events. However, there is limited data on the association between AAOCA and coronary artery disease (CAD). Therefore, the aim of this study is to determine the prevalence and symptoms of patients with AAOCA, as well as investigate the correlation between AAOCA and CAD in a population referred for coronary computed tomographic angiography (CTA).Methods and resultsAll consecutive patients who underwent CTA from 2010 to 2021 were included. Characteristics, symptoms, coronary related adverse events and CTA information were reviewed by medical records. Separate multivariable cumulative logistic regressions were performed, using the stenosis severity in each of the four coronaries as individual responses and as a combined patient clustered response. Finally, we identified 207 adult patients with AAOCA, the prevalence of AAOCA is 0.23% (207/90,501). Moreover, this study found no significant association between AAOCA and CAD. AAOCA did not contribute to higher rates of hospitalization or adverse cardiac events, including calcification.ConclusionAAOCA is a rare congenital disease that is not associated with increased presence of obstructive CAD in adults.

Ultra-high resolution coronary CT angiography on photon-counting detector CT: bi-centre study on the impact of quantum iterative reconstruction on image quality and accuracy of stenosis measurements

PurposeTo assess the impact of different quantum iterative reconstruction (QIR) levels on objective and subjective image quality of ultra-high resolution (UHR) coronary CT angiography (CCTA) images and to determine the effect of strength levels on stenosis quantification using photon-counting detector (PCD)-CT. MethodA dynamic vessel phantom containing two calcified lesions (25 % and 50 % stenosis) was scanned at heart rates of 60, 80 and 100 beats per minute with a PCD-CT system. In vivo CCTA examinations were performed in 102 patients. All scans were acquired in UHR mode (slice thickness0.2 mm) and reconstructed with four different QIR levels (1–4) using a sharp vascular kernel (Bv64). Image noise, signal-to-noise ratio (SNR), sharpness, and percent diameter stenosis (PDS) were quantified in the phantom, while noise, SNR, contrast-to-noise ratio (CNR), sharpness, and subjective quality metrics (noise, sharpness, overall image quality) were assessed in patient scans. ResultsIncreasing QIR levels resulted in significantly lower objective image noise (in vitro and in vivo: both p < 0.001), higher SNR (both p < 0.001) and CNR (both p < 0.001). Sharpness and PDS values did not differ significantly among QIRs (all pairwise p > 0.008). Subjective noise of in vivo images significantly decreased with increasing QIR levels, resulting in significantly higher image quality scores at increasing QIR levels (all pairwise p < 0.001). Qualitative sharpness, on the other hand, did not differ across different levels of QIR (p = 0.15). ConclusionsThe QIR algorithm may enhance the image quality of CCTA datasets without compromising image sharpness or accurate stenosis measurements, with the most prominent benefits at the highest strength level.

3D printing of an artificial intelligence-generated patient-specific coronary artery segmentation in a support bath

Accurate segmentation of coronary artery tree and personalized 3D printing from medical images is essential for CAD diagnosis and treatment. The current literature on 3D printing relies solely on generic models created with different software or 3D coronary artery models manually segmented from medical images. Moreover, there are not many studies examining the bioprintability of a 3D model generated by artificial intelligence (AI) segmentation for complex and branched structures. In this study, deep learning algorithms with transfer learning have been employed for accurate segmentation of the coronary artery tree from medical images to generate printable segmentations. We propose a combination of deep learning and 3D printing, which accurately segments and prints complex vascular patterns in coronary arteries. Then, we performed the 3D printing of the AI-generated coronary artery segmentation for the fabrication of bifurcated hollow vascular structure. Our results indicate improved performance of segmentation with the aid of transfer learning with a Dice overlap score of 0.86 on a test set of 10 coronary tomography angiography images. Then, bifurcated regions from 3D models were printed into the Pluronic F-127 support bath using alginate + glucomannan hydrogel. We successfully fabricated the bifurcated coronary artery structures with high length and wall thickness accuracy, however, the outer diameters of the vessels and length of the bifurcation point differ from the 3D models. The extrusion of unnecessary material, primarily observed when the nozzle moves from left to the right vessel during 3D printing, can be mitigated by adjusting the nozzle speed. Moreover, the shape accuracy can also be improved by designing a multi-axis printhead that can change the printing angle in three dimensions. Thus, this study demonstrates the potential of the use of AI-segmented 3D models in the 3D printing of coronary artery structures and, when further improved, can be used for the fabrication of patient-specific vascular implants.

