Improved long-term prognostic value of coronary CT angiography-derived plaque measures and clinical parameters on adverse cardiac outcome using machine learning

Machine Learning vs Traditional Approaches to Predict All-Cause Mortality for Acute Coronary Syndrome: A Systematic Review and Meta-analysis

BACKGROUND

Acute coronary syndrome (ACS) remains one of the leading causes of death globally. Accurate and reliable mortality risk prediction of ACS patients is essential for developing targeted treatment strategies and improve prognostication. Traditional models for risk stratification such as the GRACE and TIMI risk scores offer moderate discriminative value, and do not incorporate contemporary predictors of ACS prognosis. Machine learning (ML) models have emerged as an alternate method that may offer improved risk assessment. This review compares ML models with traditional risk scores for predicting all-cause mortality in patients with ACS.

METHODS

PubMed, Embase, Web of Science, Cochrane, CINAHL, Scopus, and IEEE XPlore databases were searched through October 30, 2024, as well as Google Scholar and manual screening of reference lists from included studies and the grey literature for studies comparing ML models with traditional statistical methods for event prediction of ACS patients. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals (CIs) in estimating risk of all-cause mortality.

RESULTS

Twelve studies were included (250,510 patients). The summary C-statistic of best-performing ML models across all end points was 0.88 (95% CI 0.86-0.91), compared with 0.82 (95% CI 0.80-0.85) for traditional methods. The difference in C-statistic between ML models and traditional methods was 0.06 (P < 0.0007). Five studies undertook external validation. The PROBAST tool demonstrated high risk of bias for all studies. Common sources of bias included reporting bias and selection bias. Best-performing ML models demonstrated superior discrimination of all-cause mortality for ACS patients compared with traditional risk scores.

CONCLUSIONS

Despite outperforming well established prognostic tools such as the GRACE and TIMI scores, current clinical applications of ML approaches remain uncertain, particularly in view of the need for greater model validation.

Clinical features and long-term outcomes in patients under 35 years with coronary artery disease: Nested case-control study

INTRODUCTION AND OBJECTIVES

Coronary artery disease (CAD) is a globally significant cardiovascular condition, ranking among the leading causes of morbidity and mortality. CAD has been predominantly associated with advanced age and classic cardiovascular risk factors. However, over the past decades, there has been a concerning rise in its occurrence among young adults, including patients under 35 years old. The present study analyzes the clinical features and outcomes of patients aged ≤35 years with CAD, compared to two age-matched control groups.

METHOD

A nested case-control study of ≤35-year-old patients referred for coronary angiography due to clinical suspicion of CAD. Patients were divided into three groups: patients ≤35 years with CAD, subjects ≤35 years without CAD, and young patients ≥36-40 years with CAD.

RESULTS

Of the 19321 coronary angiographies performed at our center over 10 years, 408 (2.1%) patients were ≤40 years old, 109 patients aged ≤35 years. Risk factors that showed a relationship with the presence of CAD were smoking (OR 2.49; 95% CI 1.03-6.03; p=0.042) and family history of coronary disease (OR 6.70; 95% CI 1.46-30.65; p=0.014). The group aged ≤35 years with CAD exhibited a risk of major cardiovascular adverse events (MACE) (HR 13.3; 95% CI 1.75-100; p<0.001) than subjects ≤35 years without CAD. The probability of major adverse cardiovascular events was associated with being ≤35 years old, diabetes, dyslipidemia, and depression.

CONCLUSION

Patients aged ≤35 exhibited a poor long-term prognosis, with a high risk of new revascularization and acute myocardial infarction during the follow-up period. Focusing on preventive measures can have a significant impact on overall prognosis.

Application of the Unbalanced Ensemble Algorithm for Prognostic Prediction Outcomes of All-Cause Mortality in Coronary Heart Disease Patients Comorbid with Hypertension

Purpose

This study sought to develop an unbalanced-ensemble model that could accurately predict death outcomes of patients with comorbid coronary heart disease (CHD) and hypertension and evaluate the factors contributing to death.

