Diagnosis Of Coronary Artery Disease Research Articles

Novel CAD Diagnosis Method Based on Search, PCA, and AdaBoostM1 Techniques

Background: Cardiovascular diseases (CVDs) are the primary cause of mortality worldwide, resulting in a growing number of annual fatalities. Coronary artery disease (CAD) is one of the basic types of CVDs, and early diagnosis of CAD is crucial for convenient treatment and decreasing mortality rates. In the literature, several studies use many features for CAD diagnosis. However, due to the large number of features used in these studies, the possibility of early diagnosis is reduced. Methods: For this reason, in this study, a new method that uses only five features—age, hypertension, typical chest pain, t-wave inversion, and region with regional wall motion abnormality—and is a combination of eight different search techniques, principal component analysis (PCA), and the AdaBoostM1 algorithm has been proposed for early and accurate CAD diagnosis. Results: The proposed method is devised and tested on a benchmark dataset called Z-Alizadeh Sani. The performance of the proposed method is tested with a variety of metrics and compared with basic machine-learning techniques and the existing studies in the literature. The experimental results have shown that the proposed method is efficient and achieves the best classification performance, with an accuracy of 91.8%, ever reported on the Z-Alizadeh Sani dataset with so few features. Conclusions: As a result, medical practitioners can utilize the proposed approach for diagnosing CAD early and accurately.

Educational attainment, severity and short-term prognosis of intracerebral haemorrhage

BackgroundEducational attainment is a critical social determinant of health that impacts the risk and severity of incident ischaemic stroke, but less is known of its impact on intracerebral haemorrhage (ICH)....

Performance of the left ventricular myocardium in patients with a negative test result during exercise stress echocardiography

Aim. To determine the possibility of identifying patients with significant coronary artery disease (CAD) by evaluation of the left ventricular (LV) myocardial work indicators by constructing pressure-strain loops despite a negative test result during exercise stress echocardiography. Materials and methods. The study included 79 patients with suspected or previously confirmed CAD, of which 47 (59%) men, who had a negative test result during exercise stress echocardiography on the treadmill. Global work index (GWI), global constructive work (GCW), global wasted work (GWW) and global work efficiency (GWE) were evaluated using the technique of constructing pressure-strain loops at rest and at peak exercise. Results. With similar systolic blood pressure and LV ejection fraction at peak exercise between the control group and the group of patients with single-vessel CAD, there was revealed significant difference in GWI, GCW and GWE at peak exercise. In patients with multi-vessel CAD, GWI and GCW also significantly differed from the control group at peak exercise. Conclusion. The evaluation of LV myocardial work indicators may be recommended for a more accurate diagnosis of CAD in negative or unreliable test results during exercise stress echocardiography.

C-5 | Cardiac Imaging Titans: A Comprehensive Meta-Analysis Comparing Computed Tomography Coronary Angiography (CTA) and Cardiac Magnetic Resonance (CMR) for Precision Diagnosis of Coronary Artery Disease (CAD)

C-5 | Cardiac Imaging Titans: A Comprehensive Meta-Analysis Comparing Computed Tomography Coronary Angiography (CTA) and Cardiac Magnetic Resonance (CMR) for Precision Diagnosis of Coronary Artery Disease (CAD)

G2ViT: Graph Neural Network-Guided Vision Transformer Enhanced Network for retinal vessel and coronary angiograph segmentation