Shifting perspectives in coronary involvement of polyarteritis nodosa: case of 3-vessel occlusion treated with 4-vessel CABG and review of literature

BackgroundPolyarteritis Nodosa (PAN) is a systemic vasculitis (SV) historically thought to spare the coronary arteries. Coronary angiography and contemporary imaging reveal coronary stenosis and dilation, which are associated with significant morbidity and mortality. Coronary arteries in PAN are burdened with accelerated atherosclerosis from generalized inflammation adding to an inherent arteritic process. Traditional atherosclerotic risk factors fail to approximate risk. Few reports document coronary pathology and optimal therapy has been guarded.MethodsDatabase publication query of English literature from 1990–2022.ResultsSeverity of coronary involvement eludes laboratory monitoring, but coronary disease associates with several clinical symptoms. Framingham risk factors inadequately approximate disease burden. Separating atherosclerosis from arteritis requires advanced angiographic methods. Therapy includes anticoagulation, immunosuppression and revascularization. PCI has been the mainstay, though stenting is confounded by vagarious alteration in luminal diameter and reports of neointimization soon after placement.ConclusionsWhen graft selection avoids the vascular territory of SV’s, CABG offers definitive therapy. We have contributed report of a novel CABG configuration in addition to reviewing, updating and discussing the literature. Accumulating evidence suggests discrete clinical symptoms warrant suspicion for coronary involvement.

Multiscale attention network via topology learning for cerebral vessel segmentation in angiography images

Cerebrovascular diseases are a widespread threat to human health. The accurate extraction of cerebral vessel structures is of paramount importance in the diagnosis and treatment of cerebrovascular diseases. However, the complexity of cerebral vessel structures and the low imaging contrast present significant challenges for vessel segmentation. Therefore, we propose a Multiscale Attention Network based on topological learning to extract vessel structures from angiographic images. This method employs a Multiscale Squeeze Attention (MSA) module for channel-wise attention learning, extracting multiscale attention feature maps from angiographic images. To maintain the topological connectivity of vessel segmentation, we introduced the clDice loss function to enforce skeleton connectivity of vessel segmentation. We conducted an experimental analysis of the proposed method using a publicly available cerebral vessel dataset. The results demonstrated that the proposed method achieved a sensitivity score of 0.8507 and a dice score of 0.8669 for cerebrovascular segmentation, enabling accurate and complete extraction of vascular structures. The proposed method was extended to coronary angiography images. The results show that the proposed method can accurately extract coronary structures, proving its broad applicability to other vascular segmentation tasks.

A Preprocessing Method for Coronary Artery Stenosis Detection Based on Deep Learning

The detection of coronary artery stenosis is one of the most important indicators for the diagnosis of coronary artery disease. However, stenosis in branch vessels is often difficult to detect using computer-aided systems and even radiologists because of several factors, such as imaging angle and contrast agent inhomogeneity. Traditional coronary artery stenosis localization algorithms often only detect aortic stenosis and ignore branch vessels that may also cause major health threats. Therefore, improving the localization of branch vessel stenosis in coronary angiographic images is a potential development property. In this study, we propose a preprocessing approach that combines vessel enhancement and image fusion as a prerequisite for deep learning. The sensitivity of the neural network to stenosis features is improved by enhancing the blurry features in coronary angiographic images. By validating five neural networks, such as YOLOv4 and R-FCN-Inceptionresnetv2, our proposed method can improve the performance of deep learning network applications on the images from six common imaging angles. The results showed that the proposed method is suitable as a preprocessing method for coronary angiographic image processing based on deep learning and can be used to amend the recognition ability of the deep model for fine vessel stenosis.