Patients and Methods

Medical records of 1058 patients with coronary heart disease combined with hypertension and excluding those acute coronary syndrome were collected. Patients were followed-up at the first, third, sixth, and twelfth months after discharge to record death events. Follow-up ended two years after discharge. Patients were divided into survival and nonsurvival groups. According to medical records, gender, smoking, drinking, COPD, cerebral stroke, diabetes, hyperhomocysteinemia, heart failure and renal insufficiency of the two groups were sorted and compared and other influencing factors of the two groups, feature selection was carried out to construct models. Owing to data unbalance, we developed four unbalanced-ensemble prediction models based on Balanced Random Forest (BRF), EasyEnsemble, RUSBoost, SMOTEBoost and the two base classification algorithms based on AdaBoost and Logistic. Each model was optimised using hyperparameters based on GridSearchCV and evaluated using area under the curve (AUC), sensitivity, recall, Brier score, and geometric mean (G-mean). Additionally, to understand the influence of variables on model performance, we constructed a SHapley Additive explanation (SHAP) model based on the optimal model.

Results

There were significant differences in age, heart rate, COPD, cerebral stroke, heart failure and renal insufficiency in the nonsurvival group compared with the survival group. Among all models, BRF yielded the highest AUC (0.810; 95% CI, 0.778-0.839), sensitivity (0.990; 95% CI, 0.981-1.000), recall (0.990; 95% CI, 0.981-1.000), and G-mean (0.806; 95% CI, 0.778-0.827), and the lowest Brier score (0.181; 95% CI, 0.178-0.185). Therefore, we identified BRF as the optimal model. Furthermore, red blood cell count (RBC), body mass index (BMI), and lactate dehydrogenase were found to be important mortality-associated risk factors.

Conclusion

BRF combined with advanced machine learning methods and SHAP is highly effective and accurately predicts mortality in patients with CHD comorbid with hypertension. This model has the potential to assist clinicians in modifying treatment strategies to improve patient outcomes.

Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions

In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.

Multimodal Cardiac Imaging Revisited by Artificial Intelligence: An Innovative Way of Assessment or Just an Aid?

Cardiovascular disease remains a leading global health challenge, necessitating advanced diagnostic approaches. This review explores the integration of artificial intelligence (AI) in multimodal cardiac imaging, tracing its evolution from early X-rays to contemporary techniques such as CT, MRI, and nuclear imaging. AI, particularly machine learning and deep learning, significantly enhances cardiac diagnostics by estimating biological heart age, predicting disease risk, and optimizing heart failure management through adaptive algorithms without explicit programming or feature engineering. Key contributions include AI's transformative role in non-invasive coronary artery disease diagnosis, arrhythmia detection via wearable devices, and personalized treatment strategies. Despite substantial progress, challenges including data standardization, algorithm validation, regulatory approval, and ethical considerations must be addressed to fully harness AI's potential. Collaborative efforts among clinicians, scientists, industry stakeholders, and regulatory bodies are essential for the safe and effective deployment of AI in cardiac imaging, promising enhanced diagnostics and personalized patient care.

Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review

BACKGROUND

A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation.

METHODS

PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789).

RESULTS

In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively.

CONCLUSION

AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.

Prognostic value of plaque volume combined with CT fractional flow reserve in patients with suspected coronary artery disease

AIM

To investigate the prognostic value of quantitative plaque volume on coronary computed tomography (CT) angiography (CTA) combined with CT fractional flow reserve (CT-FFR) for major adverse cardiac events (MACE) in suspected coronary artery disease (CAD) patients.

MATERIALS AND METHODS

Patients who underwent coronary CTA with clinically suspected CAD were enrolled retrospectively in this study. Patients' baseline, Framingham Risk Score (FRS), coronary CTA plaque assessment, and CT-FFR were analysed retrospectively. Study outcomes included rehospitalisation and MACE (ST-segment elevation myocardial infarction, unstable angina, or non-ST-segment elevation myocardial infarction, revascularisation, and cardiac death).