Blood vessel segmentation is a crucial stage in extracting morphological characteristics of vessels for the clinical diagnosis of fundus and coronary artery disease. However, traditional convolutional neural networks (CNNs) are confined to learning local vessel features, making it challenging to capture the graph structural information and fail to perceive the global context of vessels. Therefore, we propose a novel graph neural network-guided vision transformer enhanced network (G2ViT) for vessel segmentation. G2ViT skillfully orchestrates the Convolutional Neural Network, Graph Neural Network, and Vision Transformer to enhance comprehension of the entire graphical structure of blood vessels. To achieve deeper insights into the global graph structure and higher-level global context cognizance, we investigate a graph neural network-guided vision transformer module. This module constructs graph-structured representation in an unprecedented manner using the high-level features extracted by CNNs for graph reasoning. To increase the receptive field while ensuring minimal loss of edge information, G2ViT introduces a multi-scale edge feature attention module (MEFA), leveraging dilated convolutions with different dilation rates and the Sobel edge detection algorithm to obtain multi-scale edge information of vessels. To avoid critical information loss during upsampling and downsampling, we design a multi-level feature fusion module (MLF2) to fuse complementary information between coarse and fine features. Experiments on retinal vessel datasets (DRIVE, STARE, CHASE_DB1, and HRF) and coronary angiography datasets (DCA1 and CHUAC) indicate that the G2ViT excels in robustness, generality, and applicability. Furthermore, it has acceptable inference time and computational complexity and presents a new solution for blood vessel segmentation.

Clinical implementation of a fully automated quantitative perfusion cardiovascular magnetic resonance imaging workflow with a simplified dual-bolus contrast administration scheme

This study clinically implemented a ready-to-use quantitative perfusion (QP) cardiovascular magnetic resonance (QP CMR) workflow, encompassing a simplified dual-bolus gadolinium-based contrast agent (GBCA) administration scheme and fully automated QP image post-processing. Twenty-five patients with suspected obstructive coronary artery disease (CAD) underwent both adenosine stress perfusion CMR and an invasive coronary angiography or coronary computed tomography angiography. The dual-bolus protocol consisted of a pre-bolus (0.0075 mmol/kg GBCA at 0.5 mmol/ml concentration + 20 ml saline) and a main bolus (0.075 mmol/kg GBCA at 0.5 mmol/ml concentration + 20 ml saline) at an infusion rate of 3 ml/s. The arterial input function curves showed excellent quality. Stress MBF ≤ 1.84 ml/g/min accurately detected obstructive CAD (area under the curve 0.79; 95% Confidence Interval: 0.66 to 0.89). Combined visual assessment of color pixel QP maps and conventional perfusion images yielded a diagnostic accuracy of 84%, sensitivity of 70% and specificity of 93%. The proposed easy-to-use dual-bolus QP CMR workflow provides good image quality and holds promise for high accuracy in diagnosis of obstructive CAD. Implementation of this approach has the potential to serve as an alternative to current methods thus increasing the accessibility to offer high-quality QP CMR imaging by a wide range of CMR laboratories.

Temporal Relationship-Aware Treadmill Exercise Test Analysis Network for Coronary Artery Disease Diagnosis.

The treadmill exercise test (TET) serves as a non-invasive method for the diagnosis of coronary artery disease (CAD). Despite its widespread use, TET reports are susceptible to external influences, heightening the risk of misdiagnosis and underdiagnosis. In this paper, we propose a novel automatic CAD diagnosis approach. The proposed approach introduces a customized preprocessing method to obtain clear electrocardiograms (ECGs) from individual TET reports. Additionally, it presents TETDiaNet, a novel neural network designed to explore the temporal relationships within TET ECGs. Central to TETDiaNet is the TETDia block, which mimics clinicians' diagnostic processes to extract essential diagnostic information. This block encompasses an intra-state contextual learning module and an inter-state contextual learning module, modeling the temporal relationships within a single state and between states, respectively. These two modules help the TETDia block to capture effective diagnosis information by exploring the temporal relationships within TET ECGs. Furthermore, we establish a new TET dataset named TET4CAD for CAD diagnosis. It contains simplified TET reports for 192 CAD patients and 224 non-CAD patients, and each patient undergoes coronary angiography for labeling. Experimental results on TET4CAD underscore the superior performance of the proposed approach, highlighting the discriminative value of the temporal relationships within TET ECGs for CAD diagnosis.