Qualitative and quantitative image quality of coronary CT angiography using photon-counting computed tomography: Standard and Ultra-high resolution protocols

PurposeWe aimed to identify the optimal reconstruction settings based on qualitative and quantitative image quality parameters on standard and ultra-high resolution (UHR) images using photon-counting CT (PCCT). MethodWe analysed 45 patients, 29 with standard and 16 with UHR acquisition, applying both smoother and sharper kernel settings. Coronary CT angiography images were performed on a dual-source PCCT system using standard (0.4/0.6 mm slice thickness, Bv40/Bv44 kernels, QIR levels 0–4) or UHR acquisition (0.2/0.4 mm slice thickness, Bv44/Bv56 kernels, QIR levels 0–4). Qualitative image quality was assessed using a 4-point Likert scale. Image noise (SD), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated in both the proximal and distal segments. ResultsOn standard resolution, larger slice thickness resulted in an average increase of 12.5 % in CNR, whereas sharper kernel led to an average 8.7 % decrease in CNR. Highest CNR was measured on 0.6 mm, Bv40, QIR4 images and lowest on 0.4 mm, Bv44, QIR0 images: 25.8 ± 4.1vs.8.3 ± 1.6 (p < 0.001). On UHR images, highest CNR was observed on 0.4 mm, Bv40, QIR4 and lowest on 0.2 mm, Bv56 and QIR0 images: 21.5 ± 3.9vs.3.6 ± 0.8 (p < 0.001). Highest qualitative image quality was found on images with Bv44 kernel and QIR level 3/4 with both slice thicknesses on standard reconstruction. Additionally, Bv56 with QIR4 on 0.2 mm slice thickness images showed highest subjective image quality. Preserved distal vessel visualization was detected using QIR 2–4, Bv56 and 0.2 mm slice thickness. ConclusionsPhoton-counting CT demonstrated high qualitative and quantitative image quality for the assessment of coronaries and stents.

Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning

BackgroundQuantitative coronary angiography (QCA) offers objective and reproducible measures of coronary lesions. However, significant inter- and intra-observer variability and time-consuming processes hinder the practical application of on-site QCA in the current clinical setting. This study proposes a novel method for artificial intelligence-based QCA (AI-QCA) analysis of the major vessels and evaluates its performance. MethodsAI-QCA was developed using three deep-learning models trained on 7658 angiographic images from 3129 patients for the precise delineation of lumen boundaries. An automated quantification method, employing refined matching for accurate diameter calculation and iterative updates of diameter trend lines, was embedded in the AI-QCA. A separate dataset of 676 coronary angiography images from 370 patients was retrospectively analyzed to compare AI-QCA with manual QCA performed by expert analysts. A match was considered between manual and AI-QCA lesions when the minimum lumen diameter (MLD) location identified manually coincided with the location identified by AI-QCA. Matched lesions were evaluated in terms of diameter stenosis (DS), MLD, reference lumen diameter (RLD), and lesion length (LL). ResultsAI-QCA exhibited a sensitivity of 89% in lesion detection and strong correlations with manual QCA for DS, MLD, RLD, and LL. Among 995 matched lesions, most cases (892 cases, 80%) exhibited DS differences ≤10%. Multiple lesions of the major vessels were accurately identified and quantitatively analyzed without manual corrections. ConclusionAI-QCA demonstrates promise as an automated tool for analysis in coronary angiography, offering potential advantages for the quantitative assessment of coronary lesions and clinical decision-making.