RESULTS

There were 251 patients in the study, with a follow-up period of 1-6.58 years. Mean age was 61.16 ± 10.45 years and 146 (58%) patients were male. Higher CT-adapted Leaman score and quantitative plaque volume were found in patients with FRS >0.2 regardless of categorical or continuous variables. Coronary scores, quantitative plaque parameters, and CT-FFR were associated with MACE and rehospitalisation in univariate analysis. In model 1, CT-FFR was associated with MACE in multivariate Cox analysis when adjusted for FRS and CT-adapted Leaman score. Quantitative plaque parameters including calcified plaque volume, fibro-fatty plaque volume, low-attenuation plaque volume, non-calcified plaque volume, and total plaque volume were significantly associated with MACE and improved overall prognostic performance in a model adjusted for CT-FFR.

CONCLUSION

Additional quantitative plaque volume and CT-FFR further improve the predictive incremental value based on risk factor scores for prognostic prediction in patients. Adding quantitative plaque volume combined with CT-FFR analysis to anatomical and clinical assessment will be further beneficial to predict patients' prognosis of MACE.

Artificial intelligence in cardiac computed tomography

Artificial Intelligence (AI) is a broad discipline of computer science and engineering. Modern application of AI encompasses intelligent models and algorithms for automated data analysis and processing, data generation, and prediction with applications in visual perception, speech understanding, and language translation. AI in healthcare uses machine learning (ML) and other predictive analytical techniques to help sort through vast amounts of data and generate outputs that aid in diagnosis, clinical decision support, workflow automation, and prognostication. Coronary computed tomography angiography (CCTA) is an ideal union for these applications due to vast amounts of data generation and analysis during cardiac segmentation, coronary calcium scoring, plaque quantification, adipose tissue quantification, peri-operative planning, fractional flow reserve quantification, and cardiac event prediction. In the past 5 years, there has been an exponential increase in the number of studies exploring the use of AI for cardiac computed tomography (CT) image acquisition, de-noising, analysis, and prognosis. Beyond image processing, AI has also been applied to improve the imaging workflow in areas such as patient scheduling, urgent result notification, report generation, and report communication. In this review, we discuss algorithms applicable to AI and radiomic analysis; we then present a summary of current and emerging clinical applications of AI in cardiac CT. We conclude with AI's advantages and limitations in this new field.

Machine learning based ischemia-specific stenosis prediction: A Chinese multicenter coronary CT angiography study

OBJECTIVES

To evaluate the performance of coronary computed tomography angiography (CCTA) derived characteristics including CT derived fractional flow reserve (CT-FFR) with FFR as a reference standard in identifying the lesion-specific ischemia by machine learning (ML) algorithms.

METHODS

The retrospective analysis enrolled 596 vessels in 462 patients (mean age, 61 years ± 11 [SD]; 71.4 % men) with suspected coronary artery disease who underwent CCTA and invasive FFR. The data were divided into training cohort, internal validation cohort, external validation cohorts 1 and 2 according to participating centers. All CCTA-derived parameters, which contained 10 qualitative and 33 quantitative plaque parameters, were collected to establish ML model. The Boruta and unsupervised clustering algorithm were implemented to select important and non-redundant parameters. Finally, the eight features with the highest mean importance were included for further ML model establishment and decision tree building. Five models were built to predict lesion-specific ischemia: stenosis degree from CCTA, CT-FFR, ΔCT-FFR, ML model and nested model.

