Artificial intelligence: improving the efficiency of cardiovascular imaging

AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review

The widespread use of medical imaging techniques such as X-rays and computed tomography (CT) has raised significant concerns regarding ionizing radiation exposure, particularly among vulnerable populations requiring frequent imaging. Achieving a balance between high-quality diagnostic imaging and minimizing radiation exposure remains a fundamental challenge in radiology. Artificial intelligence (AI) has emerged as a transformative solution, enabling low-dose imaging protocols that enhance image quality while significantly reducing radiation doses. This review explores the role of AI-assisted low-dose imaging, particularly in CT, X-ray, and magnetic resonance imaging (MRI), highlighting advancements in deep learning models, convolutional neural networks (CNNs), and other AI-based approaches. These technologies have demonstrated substantial improvements in noise reduction, artifact removal, and real-time optimization of imaging parameters, thereby enhancing diagnostic accuracy while mitigating radiation risks. Additionally, AI has contributed to improved radiology workflow efficiency and cost reduction by minimizing the need for repeat scans. The review also discusses emerging directions in AI-driven medical imaging, including hybrid AI systems that integrate post-processing with real-time data acquisition, personalized imaging protocols tailored to patient characteristics, and the expansion of AI applications to fluoroscopy and positron emission tomography (PET). However, challenges such as model generalizability, regulatory constraints, ethical considerations, and computational requirements must be addressed to facilitate broader clinical adoption. AI-driven low-dose imaging has the potential to revolutionize radiology by enhancing patient safety, optimizing imaging quality, and improving healthcare efficiency, paving the way for a more advanced and sustainable future in medical imaging.

AI-augmented Biophysical modeling in thermoplasmonics for real-time monitoring and diagnosis of human tissue infections

Identifying tissue infections from the body still poses an unprecedented challenge in society. Conventional diagnostic procedures are time-consuming and lack a real-time monitoring mode. This study proposes a system with an Artificial Intelligence (AI)-assisted Thermoplasmonics scheme that has a 57.7% shorter detection time than traditional techniques. The proposed system combines AI with Localised Surface Plasmon Resonance (LSPR) technology. Employing 2,333,481 single-cell transcriptomic profiles from 486 people (107 non-affected, 379 affected), an effective circuitry deep learning setup was designed and validated to analyse Thermoplasmonics sensor data in real-time. The system achieved an overall accuracy of 92.3% It achieved a 42.3% reduction in false positives and a 35.6% decrease in cost per healthcare diagnosis. It also achieved a classification accuracy of 1-94.5%, significantly higher than traditional culture methods' accuracies. The mean detection time was brought down to 42.3 min (SD = 12.8), and 99.7% of the time, all the analyses were done in less than 1 s. Clinical implementation in three major medical centres (n = 1655 cases) demonstrated significant improvements: a 31.3% decrease in the proportion of antibiotic cases misuse and a 23% decrease in hospital stays. Cost-benefit studies showed the system's feasibility in saving $2.8 million per hospital annually.

Feasibility study of 'Triple-Low' technique for coronary artery computed tomography angiography (CCTA)