Socioeconomic deprivation and illness trajectory in the Scottish population after COVID-19 hospitalization

BackgroundThe associations between deprivation and illness trajectory after hospitalisation for coronavirus disease-19 (COVID-19) are uncertain.MethodsA prospective, multicentre cohort study was conducted on post-COVID-19 patients, enrolled either in-hospital or shortly post-discharge. Two evaluations were carried out: an initial assessment and a follow-up at 28–60 days post-discharge. The study encompassed research blood tests, patient-reported outcome measures, and multisystem imaging (including chest computed tomography (CT) with pulmonary and coronary angiography, cardiovascular and renal magnetic resonance imaging). Primary and secondary outcomes were analysed in relation to socioeconomic status, using the Scottish Index of Multiple Deprivation (SIMD). The EQ-5D-5L, Brief Illness Perception Questionnaire (BIPQ), Patient Health Questionnaire-4 (PHQ-4) for Anxiety and Depression, and the Duke Activity Status Index (DASI) were used to assess health status.ResultsOf the 252 enrolled patients (mean age 55.0 ± 12.0 years; 40% female; 23% with diabetes), deprivation status was linked with increased BMI and diabetes prevalence. 186 (74%) returned for the follow-up. Within this group, findings indicated associations between deprivation and lung abnormalities (p = 0.0085), coronary artery disease (p = 0.0128), and renal inflammation (p = 0.0421). Furthermore, patients with higher deprivation exhibited worse scores in health-related quality of life (EQ-5D-5L, p = 0.0084), illness perception (BIPQ, p = 0.0004), anxiety and depression levels (PHQ-4, p = 0.0038), and diminished physical activity (DASI, p = 0.002). At the 3-month mark, those with greater deprivation showed a higher frequency of referrals to secondary care due to ongoing COVID-19 symptoms (p = 0.0438). However, clinical outcomes were not influenced by deprivation.ConclusionsIn a post-hospital COVID-19 population, socioeconomic deprivation was associated with impaired health status and secondary care episodes. Deprivation influences illness trajectory after COVID-19.

Angiographic characteristics of coronary artery disease in patients undergoing diagnostic coronary angiography at a tertiary hospital in Tanzania

BackgroundCoronary artery disease (CAD) is an important cause of global burden of disease. There is a paucity of data on the burden and risk factors for CAD in sub-Saharan Africa (SSA), despite the rising trends in the shared risk factors across regions. The recent introduction of cardiac catheterization laboratory services in SSA could shed light on the burden of CAD in the region. We aimed to assess the angiographic characteristics among patients undergoing diagnostic coronary angiography (CAG) at a single tertiary care hospital in Tanzania.MethodsThis study was a retrospective chart review. A total of 728 patients ≥ 18 years of age who underwent CAG from January 2020 to December 2022 were recruited into the study. Basic demographic variables, risk factors and clinical characteristics including CAG findings were obtained from the registry. In addition, CAG images were retrieved for assessment of angiographic features. The luminal vessel stenosis was assessed based on eyeballing and the degree of obstruction was agreed by two independent and experienced cardiologists. The coronary stenosis of ≥ 50% was considered significant for obstructive CAD. The study was approved by the local ethics committee.ResultsOf patients who were recruited into the study, 384 (52.23%) were female. The study participants had a mean age of 59.46 ± 10.83 standard deviation (SD) and mean body mass index (BMI) of 31.18 kg/m2. The prevalence of CAD of any degree was estimated at 24.43% (34.18% in male, 15.50% in female), while that of obstructive CAD was 18.27%. Forty six percent of those with obstructive CAD had multiple vessel disease (MVD). Nearly 77% of patients were found to have ≥ 50–70% luminal stenosis and while those with ≥ 70% luminal coronary artery stenosis constituted 56.65%. Right coronary artery (RCA) was the most commonly affected vessel, accounting for 36.84% when any vessel disease or 56% when single vessel disease were considered. Being 65 years or older and comorbidity with type 2 diabetes (T2D) were independent risk factors for developing CAD.ConclusionThere is a high prevalence of obstructive CAD among patients undergoing diagnostic CAG in Tanzania, with male gender preponderance and increasingly higher in older age, often with severe disease. A large, prospective study is needed to provide epidemiological and clinical data for developing a locally-relevant cardio-preventive strategy for CAD intervention in Tanzania.