RESULTS

Low-attenuation plaque, bend and lesion length were the main predictors of ischemia-specific lesions. Of 5 models, the ML model showed favorable discrimination for ischemia-specific lesions in the training and three validation sets (area under the curve [95 % confidence interval], 0.93 [0.90-0.96], 0.86 [0.79-0.94], 0.88 [0.83-0.94], and 0.90 [0.84-0.96], respectively). The nested model which combined the ML model and CT-FFR showed better diagnostic efficacy (AUC [95 %CI], 0.96 [0.94-0.99], 0.92 [0.86-0.99], 0.92 [0.86-0.99] and 0.94 [0.91-0.98], respectively; all P < 0.05), and net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were significantly higher than CT-FFR alone.

CONCLUSIONS

Comprehensive CCTA-derived multiparameter model could better predict the ischemia-specific lesions by ML algorithms compared to stenosis degree from CTA, CT-FFR and ΔCT-FFR. Decision tree can be used to predict myocardial ischemia effectively.

Prognostic Utility of Coronary Computed Tomography Angiography-derived Plaque Information on Long-term Outcome in Patients With and Without Diabetes Mellitus

PURPOSE

To investigate the long-term prognostic value of coronary computed tomography angiography (cCTA)-derived plaque information on major adverse cardiac events (MACE) in patients with and without diabetes mellitus.

MATERIALS AND METHODS

In all, 64 patients with diabetes (63.3±10.1 y, 66% male) and suspected coronary artery disease who underwent cCTA were matched with 297 patients without diabetes according to age, sex, cardiovascular risk factors, and statin and antithrombotic therapy. MACE were recorded. cCTA-derived risk scores and plaque measures were assessed. The discriminatory power to identify MACE was evaluated using multivariable regression analysis and concordance indices.

RESULTS

After a median follow-up of 5.4 years, MACE occurred in 31 patients (8.6%). In patients with diabetes, cCTA risk scores and plaque measures were significantly higher compared with nondiabetic patients (all P <0.05). The following plaque measures were predictors of MACE using multivariable Cox regression analysis (hazard ratio [HR]) in patients with diabetes: segment stenosis score (HR=1.20, P <0.001), low-attenuation plaque (HR=3.47, P =0.05), and in nondiabetic patients: segment stenosis score (HR=1.92, P <0.001), Agatston score (HR=1.0009, P =0.04), and low-attenuation plaque (HR=4.15, P =0.04). A multivariable model showed a significantly improved C-index of 0.96 (95% confidence interval: 0.94-0.0.97) for MACE prediction, when compared with single measures alone.

CONCLUSION

Diabetes is associated with a significantly higher extent of coronary artery disease and plaque features, which have independent predictive values for MACE. cCTA-derived plaque information portends improved risk stratification of patients with diabetes beyond the assessment of obstructive stenosis on cCTA alone with subsequent impact on individualized treatment decision-making.

Exploring the EVolution in PrognOstic CapabiLity of MUltisequence Cardiac MagneTIc ResOnance in PatieNts Affected by Takotsubo Cardiomyopathy Based on Machine Learning Analysis: Design and Rationale of the EVOLUTION Study

PURPOSE

Takotsubo cardiomyopathy (TTC) is a transient but severe acute myocardial dysfunction with a wide range of outcomes from favorable to life-threatening. The current risk stratification scores of TTC patients do not include cardiac magnetic resonance (CMR) parameters. To date, it is still unknown whether and how clinical, trans-thoracic echocardiography (TTE), and CMR data can be integrated to improve risk stratification.

METHODS

EVOLUTION (Exploring the eVolution in prognOstic capabiLity of mUlti-sequence cardiac magneTIc resOnance in patieNts affected by Takotsubo cardiomyopathy) is a multicenter, international registry of TTC patients who will undergo a clinical, TTE, and CMR evaluation. Clinical data including demographics, risk factors, comorbidities, laboratory values, ECG, and results from TTE and CMR analysis will be collected, and each patient will be followed-up for in-hospital and long-term outcomes. Clinical outcome measures during hospitalization will include cardiovascular death, pulmonary edema, arrhythmias, stroke, or transient ischemic attack.Clinical long-term outcome measures will include cardiovascular death, pulmonary edema, heart failure, arrhythmias, sudden cardiac death, and major adverse cardiac and cerebrovascular events defined as a composite endpoint of death from any cause, myocardial infarction, recurrence of TTC, transient ischemic attack, and stroke. We will develop a comprehensive clinical and imaging score that predicts TTC outcomes and test the value of machine learning models, incorporating clinical and imaging parameters to predict prognosis.