Non-invasive coronary artery disease identification through the iris and bio-demographic health profile features using stacking learning

This study proposes a non-invasive method for predicting Coronary Artery Disease (CAD) using iris analysis, patient data, and Machine Learning (ML), primarily with iris images. It involved 281 participants, comprising 155 CAD patients and 126 non-patient controls, with eye images and biodemographic data collected at a Cardiology outpatient clinic. The study explored three scenarios: Scenario-I focused on biodemographic data, Scenario-II on iris features, and Scenario-III combined iris images and data. Iris processing included location determination, normalization, and heart region selection, with image enhancement via adaptive histogram equalization. Feature extraction through a 2-level wavelet transform generated 272 attributes, including statistical, Gray Level Co-occurrence Matrix, and Gray Level Run Length Matrix features for eight subcomponents. Correlation-based selection identified the best features, and classification employed ML techniques and incorporated stacking learning to enhance the results. Scenario-I achieved the highest accuracy at 83.57% among all evaluated algorithms. In Scenario-II, the proposed algorithm consistently outperformed others, achieving 94.88% accuracy and strong performance in other metrics, highlighting its effectiveness. In Scenario-III, the algorithm maintained superiority with 96.07% accuracy, specificity, recall, and area under the curve values. The proposed algorithm consistently outperforms other methods across scenarios, indicating its potential for CAD diagnosis, making it a promising choice for future CAD systems. The proposed algorithm presents a novel approach to the preliminary diagnosis of CAD, eliminating the necessity for electrocardiography, echocardiography, or effort tests. It also enables seamless integration into telemedicine systems, allowing for tele-diagnosis to conduct preliminary assessments before routine clinical practice.

Economic and healthcare resource utilization assessments of PET imaging in Coronary Artery Disease diagnosis: a systematic review and discussion of opportunities for future economic evaluations

Aims This systematic literature review (SLR) consolidated economic and healthcare resource utilization (HCRU) evidence for positron emission tomography (PET) and single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) to inform future economic evaluations. Materials and methods An electronic search was conducted in MEDLINE, Embase, and Cochrane databases from 2012–2022. Economic and HCRU studies in adults who underwent PET- or SPECT-MPI for coronary artery disease (CAD) diagnosis were eligible. A qualitative methodological assessment of existing economic evaluations, HCRU, and downstream cardiac outcomes was completed. Exploratory meta-analyses of clinical outcomes were performed. Results The search yielded 13,439 results, with 71 records included. Economic evaluations and comparative clinical trials were limited in number and outcome types (HCRU, downstream cardiac outcomes, and diagnostic performance) assessed. No studies included all outcome types and only one economic evaluation linked diagnostic performance to HCRU. The meta-analyses of comparative studies demonstrated significantly higher rates of early- and late-invasive coronary angiography and revascularization for PET- compared to SPECT-MPI; however, the rate of repeat testing was lower with PET-MPI. The rate of acute myocardial infarction was lower, albeit non-significant with PET- vs. SPECT-MPI. Limitations and conclusions This SLR identified economic and HCRU evaluations following PET- and SPECT-MPI for CAD diagnosis and determined that existing studies do not capture all pertinent outcome parameters or link diagnostic performance to downstream HCRU and cardiac outcomes, thus, resulting in simplified assessments of CAD burden. A limitation of this work relates to heterogeneity in study designs, patient populations, and follow-up times of existing studies. Resultingly, it was challenging to pool data in meta-analyses. Overall, this work provides a foundation for the development of comprehensive economic models for PET- and SPECT-MPI in CAD diagnosis, which should link diagnostic outcomes to HCRU and downstream cardiac events to capture the full CAD scope.