Coronary physiology instantaneous wave-free ratio (iFR) derived from x-ray angiography using artificial intelligence deep learning models: a pilot study.

Coronary angiography (CAG)-derived physiology methods have been developed in an attempt to simplify and increase the usage of coronary physiology, based mostly on dynamic fluid computational algorithms. We aimed to develop a different approach based on artificial intelligence methods, which has seldom been explored. Consecutive patients undergoing invasive instantaneous free-wave ratio (iFR) measurements were included. We developed artificial intelligence (AI) models capable of classifying target lesions as positive (iFR ≤ 0.89) or negative (iFR > 0.89). The predictions were then compared to the true measurements. Two hundred-fifty measurements were included, and 3 models were developed. Model 3 had the best overall performance: accuracy, negative predictive value (NPV), positive predictive value (PPV), sensitivity, and specificity were 69%, 88%, 44%, 74%, and 67%, respectively. Performance differed per target vessel. For the left anterior descending artery (LAD), model 3 had the highest accuracy (66%), while model 2 the highest NPV (86%) and sensitivity (91%). PPV was always low/modest. Model 1 had the highest specificity (68%). For the right coronary artery, model 1's accuracy was 86%, NPV was 97%, and specificity was 87%, but all models had low PPV (maximum 25%) and low/modest sensitivity (maximum 60%). For the circumflex, model 1 performed best: accuracy, NPV, PPV, sensitivity, and specificity were 69%, 96%, 24%, 80%, and 68%, respectively. We developed 3 AI models capable of binary iFR estimation from CAG images. Despite modest accuracy, the consistently high NPV is of potential clinical significance, as it would enable avoiding further invasive maneuvers after CAG. This pivotal study offers proof of concept for further development.

Image and Clinical Characteristics of the Right Coronary Artery Originating From the Left Coronary Sinus: A Database Review.

This article systematically explores the imaging and clinical characteristics of a relatively rare cardiac anomaly: the right coronary artery originating from the left coronary sinus. Through a comprehensive analysis of existing literature, this study aims to provide a comprehensive understanding of the prevalence, diagnostic methods, and potential clinical implications of this anatomical variation. Anatomical classification is introduced, along with clinical imaging diagnostic methods, including coronary angiography, computed tomography, and magnetic resonance imaging. Additionally, the review delves into the clinical significance of this anomaly, including its potential associations with myocardial ischemia, arrhythmias, and acute cardiac events, outlining clinical approaches to diagnosing myocardial ischemia. The study results consolidate current knowledge about this cardiac variation, emphasizing the importance of recognizing and appropriately managing it in clinical practice.

Motion artifact correction in cardiac CT using cross-phase temporospatial information and synergistic attention gate and spatial transformer sub-networks

Objectives. To improve quality of coronary CT angiography (CCTA) images using a generalizable motion-correction algorithm. Approach. A neural network with attention gate and spatial transformer (ATOM) was developed to correct coronary motion. Phantom and patient CCTA images (39 males, 32 females, age range 19–92, scan date 02/2020 to 10/2021) retrospectively collected from dual-source CT were used to create training, development, and testing sets corresponding to 140- and 75 ms temporal resolution, with 75 ms images as labels. To test generalizability, ATOM was deployed for locally adaptive motion-correction in both 140- and 75 ms patient images. Objective metrics were used to assess motion-corrupted and corrected phantom and patient images, including structural-similarity-index (SSIM), dice-similarity-coefficient (DSC), peak-signal-noise-ratio (PSNR), and normalized root-mean-square-error (NRMSE). In objective quality assessment, ATOM was compared with several baseline networks, including U-net, U-net plus attention gate, U-net plus spatial transformer, VDSR, and ResNet. Two cardiac radiologists independently interpreted motion-corrupted and -corrected images at 75 and 140 ms in a blinded fashion and ranked diagnostic image quality (worst to best: 1–4, no ties). Main results. ATOM improved quality metrics (p < 0.05) before/after correction: in phantom, SSIM 0.87/0.95, DSC 0.85/0.93, PSNR 19.4/22.5, NRMSE 0.38/0.27; in patient images, SSIM 0.82/0.88, DSC 0.88/0.90, PSNR 30.0/32.0, NRMSE 0.16/0.12. ATOM provided more consistent improvement of objective image quality, compared to the presented baseline networks. The motion-corrected images received better ranks than un-corrected at the same temporal resolution (p < 0.05): 140 ms images 1.65/2.25, and 75 ms images 3.1/3.2. The motion-corrected 75 ms images received the best rank in 65% of testing cases. A fair-to-good inter-reader agreement was observed (Kappa score 0.58). Significance. ATOM reduces motion artifacts, improving visualization of coronary arteries. This algorithm can be used to virtually improve temporal resolution in both single- and dual-source CT.