This study aims to explore the feasibility of applying the "Three-Low" technique (low injection rate, low iodine contrast volume, low radiation dose) in coronary CT angiography (CCTA). We prospectively collected data from 90 patients who underwent CCTA at our hospital between 2021 and 2024. The patients were randomly assigned to either the experimental group (n = 45) or the control group (n = 45). The experimental group parameters were as follows: injection rate of 3.5-4.0 ml/s, iodine contrast volume of 35-40 ml, tube voltage of 100 kVp, and tube current of 250 mA. The control group parameters were: injection rate of 4.5-5.0 ml/s, iodine contrast volume of 45-50 ml, tube voltage of 120 kVp, and tube current of 450 mA. Both groups received a high-concentration, non-ionic, water-soluble contrast agent (Iomeprol, 40 gl/100 ml). The heart rate of all patients was ≤ 70 bpm, and breath-hold scanning was performed after breathing training. The study compared the CT values of the left anterior descending artery, left circumflex artery, right coronary artery, and aorta, as well as background noise, signal-to-noise ratio (SNR), volumetric CT dose index, dose-length product, effective radiation dose, and total iodine dose between the two groups. In the control group, no cases of contrast extravasation occurred, while 6 cases of extravasation were observed in the experimental group (p = 0.026). There was no significant difference between the groups in terms of vascular image quality (mean vascular image quality score: experimental group 4.27 ± 0.62 vs. control group 4.24 ± 0.57, p > 0.05) or vascular motion artifact score (mean vascular motion artifact score: experimental group 4.20 ± 0.59 vs. control group 4.13 ± 0.55, p > 0.05). However, significant differences were found between the experimental and control groups in the CT values of the left anterior descending artery (experimental group: 571.31 ± 49.66 HU vs. control group: 449.20 ± 36.80 HU, p < 0.05), left circumflex artery (experimental group: 570.41 ± 49.98 HU vs. control group: 450.95 ± 39.27 HU, p < 0.05), right coronary artery (experimental group: 584.52 ± 53.70 HU vs. control group: 452.66 ± 40.67 HU, p < 0.05), aorta (experimental group: 624.91 ± 48.99 HU vs. control group: 465.36 ± 34.37 HU, p < 0.05), background noise (experimental group: 24.76 ± 1.97 vs. control group: 19.09 ± 1.69, p < 0.05), SNR (experimental group: 25.30 ± 1.81 vs. control group: 24.47 ± 1.75, p < 0.05), volumetric CT dose index (experimental group: 22.97 ± 1.47 mGy vs. control group: 50.53 ± 4.89 mGy, p < 0.05), dose-length product (experimental group: 363.68 ± 21.45 mGy·cm vs. control group: 782.41 ± 58.20 mGy·cm, p < 0.05), and effective radiation dose (experimental group: 5.09 ± 0.30 mSv vs. control group: 10.95 ± 0.81 mSv, p < 0.05).The results of the Fisher test indicated that the extravasation rate was significantly higher in the high injection rate group compared to the low injection rate group (P = 0.024). The "Three-Low" technique in CCTA imaging effectively reduces the incidence of contrast extravasation caused by high injection rates and decreases the radiation dose, making it a highly feasible option for clinical application and worthy of broader adoption.

Artificial Intelligence-Driven Advances in Coronary Calcium Scoring: Expanding Preventive Cardiology

Coronary artery disease (CAD) remains a leading global cause of morbidity and mortality, underscoring the need for effective cardiovascular risk stratification and preventive strategies. Coronary artery calcium (CAC) scoring, traditionally performed using electrocardiogram (ECG)-gated cardiac computed tomography (CT) scans, has been widely validated as a robust tool for assessing cardiovascular risk. However, its application has been largely limited to high-risk populations due to the costs, technical requirements, and limited accessibility of cardiac CT scans. Recent advancements in artificial intelligence (AI) have introduced transformative opportunities to extend CAC detection to noncardiac CT scans, such as those performed for lung cancer screening, enabling broader and more accessible cardiovascular screening. This review provides a comprehensive analysis of AI-driven CAC detection, examining various types of AI models for CAC detection, like convolutional neural networks (CNNs) and U-Net architectures, and exploring the clinical, operational, and ethical implications of incorporating these technologies into routine practice. Technical challenges, including imaging variability, data privacy, and model bias, are discussed alongside essential areas for further research, such as standardization and validation across diverse populations. By leveraging widely available imaging data, AI-enabled CAC detection has the potential to advance preventive cardiology, supporting earlier risk identification, optimizing healthcare resources, and improving patient outcomes.

Radiomics parameters of epicardial adipose tissue predict mortality in acute pulmonary embolism

BACKGROUND

Accurate prediction of short-term mortality in acute pulmonary embolism (APE) is very important. The aim of the present study was to analyze the prognostic role of radiomics values of epicardial adipose tissue (EAT) in APE.