Nε-carboxymethyl-lysine and inflammatory cytokines, markers and mediators of coronary artery disease progression in diabetes

This editorial refers to the article “Comparative analysis of Nε-carboxymethyl-lysine and inflammatory markers in diabetic and non-diabetic coronary artery disease patients”, published in the recent issue of the World Journal of Diabetes 2023 is based on glucose metabolism, advanced glycation end products (AGEs), inflammation and adiposity on diabetes and coronary artery disease (CAD). This study has included CAD patients who were stratified according to glycosylated hemoglobin higher than 6.5 and sex-matched. A higher prevalence of hypertension, dyslipidemia, and non-vegetarian diet were found in the diabetic group. These risk factors might influence body weight and adiposity and explain the increment of the left atrium. Although this data was not supported by the study. The diet can also explain the non-enzymatic reactions on lipids, proteins, or nucleic acids and consequently an increment of AGEs. These molecules can emit fluorescence. However, one of the non-fluorescent and most abundant AGEs is Nε-carboxymethyl-lysine (CML). Its association with coronary artery stenosis and severity in the diabetic group might suggest its role as a player in CAD progression. Thus, CML, after binding with its receptor (RAGE), can induce calcification cascade through reactive oxygen species and mitogen-activated protein kinase. Moreover, this interaction AGE-RAGE can cause activation of the transcription nuclear factor-kb and induce inflammatory cytokines. It might explain the relationship between CML and pro-inflammatory cytokines in diabetic and CAD patients. Although this is a population from one center, the determination of CML and inflammatory cytokines might improve the diagnosis of severe and progressive CAD. Future and comparative studies among glycosylated hemoglobin, CML, and other AGE levels according to diagnosis and prognosis value might modify the clinical practice. Although these molecules are irreversible, they can act through a specific receptor inducing a signal transduction that might be modu-lated by inhibitors, antibodies, or siRNA. Further mechanistic studies might improve the development of future preventive therapies for diabetic patients.

Risk Stratification Using Cardiac Imaging: A Comprehensive Review

There are numerous cardiac imaging modalities which aid in the diagnosis and management of coronary artery disease (CAD). Each modality has variable efficacy in detecting stenosis and stratifying risk among those with CAD. Clinicians must evaluate these methods in light of their patients’ clinical presentations, to choose the most appropriate imaging technique. Understanding the unique benefits and indications of each modality aids in the selection of high-value imaging. Following is a review of the available cardiac imaging methods for the identification and risk stratification of CAD.

DuDoCFNet: Dual-Domain Coarse-to-Fine Progressive Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT.

Single-Photon Emission Computed Tomography (SPECT) is widely applied for the diagnosis of coronary artery diseases. Low-dose (LD) SPECT aims to minimize radiation exposure but leads to increased image noise. Limited-view (LV) SPECT, such as the latest GE MyoSPECT ES system, enables accelerated scanning and reduces hardware expenses but degrades reconstruction accuracy. Additionally, Computed Tomography (CT) is commonly used to derive attenuation maps (μ-maps) for attenuation correction (AC) of cardiac SPECT, but it will introduce additional radiation exposure and SPECT-CT misalignments. Although various methods have been developed to solely focus on LD denoising, LV reconstruction, or CT-free AC in SPECT, the solution for simultaneously addressing these tasks remains challenging and under-explored. Furthermore, it is essential to explore the potential of fusing cross-domain and cross-modality information across these interrelated tasks to further enhance the accuracy of each task. Thus, we propose a Dual-Domain Coarse-to-Fine Progressive Network (DuDoCFNet), a multi-task learning method for simultaneous LD denoising, LV reconstruction, and CT-free μ-map generation of cardiac SPECT. Paired dual-domain networks in DuDoCFNet are cascaded using a multi-layer fusion mechanism for cross-domain and cross-modality feature fusion. Two-stage progressive learning strategies are applied in both projection and image domains to achieve coarse-to-fine estimations of SPECT projections and CT-derived μ-maps. Our experiments demonstrate DuDoCFNet's superior accuracy in estimating projections, generating μ-maps, and AC reconstructions compared to existing single- or multi-task learning methods, under various iterations and LD levels. The source code of this work is available at https://github.com/XiongchaoChen/DuDoCFNet-MultiTask.