Ensemble Learning-Based Coronary Artery Disease Detection Using Computer Tomography Images

Coronary artery disease (CAD) is the most prevalent form of cardiovascular disease that may result in myocardial infarction. Annually, it leads to millions of fatalities and causes billions of dollars in global economic losses. Limited resources and complexities in interpreting results pose challenges to healthcare centers in implementing deep learning (DL)-based CAD detection models. Ensemble learning (EL) allows developers to build an effective CAD detection model by integrating the outcomes of multiple medical imaging models. In this study, the authors build an EL-based CAD detection model to identify CAD from coronary computer tomography angiography (CCTA) images. They employ a feature engineering technique, including MobileNet V3, CatBoost, and LightGBM models. A random forest (RF) classifier is used to ensemble the outcomes of the CatBoost and LightGBM models. The authors generalize the model using two benchmark datasets. The proposed model achieved an accuracy of 99.7% and 99.6% with limited computational resources. The generalization results highlight the importance of the proposed model’s efficiency in identifying CAD from the CCTA images. Healthcare centers and cardiologists can benefit from the proposed model to identify CAD in the initial stages. The proposed feature engineering can be extended using a liquid neural network model to reduce computational resources.

Effect of inflammation on association between cancer and coronary artery disease

BackgroundCancer and coronary artery disease (CAD) is reported to often co-exist in same individuals, however, whether cancer is directly associated with anatomical severity of CAD is rarely studied. The present study aimed to observe the relationship between newly diagnosed cancer and anatomical severity of CAD, moreover, to investigate effect of inflammation on the relationship of cancer with CAD.Methods374 patients with newly diagnosed cancer who underwent coronary angiography (CAG) were enrolled. Through 1:3 propensity score matching (PSM) to cancer patients based on the age and gender among 51,106 non-cancer patients who underwent CAG, 1122 non-cancer patients were selected as control patients. Anatomical severity of CAD was assessed using SYNTAX score (SXscore) based on coronary angiographic image. SXscore ≤ 22 (highest quartile) was defined as SX-low, and SXscore > 22 as SX-high. The ratio of neutrophil to lymphocyte count (NLR) was used to describe inflammation level. Association between cancer and the anatomical severity of CAD was investigated using logistic regression.ResultsUnivariate logistic regression analysis showed a correlation between cancer and anatomical severity of CAD (OR: 1.419, 95% CI: 1.083–1.859; P = 0.011). Cancer was associated with increased risk of SX-high after adjusted for common risk factors of CAD (OR: 1.598, 95% CI: 1.172–2.179, P = 0.003). Significant association between cancer and SX-high was revealed among patients with high inflammation (OR: 1.656, 95% CI: 1.099–2.497, P = 0.016), but not among patients with low inflammation (OR: 1.530, 95% CI: 0.973–2.498, P = 0.089).ConclusionsCancer was associated with severity of CAD, however, the association between the two diseases was significant among patients with high inflammation rather than among patients with low inflammation.