METHODS

Overall, 508 patients were included into the study, 209 female (42.1%), mean age, 64.7 ± 14.8 years. 4.6%and 12.4% died (7- and 30-day mortality, respectively). For external validation, a cohort of 186 patients was further analysed. 20.2% and 27.7% died (7- and 30-day mortality, respectively). CTPA was performed at admission for every patient before any previous treatment on multi-slice CT scanners. A trained radiologist, blinded to patient outcomes, semiautomatically segmented the EAT on a dedicated workstation using ImageJ software. Extraction of radiomic features was applied using the pyradiomics library. After correction for correlation among features and feature cleansing by random forest and feature ranking, we implemented feature signatures using 247 features of each patient. In total, 26 feature combinations with different feature class combinations were identified. Patients were randomly assigned to a training and a validation cohort with a ratio of 7:3. We characterized two models (30-day and 7-day mortality). The models incorporate a combination of 13 features of seven different image feature classes.

FINDINGS

We fitted the characterized models to a validation cohort (n = 169) in order to test accuracy of our models. We observed an AUC of 0.776 (CI 0.671-0.881) and an AUC of 0.724 (CI 0.628-0.820) for the prediction of 30-day mortality and 7-day mortality, respectively. The overall percentage of correct prediction in this regard was 88% and 79% in the validation cohorts. Lastly, the AUC in an independent external validation cohort was 0.721 (CI 0.633-0.808) and 0.750 (CI 0.657-0.842), respectively.

INTERPRETATION

Radiomics parameters of EAT are strongly associated with mortality in patients with APE.

CLINICAL TRIAL NUMBER

Not applicable.

Noninvasive Atherosclerotic Phenotyping: The Next Frontier into Understanding the Pathobiology of Coronary Artery Disease

PURPOSE OF REVIEW

Despite recent advances, coronary artery disease remains one of the leading causes of mortality worldwide. Noninvasive imaging allows atherosclerotic phenotyping by measurement of plaque burden, morphology, activity and inflammation, which has the potential to refine patient risk stratification and guide personalized therapy. This review describes the current and emerging roles of advanced noninvasive cardiovascular imaging methods for the assessment of coronary artery disease.

RECENT FINDINGS

Cardiac computed tomography enables comprehensive, noninvasive imaging of the coronary vasculature, and is used to assess luminal stenoses, coronary calcifications, and distinct adverse plaque characteristics, helping to identify patients prone to future events. Novel software tools, implementing artificial intelligence solutions, can automatically quantify and characterize atherosclerotic plaque from standard computed tomography datasets. These quantitative imaging biomarkers have been shown to improve patient risk stratification beyond clinical risk scores and current clinical interpretation of cardiac computed tomography. In addition, noninvasive molecular imaging in higher risk patients can be used to assess plaque activity and plaque thrombosis. Noninvasive imaging allows unique insight into the burden, morphology and activity of atherosclerotic coronary plaques. Such phenotyping of atherosclerosis can potentially improve individual patient risk prediction, and in the near future has the potential for clinical implementation.

The Society of Thoracic Surgeons Adult Cardiac Surgery Database: Intraoperative Echocardiography Reporting

OBJECTIVES

To identify trends in the reporting of intraoperative transesophageal echocardiographic (TEE) data in the Society of Thoracic Surgeons (STS) Adult Cardiac Surgery Database (ACSD) and the Adult Cardiac Anesthesiology (ACA) module by period, practice type, and geographic distribution, and to elucidate ongoing areas for practice improvement.

DESIGN

A retrospective study.

SETTING

STS ACSD.

PARTICIPANTS

Procedures reported in the STS ACSD between July 2017 and December 2021 in participating programs in the United States.

INTERVENTIONS

None MEASUREMENTS AND MAIN RESULTS: Intraoperative TEE is reported for 73% of all procedures in ACSD. Although the intraoperative TEE data reporting rate increased from 2017 to 2021 for isolated coronary artery bypass graft surgery, it remained low at 62.2%. The reporting of relevant echocardiographic variables across a wide range of procedures has steadily increased over the study period but also remained low. The reporting in the ACA module is high for most variables and across all anesthesia care models; however, the overall contribution of the ACA module to the ACSD remains low.

CONCLUSIONS

This progress report suggests a continued need to raise awareness regarding current practices of reporting intraoperative TEE in the ACSD and the ACA, and highlights opportunities for improving reporting and data abstraction.

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.

Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review

Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.

Predicting the effectiveness of drugs used for treating cardiovascular conditions in newborn infants

BACKGROUND

Cardiovascular support (CVS) treatment failure (TF) is associated with a poor prognosis in preterm infants.