Значение лучевых методов в диагностике ишемической болезни сердца у конкретного пациента

Purpose: Demonstrate a clinical observation in which to establish a diagnosis it was necessary to use eight methods for diagnosing coronary pathology, four of which are radiation. Material and methods: To establish a diagnosis in a cardiac patient with suspected coronary heart disease (CHD), post-infarction cardiosclerosis, echocardiography (ECG), Holter monitoring (HM), bicycle ergometry (VE), X-ray computed tomography (X-ray computed tomography) to determine calcification of the coronary arteries, single-photon selective computer tomography (SPECT), magnetic resonance computed tomography (MRI), positron emission computed tomography (PET), coronary angiography (CAG). Results: The sequential use of eight diagnostic methods, four from radiation, was established when observing cardiosclerosis with coronary heart disease, cardiosclerosis in the 4, 5, 10, and 11 segments of the heart, complicated by a left ventricular aneurysm in the lower and lateral walls with minor ischemia at the height of physical activity. The need to use SPECT/CT in the complex diagnosis of coronary heart disease consists of using hybrid tomography and sequentially performing two studies in one diagnostic procedure (X-ray computed tomography and SPECT with 99m Tc-technetril) it seems possible to obtain 26 study indicators (with X-ray computed tomography – 4 indicators assessing calcification of the coronary arteries, with SPECT – 11 indicators of perfusion and 11 indicators of myocardial function). Conclusion: A clinical observation of the diagnosis of coronary artery disease with post-infarction cardiosclerosis and left ventricular aneurysm was demonstrated in which eight diagnostic technologies were used (ECG, CM, VE, CT, SPECT, MRI, PET, and CAG), four of which relate to radiation diagnostics (X-ray CT, SPECT, PET, and KAG). A feature of sequential hybrid tomography (X-ray CT and SPECT with 99mTc-technitrile) is that this technology allows you to obtain 26 research indicators.

Ultra-high-resolution CT vs. invasive angiography for detecting hemodynamically significant coronary artery disease: Rationale and methods of the CORE-PRECISION multicenter study

BackgroundDirect coronary arterial evaluation via computed tomography (CT) angiography is the most accurate noninvasive test for the diagnosis of coronary artery disease (CAD). However, diagnostic accuracy is limited in the setting of severe coronary calcification or stents. Ultra-high-resolution CT (UHR-CT) may overcome this limitation, but no rigorous study has tested this hypothesis. MethodsThe CORE-PRECISION is an international, multicenter, prospective diagnostic accuracy study testing the non-inferiority of UHR-CT compared to invasive coronary angiography (ICA) for identifying patients with hemodynamically significant CAD. The study will enroll 150 patients with history of CAD, defined as prior documentation of lumen obstruction, stenting, or a calcium score ≥400, who will undergo UHR-CT before clinically prompted ICA. Assessment of hemodynamically significant CAD by UHR-CT and ICA will follow clinical standards. The reference standard will be the quantitative flow ratio (QFR) with <0.8 defined as abnormal. All data will be analyzed in independent core laboratories. ResultsThe primary outcome will be the comparative diagnostic accuracy of UHR-CT vs. ICA for detecting hemodynamically significant CAD on a patient level. Secondary analyses will focus on vessel level diagnostic accuracy, quantitative stenosis analysis, automated contour detection, in-depth plaque analysis, and others. ConclusionCORE-PRECISION aims to investigate if UHR-CT is non-inferior to ICA for detecting hemodynamically significant CAD in high-risk patients, including those with severe coronary calcification or stents. We anticipate this study to provide valuable insights into the utility of UHR-CT in this challenging population and for its potential to establish a new standard for CAD assessment.

[Multidistrict atherosclerotic disease: epidemiological and clinical framework].

Atherosclerosis is a systemic disease that can involve different arterial districts. Traditionally, the focus of cardiologists has been on the diagnosis and treatment of atherosclerotic coronary artery disease (CAD). However, atherosclerosis localization in other districts is increasingly common and is associated with an increased risk of CAD and, more generally, of adverse cardiovascular events. Although the term peripheral arterial disease (PAD) commonly refers to the localization of atherosclerotic disease in the arterial districts of the lower limbs, in this document, in accordance with the European Society of Cardiology guidelines, the term PAD will be used for all the locations of atherosclerotic disease excluding coronary and aortic ones. The aim of this review is to report updated data on PAD epidemiology, with particular attention to the prevalence and its prognostic impact on patients with CAD. Furthermore, the key points for an appropriate diagnostic framework and a correct pharmacological therapeutic approach are summarized, while surgical/interventional treatment goes beyond the scope of this review.