Fully automated artificial intelligence-based coronary CT angiography image processing: efficiency, diagnostic capability, and risk stratification.

To prospectively investigate whether fully automated artificial intelligence (FAAI)-based coronary CT angiography (CCTA) image processing is non-inferior to semi-automated mode in efficiency, diagnostic ability, and risk stratification of coronary artery disease (CAD). Adults with indications for CCTA were prospectively and consecutively enrolled at two hospitals and randomly assigned to either FAAI-based or semi-automated image processing using equipment workstations. Outcome measures were workflow efficiency, diagnostic accuracy for obstructive CAD (≥ 50% stenosis), and cardiovascular events at 2-year follow-up. The endpoints included major adverse cardiovascular events, hospitalization for unstable angina, and recurrence of cardiac symptoms. The non-inferiority margin was 3 percentage difference in diagnostic accuracy and C-index. In total, 1801 subjects (62.7 ± 11.1 years) were included, of whom 893 and 908 were assigned to the FAAI-based and semi-automated modes, respectively. Image processing times were 121.0 ± 18.6 and 433.5 ± 68.4 s, respectively (p<0.001). Scan-to-report release times were 6.4 ± 2.7 and 10.5 ± 3.8 h, respectively (p< 0.001). Of all subjects, 152 and 159 in the FAAI-based and semi-automated modes, respectively, subsequently underwent invasive coronary angiography. The diagnostic accuracies for obstructive CAD were 94.7% (89.9-97.7%) and 94.3% (89.5-97.4%), respectively (difference 0.4%). Of all subjects, 779 and 784 in the FAAI-based and semi-automated modes were followed for 589 ± 182 days, respectively, and the C-statistic for cardiovascular events were 0.75 (0.67 to 0.83) and 0.74 (0.66 to 0.82) (difference 1%). FAAI-based CCTA image processing significantly improves workflow efficiency than semi-automated mode, and is non-inferior in diagnosing obstructive CAD and risk stratification for cardiovascular events. Conventional coronary CT angiography image processing is semi-automated. This observation shows that fully automated artificial intelligence-based image processing greatly improves efficiency, and maintains high diagnostic accuracy and the effectiveness in stratifying patients for cardiovascular events. • Coronary CT angiography (CCTA) relies heavily on high-quality and fast image processing. • Full-automation CCTA image processing is clinically non-inferior to the semi-automated mode. • Full automation can facilitate the application of CCTA in early detection of coronary artery disease.

Dataset for Automatic Region-based Coronary Artery Disease Diagnostics Using X-Ray Angiography Images

X-ray coronary angiography is the most common tool for the diagnosis and treatment of coronary artery disease. It involves the injection of contrast agents into coronary vessels using a catheter to highlight the coronary vessel structure. Typically, multiple 2D X-ray projections are recorded from different angles to improve visualization. Recent advances in the development of deep-learning-based tools promise significant improvement in diagnosing and treating coronary artery disease. However, the limited public availability of annotated X-ray coronary angiography image datasets presents a challenge for objective assessment and comparison of existing tools and the development of novel methods. To address this challenge, we introduce a novel ARCADE dataset with 2 objectives: coronary vessel classification and stenosis detection. Each objective contains 1500 expert-labeled X-ray coronary angiography images representing: i) coronary artery segments; and ii) the locations of stenotic plaques. These datasets will serve as a benchmark for developing new methods and assessing existing approaches for the automated diagnosis and risk assessment of coronary artery disease.

Coronary artery angiography image vessel segmentation method based on Feature Pyramid Network

Coronary artery angiography image vessel segmentation method based on Feature Pyramid Network

Relationship Between Total Leucocyte Count And Coronary Atherosclerosis Detected By Dual-Source Multi-Slice Computed Tomographic Coronary Angiography

Relationship Between Total Leucocyte Count And Coronary Atherosclerosis Detected By Dual-Source Multi-Slice Computed Tomographic Coronary Angiography