METHODS

Medical charts of infants with a birth weight <1500 g who received either dopamine (Dp) or dobutamine (Db), were reviewed. Treatment response (TR) occurred if blood pressure increased >3rd centile for gestational age or superior vena cava flow was maintained >55 ml/kg/min, with decreased lactate or less negative base excess, without additional CVS. A predictive model of Dp and Db on TR was designed and the impact of TR on survival was analyzed.

RESULTS

Sixty-six infants (median gestational age 27.3 weeks, median birth weight 864 g) received Dp (n = 44) or Db (n = 22). TR occurred in 59% of the cases treated with Dp and 31% with Db, p = 0.04. Machine learning identified a model that correctly labeled Db response in 90% of the cases and Dp response in 61.4%. Sixteen infants died (9% of the TR group, 39% of the TF group; p = 0.004). Brain or gut morbidity-free survival was observed in 52% vs 30% in the TR and TF groups, respectively (p = 0.08).

CONCLUSIONS

New predictive models can anticipate Db but not Dp effectiveness in preterm infants. These algorithms may help the clinicians in the decision-making process.

IMPACT

Failure of cardiovascular support treatment increases the risk of mortality in very low birth weight infants. A predictive model built with machine learning techniques can help anticipate treatment response to dobutamine with high accuracy. Predictive models based on artificial intelligence may guide the clinicians in the decision-making process.

Predicting coronary plaque progression with conventional plaque parameters and radiomics features derived from coronary CT angiography

OBJECTIVES

To determine the value of combining conventional plaque parameters and radiomics features derived from coronary computed tomography angiography (CCTA) for predicting coronary plaque progression.

MATERIALS AND METHODS

Clinical data and CCTA images of 400 patients who underwent at least two CCTA examinations between January 2009 and August 2020 were analyzed retrospectively. Diameter stenosis, total plaque volume and burden, calcified plaque volume and burden, noncalcified plaque volume and burden (NCPB), pericoronary fat attenuation index (FAI), and other conventional plaque parameters were recorded. The patients were assigned to a training cohort (n = 280) and a validation cohort (n = 120) in a 7:3 ratio using a stratified random splitting method. The area under the receiver operating characteristics curve (AUC) was used to evaluate the predictive abilities of conventional parameters (model 1), radiomics features (model 2), and their combination (model 3).

RESULTS

FAI and NCPB were identified as independent risk factors for coronary plaque progression in the training cohort. Both model 2 (training cohort AUC: 0.814, p < 0.001; validation cohort AUC: 0.729, p = 0.288) and model 3 (training cohort AUC: 0.824, p < 0.001; validation cohort AUC: 0.758, p = 0.042) had better diagnostic performances in predicting plaque progression than model 1 (training cohort AUC: 0.646; validation cohort AUC: 0.654). Moreover, model 3 was slightly higher than model 2, although not statistically significant.

CONCLUSIONS

The combination of conventional coronary plaque parameters and CCTA-derived radiomics features had a better ability to predict plaque progression than conventional parameters alone.

CLINICAL RELEVANCE STATEMENT

The conventional coronary plaque characteristics such as noncalcified plaque burden, pericoronary fat attenuation index, and radiomics features derived from CCTA can identify plaques prone to progression, which is helpful for further clinical decision-making of coronary artery disease.

KEY POINTS

• FAI and NCPB were identified as independent risk factors for predicting plaque progression. • Coronary plaque radiomics features were more advantageous than conventional parameters in predicting plaque progression. • The combination of conventional coronary plaque parameters and radiomics features could significantly improve the predictive ability of plaque progression over conventional parameters alone.

Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve

Advancements in artificial intelligence (AI) have rapidly transformed various sectors, and the field of echocardiography is no exception. AI-driven technologies hold immense potential to revolutionize echo labs' diagnostic capabilities and improve patient care. This paper explores the importance for echo labs to embrace AI and stay ahead of the curve in harnessing its power. Our manuscript provides an overview of the growing impact of AI on medical imaging, specifically echocardiography. It highlights how AI-driven algorithms can enhance image quality, automate measurements, and accurately diagnose cardiovascular diseases. Additionally, we emphasize the importance of training echo lab professionals in AI implementation to optimize its integration into routine clinical practice. By embracing AI, echo labs can overcome challenges such as workload burden and diagnostic accuracy variability, improving efficiency and patient outcomes. This paper highlights the need for collaboration between echocardiography laboratory experts, AI researchers, and industry stakeholders to drive innovation and establish standardized protocols for implementing AI in echocardiography. In conclusion, this article emphasizes the importance of AI adoption in echocardiography labs, urging practitioners to proactively integrate AI technologies into their workflow and take advantage of their present opportunities. Embracing AI is not just a choice but an imperative for echo labs to maintain their leadership and excel in delivering state-of-the-art cardiac care in the era of advanced medical technologies.

Challenges and Burdens in the Coronary Artery Disease Care Pathway for Patients Undergoing Percutaneous Coronary Intervention: A Contemporary Narrative Review

Clinical and economic burdens exist within the coronary artery disease (CAD) care pathway despite advances in diagnosis and treatment and the increasing utilization of percutaneous coronary intervention (PCI). However, research presenting a comprehensive assessment of the challenges across this pathway is scarce. This contemporary review identifies relevant studies related to inefficiencies in the diagnosis, treatment, and management of CAD, including clinician, patient, and economic burdens. Studies demonstrating the benefits of integration and automation within the catheterization laboratory and across the CAD care pathway were also included. Most studies were published in the last 5-10 years and focused on North America and Europe. The review demonstrated multiple potentially avoidable inefficiencies, with a focus on access, appropriate use, conduct, and follow-up related to PCI. Inefficiencies included misdiagnosis, delays in emergency care, suboptimal testing, longer procedure times, risk of recurrent cardiac events, incomplete treatment, and challenges accessing and adhering to post-acute care. Across the CAD pathway, this review revealed that high clinician burnout, complex technologies, radiation, and contrast media exposure, amongst others, negatively impact workflow and patient care. Potential solutions include greater integration and interoperability between technologies and systems, improved standardization, and increased automation to reduce burdens in CAD and improve patient outcomes.

Radiomic Phenotype of Periatrial Adipose Tissue in the Prognosis of Late Postablation Recurrence of Idiopathic Atrial Fibrillation

The aim of the study is to find new predictors of postablation atrial fibrillation (AF) recurrence in patients with lone AF using a texture analysis of the periatrial adipose tissue (PAAT) of the left atrium.

Materials and Methods

Forty-three patients admitted for lone AF catheter ablation, who had undergone multispiral coronary angiography, were enrolled in the study. PAAT segmentation was performed using 3D Slicer application followed by extraction of 93 radiomic features. At the end of the follow-up period, patients were divided into 2 groups depending on the presence or absence of AF recurrence.

Results

12 months of follow-up after catheter ablation, postablation AF recurrence was reported in 19 out of 43 patients. Of 93 extracted radiomic features of PAAT, statistically significant differences were observed for 3 features of the Gray Level Size Zone matrix. At the same time, only one radiomic feature of PAAT, Size Zone Non Uniformity Normalized, was an independent predictor of postablative recurrence of AF after catheter ablation and 12 months of follow-up (McFadden's R²=0.451, OR - 0.506, 95% CI: 0.331‒0.776, p<0.001).

Conclusion

The radiomic analysis of periatrial adipose tissue may be considered as a promising non-invasive method for predicting adverse outcomes of the catheter treatment, which opens the possibilities for planning and correction of patient management tactics after intervention.

Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging

Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.

Radiomic phenotype of epicardial adipose tissue in the prognosis of atrial fibrillation recurrence after catheter ablation in patients with lone atrial fibrillation

Background

Epicardial adipose tissue (EAT) has been considered as one of the probable triggers of atrial fibrillation (AF). CT-rediomics is a perspective noninvasive method of assessment of EAT. We evaluate the radiomic phenotype of EAT in patients with lone AF in the prognosis of AF recurrence after catheter ablation.

Methods

A total of 43 patients with lone AF referred for CA and 20 out-hospital patients without arrhythmia underwent multidetector computed tomography coronary angiography. Segmentation of EAT and extraction radiomic features were performed on calcium scoring series using by 3D-Slicer. Clinical follow-up was performed for 12 months period after the CA.