CONCLUSIONS

The main goal of the study is to develop a comprehensive clinical and imaging score, that includes TTE and CMR data, in a large cohort of TTC patients for risk stratification and outcome prediction as a basis for possible changes in patient management.

Prognostic Value of Machine Learning-based Time-to-Event Analysis Using Coronary CT Angiography in Patients with Suspected Coronary Artery Disease

Purpose

To assess the long-term prognostic value of a machine learning (ML) approach in time-to-event analyses incorporating coronary CT angiography (CCTA)-derived and clinical parameters in patients with suspected coronary artery disease.

Materials and Methods

The retrospective analysis included patients with suspected coronary artery disease who underwent CCTA between October 2004 and December 2017. Major adverse cardiovascular events were defined as the composite of all-cause death, myocardial infarction, unstable angina, or late revascularization (>90 days after index scan). Clinical and CCTA-derived parameters were assessed as predictors of major adverse cardiovascular events and incorporated into two models: a Cox proportional hazards model with recursive feature elimination and an ML model based on random survival forests. Both models were trained and validated by employing repeated nested cross-validation. Harrell concordance index (C-index) was used to assess the predictive power.

Results

A total of 5457 patients (mean age, 61 years ± 11 [SD]; 3648 male patients) were evaluated. The predictive power of the ML model (C-index, 0.74; 95% CI: 0.71, 0.76) was significantly higher than the Cox model (C-index, 0.71; 95% CI: 0.68, 0.74; P = .02). The ML model also outperformed the segment stenosis score (C-index, 0.69; 95% CI: 0.66, 0.72; P < .001), which was the best performing CCTA-derived parameter, and patient age (C-index, 0.66; 95% CI: 0.63, 0.69; P < .001), the best performing clinical parameter.

Conclusion

An ML model for time-to-event analysis based on random survival forests had higher performance in predicting major adverse cardiovascular events compared with established clinical or CCTA-derived metrics and a conventional Cox model.Keywords: Machine Learning, CT Angiography, Cardiac, Arteries, Heart, Arteriosclerosis, Coronary Artery DiseaseSupplemental material is available for this article.© RSNA, 2023.

The Cost Effectiveness of Coronary CT Angiography and the Effective Utilization of CT-Fractional Flow Reserve in the Diagnosis of Coronary Artery Disease

Given the high global disease burden of coronary artery disease (CAD), a major problem facing healthcare economic policy is identifying the most cost-effective diagnostic strategy for patients with suspected CAD. The aim of this review is to assess the long-term cost-effectiveness of coronary computed tomography angiography (CCTA) when compared with other diagnostic modalities and to define the cost and effective diagnostic utilization of computed tomography-fractional flow reserve (CT-FFR). A search was conducted through the MEDLINE database using PubMed with 16 of 119 manuscripts fitting the inclusion and exclusion criteria for review. An analysis of the data included in this review suggests that CCTA is a cost-effective strategy for both low risk acute chest pain patients presenting to the emergency department (ED) and low-to-intermediate risk stable chest pain outpatients. For patients with intermediate-to-high risk, CT-FFR is superior to CCTA in identifying clinically significant stenosis. In low-to-intermediate risk patients, CCTA provides a cost-effective diagnostic strategy with the potential to reduce economic burden and improve long-term health outcomes. CT-FFR should be utilized in intermediate-to-high risk patients with stenosis of uncertain clinical significance. Long-term analysis of cost-effectiveness and diagnostic utility is needed to determine the optimal balance between the cost-effectiveness and diagnostic utility of CT-FFR.