A hybrid convolutional and transformer network for segmentation of coronary computed tomography angiographic slices

AbstractCoronary artery three‐dimensional reconstruction is essential for preventing, diagnosing, and treating coronary heart disease. This study introduces SegUnet, a lightweight hybrid CNN‐Transformer network for pixel‐level segmentation of coronary artery computed tomography angiography (CTA) slices to enhance the precision of coronary artery reconstruction. The overall SegUnet adopts a U‐shaped encoder‐decoder structure, with a hierarchical Transformer structure serving as the encoder to extract global contextual information; the decoder is enhanced with spatial attention mechanism (SAM) and residual structure to improve its perception and adaptive adjustment abilities for coronary artery vessels. In contrast to other methods, we propose an interpolation‐based multi‐feature fusion bridge to integrate multi‐scale features between encoder levels, capturing their semantic dependencies. Experimental results show that SegUnet achieves a prediction accuracy of 97.23% on the test set of coronary arteries 2D slices. Moreover, the network exhibits excellent performance on Synapse Multi‐Organ Segmentation, highlighting the superiority, effectiveness, and robustness of our proposed method. The outstanding performance of SegUnet in coronary artery segmentation has elevated the quality of three‐dimensional reconstruction of the coronary arteries, exerting a positive impact on the enhancement of diagnosis and treatment of coronary artery diseases.

High-sensitivity cardiac troponin T in detecting obstructive coronary artery disease in hemodialysis patients listed for kidney transplantation.

Cardiovascular diseases are the leading cause of morbidity and mortality in patients with end-stage renal disease. This study aimed to assess the prognostic value of high-sensitivity cardiac troponin T (hs-cTnT) in identifying patients with obstructive coronary artery disease (CAD) among patients on hemodialysis listed for kidney transplantation. The study prospectively enrolled consecutive adult hemodialysis patients listed for kidney transplantation. They underwent laboratory tests and a standardized set of imaging and functional tests, including coronary angiography, according to patient characteristics. The study included 100 consecutive patients (72 men)at a median age of 56.5 years. Ultimately, 48% of the patients were diagnosed with obstructive CAD. Age and plasma hs-cTnT levels predicted the diagnosis of obstructive CAD (OR, 1.13; 95% CI, 1.08-1.20; P < 0.001 and OR, 1.03; 95% CI, 1.01-1.05; P = 0.001, respectively). The calculated cut-off value for age was 53 years, which showed sensitivity of 87.5% and specificity of 76.9% for obstructive CAD diagnosis. The calculated value for hs-cTnT was 0.067 ng/ml, which showed sensitivity of 61.4% and specificity of 82.2% for the detection of obstructive CAD. In patients aged >52 years, 79.2% were diagnosed with obstructive CAD. However, in the group of patients ≤52 years and with hs-cTnT >0.069 ng/ml, the incidence of obstructive CAD was significantly higher than in the group with hs-cTnT level ≤0.069 ng/ml. Baseline hs-cTnT level is a useful prognostic biomarker in the diagnosis of obstructive CAD in hemodialysis patients listed for kidney transplantation.

INFLUENCE OF ANDROGEN STATUS ON THE RISK OF HOSPITALIZATION FOR CARDIOVASCULAR DISEASES IN MEN WITH CORONARY HEART DISEASE

Aim – to create a mathematical model for predicting the risk of hospitalizations within 6 months in men with coronary artery disease (CAD), taking into account androgen status and other clinical and laboratory parameters.Materials and methods. 102 men aged 45-65 with a diagnosis of coronary artery disease were included in the study. Key predictors were identified using ROC analysis, including testosterone level, age, cholesterol, systolic blood pressure, and heart rate. These parameters were integrated into a logistic regression equation to formulate a predictive model for the risk of hospitalization during the next 6 months. The results. The logistic regression model demonstrated reliable predictive capabilities with an area under the ROC curve of 0.790, p&lt;0.05. The coefficients of individual parameters revealed a significant contribution to the overall forecasting accuracy. The obtained formula allows us to estimate the probability of hospitalization during the next six months for men with coronary artery disease.Conclusions: This study created a predictive model for the risk of hospitalization in men with CAD, providing clinicians with a tool for risk assessment and management. Taking androgen status into account along with traditional cardiovascular risk factors increases the accuracy of the received prognosis. The obtained results emphasize the potential of personalized medicine in optimizing patient care. Prospects for further research: Future research should focus on external validation of the developed model in different populations and the study of additional factors that may improve risk prediction. In addition, the study of dynamic changes in the predicted parameters over time can improve the temporal accuracy of the model.

High-Resolution Free-Breathing Automated Quantitative Myocardial Perfusion by Cardiovascular Magnetic Resonance for the Detection of Functionally Significant Coronary Artery Disease.

Current assessment of myocardial ischaemia from stress perfusion cardiovascular magnetic resonance (SP-CMR) largely relies on visual interpretation. This study investigated the use of high-resolution free-breathing SP-CMR with automated quantitative mapping in the diagnosis of coronary artery disease (CAD). Diagnostic performance was evaluated against invasive coronary angiography (ICA) with fractional flow reserve (FFR) measurement. Seven-hundred and three patients were recruited for SP-CMR using the research sequence at 3 Tesla. Of those receiving ICA within 6 months, 80 patients either had FFR measurement, or identification of a chronic total occlusion (CTO) with inducible perfusion defects seen on SP-CMR. Myocardial blood flow (MBF) maps were automatically generated in-line on the scanner following image acquisition at hyperaemic stress and rest, allowing myocardial perfusion reserve (MPR) calculation. 75 coronary vessels assessed by FFR, and 28 vessels with CTO were evaluated at both segmental and coronary territory level. Coronary territory stress MBF and MPR were reduced in FFR-positive (≤ 0.80) regions (median stress MBF: 1.74 [0.90-2.17] ml/min/g; MPR: 1.67 [1.10-1.89]) compared with FFR-negative regions (stress MBF: 2.50 [2.15-2.95] ml/min/g; MPR 2.35 [2.06-2.54] p < 0.001 for both). Stress MBF ≤ 1.94 ml/min/g and MPR ≤ 1.97 accurately detected FFR-positive CAD on a per-vessel basis (area under the curve: 0.85 and 0.96 respectively; p < 0.001 for both). A novel scanner-integrated high-resolution free-breathing SP-CMR sequence with automated in-line perfusion mapping is presented which accurately detects functionally significant CAD.

Evaluating machine learning models for prediction of coronary artery disease

Coronary artery disease (CAD) is a prevailing global health issue and a leading cause of death worldwide. Its accurate and timely diagnosis is crucial for effectively managing the disease and improving patient outcomes. In this study, we conducted a comparative analysis of machine learning (ML)-based approaches to detect and diagnose CAD. A dataset of 918 instances from the UCI ML repository, comprising 11 typical risk factors and CAD predictors, was used for this investigation. The study deployed ML models in Google Colaboratory and PyCaret, testing their efficacy in diagnosing CAD. Our study provides a detailed overview of these ML methodologies, their strengths, and limitations, underscoring the potential of these algorithms to revolutionize CAD diagnosis and treatment. The overall goal of the study is to create a model that can predict the presence or chance of presence of CAD based on different parameters of the patient&amp;rsquo;s history. Findings include the showcased logistic regression model, which was proven to be particularly effective, with an area under curve of 0.88, indicating a high ability to differentiate between patients with and without CAD, and a successful ability to identify clinically key features of CAD such as the presence of exertional angina and chest pain. This study emphasizes the importance of further research in this field to establish ML as a cornerstone of modern healthcare diagnostics.