Artificial Intelligence in Coronary CT Angiography: Current Status and Future Prospects

Coronary heart disease (CHD) is the leading cause of mortality in the world. Early detection and treatment of CHD are crucial. Currently, coronary CT angiography (CCTA) has been the prior choice for CHD screening and diagnosis, but it cannot meet the clinical needs in terms of examination quality, the accuracy of reporting, and the accuracy of prognosis analysis. In recent years, artificial intelligence (AI) has developed rapidly in the field of medicine; it played a key role in auxiliary diagnosis, disease mechanism analysis, and prognosis assessment, including a series of studies related to CHD. In this article, the application and research status of AI in CCTA were summarized and the prospects of this field were also described.

Prediction of acute myocardial infarction by multi-parameter coronary computed tomography angiography

AIM

To investigate the performance of multi-parameter coronary computed tomography angiography (CCTA), including stenosis, plaque qualitative-quantitative characteristics, and fractional flow reserve derived from CCTA (FFRct), to predict acute myocardial infarction (AMI) and build a combined model.

MATERIALS AND METHODS

Thirty patients with AMI 90 days after CCTA and 120 matched patients without AMI were enrolled retrospectively. Multiple CCTA parameters were analysed and compared. Independent risk factors were obtained through univariate and multivariate regression analyses, after which a multi-parameter model was built.

RESULTS

A total of 150 patients were analysed successfully. The multi-parameter CCTA model (area under the curve, 0.944; p<0.001) had a higher predictive value than each single parameter (p<0.001, all). Independent risk factors were intra-plaque dye penetration (IDP; odds ratio [OR], 8.373; p=0.002), lipid plaque volume (LPV; OR, 1.263; p<0.001), and FFRct ≤0.83 (OR, 8.092; p=0.001).

CONCLUSION

This one-stop multi-parameter CCTA model, comprising IDP, LPV, and FFRct as independent risk factors, has good performance to predict AMI.

Predicting major adverse cardiac events based on multi-parameter coronary computed tomography angiography

OBJECTIVE

To build a nomogram model to improve the prediction of major adverse cardiac events (MACE) using multi-parameter coronary computed tomography angiography (CCTA).

METHODS

All patients underwent CCTA. Those who developed MACE 90 days later but within 2 years between January 2008 and December 2018 were retrospectively enrolled as MACE group, while those without MACE were 1:1 propensity score matched in the control group. CCTA stenosis, plaque qualitative-quantitative characteristics, and fractional flow reserve derived from computed tomography angiography (FFRct) were analyzed and compared between the two groups. The independent risk factors for predicting MACE were obtained through univariate and multivariate regression analysis, after which multi-parameter models were built to predict MACE. Finally, the nomogram for predicting MACE was created using the independent risk factors from multivariate regression analysis.

RESULTS

A total of 483 vessels in 260 patients were successfully analyzed. The combination of CCTA stenosis, plaque qualitative-quantitative characteristics, and FFRct (AUC = 0.922, P<0.001) showed a higher predictive value compared to CCTA stenosis alone, FFRct alone, plaque qualitative-quantitative characteristics alone, CCTA stenosis combined with plaque qualitative-quantitative characteristics, and CCTA stenosis combined with FFRct (all P <0.001). Independent risk factors were CCTA stenosis ≥50%, low attenuation plaque, positive remodeling, napkin ring sign, lipid plaque volume proportion, and FFRct. Subsequently, a nomogram was created using these independent risk factors.

CONCLUSIONS

The multi-parameter CCTA model has improved performance in predicting MACE. Nomogram for predicting MACE, which includes these factors, represents a practical and easy-to-use method in the clinical setting. This article is protected by copyright. All rights reserved.

Machine learning applications in cardiac computed tomography: a composite systematic review

Artificial intelligence and machine learning (ML) models are rapidly being applied to the analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Six searches were performed in Medline, Embase, and the Cochrane Library up to November 2021 for (i) CT-fractional flow reserve (CT-FFR), (ii) atrial fibrillation (AF), (iii) aortic stenosis, (iv) plaque characterization, (v) fat quantification, and (vi) coronary artery calcium score. We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-FFR can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue can also be automatically, accurately, and rapidly quantified. Effective ML algorithms have been developed to streamline and optimize the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of AF. In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT.

Long-COVID diagnosis: From diagnostic to advanced AI-driven models

SARS-COV 2 is recognized to be responsible for a multi-organ syndrome. In most patients, symptoms are mild. However, in certain subjects, COVID-19 tends to progress more severely. Most of the patients infected with SARS-COV2 fully recovered within some weeks. In a considerable number of patients, like many other viral infections, various long-lasting symptoms have been described, now defined as "long COVID-19 syndrome". Given the high number of contagious over the world, it is necessary to understand and comprehend this emerging pathology to enable early diagnosis and improve patents outcomes. In this scenario, AI-based models can be applied in long-COVID-19 patients to assist clinicians and at the same time, to reduce the considerable impact on the care and rehabilitation unit. The purpose of this manuscript is to review different aspects of long-COVID-19 syndrome from clinical presentation to diagnosis, highlighting the considerable impact that AI can have.

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.

The evolving role of artificial intelligence in cardiac image analysis

Research in artificial intelligence (AI) have progressed over the last decade. The field of cardiac imaging has seen significant developments using newly developed deep learning methods for automated image analysis and AI tools for disease detection and prognostication. This review article is aimed at those without special background in AI. We review AI concepts and we survey the growing contemporary applications of AI for image analysis in echocardiography, nuclear cardiology, cardiac computed tomography, cardiac magnetic resonance, and invasive angiography.

Artificial intelligence in computed tomography plaque characterization: A review

Cardiovascular disease (CVD) is associated with high mortality around the world. Prevention and early diagnosis are key targets in reducing the socio-economic burden of CVD. Artificial intelligence (AI) has experienced a steady growth due to technological innovations that have to lead to constant development. Several AI algorithms have been applied to various aspects of CVD in order to improve the quality of image acquisition and reconstruction and, at the same time adding information derived from the images to create strong predictive models. In computed tomography angiography (CTA), AI can offer solutions for several parts of plaque analysis, including an automatic assessment of the degree of stenosis and characterization of plaque morphology. A growing body of evidence demonstrates a correlation between some type of plaques, so-called high-risk plaque or vulnerable plaque, and cardiovascular events, independent of the degree of stenosis. The radiologist must apprehend and participate actively in developing and implementing AI in current clinical practice. In this current overview on the existing AI literature, we describe the strengths, limitations, recent applications, and promising developments of employing AI to plaque characterization with CT.

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

Cardiac computed tomography angiography (CCTA) is widely used as a diagnostic tool for evaluation of coronary artery disease (CAD). Despite the excellent capability to rule-out CAD, CCTA may overestimate the degree of stenosis; furthermore, CCTA analysis can be time consuming, often requiring advanced postprocessing techniques. In consideration of the most recent ESC guidelines on CAD management, which will likely increase CCTA volume over the next years, new tools are necessary to shorten reporting time and improve the accuracy for the detection of ischemia-inducing coronary lesions. The application of artificial intelligence (AI) may provide a helpful tool in CCTA, improving the evaluation and quantification of coronary stenosis, plaque characterization, and assessment of myocardial ischemia. Furthermore, in comparison with existing risk scores, machine-learning algorithms can better predict the outcome utilizing both imaging findings and clinical parameters. Medical AI is moving from the research field to daily clinical practice, and with the increasing number of CCTA examinations, AI will be extensively utilized in cardiac imaging. This review is aimed at illustrating the state of the art in AI-based CCTA applications and future clinical scenarios.