Results

EAT in patients with lone AF had a distinct radiomic phenotype. Thus, 45 of 93 calculated radiomic features, volume and attenuation of EAT were significantly different between patients with lone AF and persons without any arrhythmia. In addition, 17 radiomic features were significantly different in subgroups with and without AF recurrence. Multivariate regression analysis demonstrated that only gray level nonuniformity normalized (GLSZM) was an independent predictor of AF recurrence (OR 1.0027, 95%CI 1.0009-1.0044, p = 0.002). ROC analysis data showed that GLSZM >1227.4 indicates high probability of AF recurrence during 12 months (sensitivity 89.4%, specificity 70.8%, AUC: 0.809; p = 0.001).

Conclusion

The radiomic parameter GLSZM is associated with late AF recurrence after CA in patients with lone AF. In current study GLSZM was a stronger predictor of lone AF recurrence in multivariate analysis comparing with other established risk factors and EAT volume and attenuation.

Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools

Cardiovascular disease remains the leading cause of death in women. Given accumulating evidence on sex- and gender-based differences in cardiovascular disease development and outcomes, the need for more effective approaches to screening for risk factors and phenotypes in women is ever urgent. Public health surveillance and health care delivery systems now continuously generate massive amounts of data that could be leveraged to enable both screening of cardiovascular risk and implementation of tailored preventive interventions across a woman's life span. However, health care providers, clinical guidelines committees, and health policy experts are not yet sufficiently equipped to optimize the collection of data on women, use or interpret these data, or develop approaches to targeting interventions. Therefore, we provide a broad overview of the key opportunities for cardiovascular screening in women while highlighting the potential applications of artificial intelligence along with digital technologies and tools.

Artificial intelligence in cardiovascular CT: Current status and future implications

Artificial intelligence (AI) refers to the use of computational techniques to mimic human thought processes and learning capacity. The past decade has seen a rapid proliferation of AI developments for cardiovascular computed tomography (CT). These algorithms aim to increase efficiency, objectivity, and performance in clinical tasks such as image quality improvement, structure segmentation, quantitative measurements, and outcome prediction. By doing so, AI has the potential to streamline clinical workflow, increase interpretative speed and accuracy, and inform subsequent clinical pathways. This review covers state-of-the-art AI techniques in cardiovascular CT and the future role of AI as a clinical support tool.

Estimation of Nuclear Medicine Exposure Measures Based on Intelligent Computer Processing

This paper provides an in-depth discussion and analysis of the estimation of nuclear medicine exposure measurements using computerized intelligent processing. The focus is on the study of energy extraction algorithms to obtain a high energy resolution with the lowest possible ADC sampling rate and thus reduce the amount of data. This paper focuses on the direct pulse peak extraction algorithm, polynomial curve fitting algorithm, double exponential function curve fitting algorithm, and pulse area calculation algorithm. The detector output waveforms are obtained with an oscilloscope, and the analysis module is designed in MATLAB. Based on these algorithms, the data obtained from six different lower sampling rates are analyzed and compared with the results of the high sampling rate direct pulse peak extraction algorithm and the pulse area calculation algorithm, respectively. The correctness of the compartment model was checked, and the results were found to be realistic and reliable, which can be used for the analysis of internal exposure data in radiation occupational health management, estimation of internal exposure dose for nuclear emergency groups, and estimation of accidental internal exposure dose. The results of the compartment model of the respiratory tract and the compartment model of the digestive tract can be used to calculate the distribution and retention patterns of radionuclides and their compounds in the body, which can be used to assess the damage of radionuclide internal contamination and guide the implementation of medical treatment.

Cardiac Computed Tomography Radiomics for the Non-Invasive Assessment of Coronary Inflammation

Radiomics, via the extraction of quantitative information from conventional radiologic images, can identify imperceptible imaging biomarkers that can advance the characterization of coronary plaques and the surrounding adipose tissue. Such an approach can unravel the underlying pathophysiology of atherosclerosis which has the potential to aid diagnostic, prognostic and, therapeutic decision making. Several studies have demonstrated that radiomic analysis can characterize coronary atherosclerotic plaques with a level of accuracy comparable, if not superior, to current conventional qualitative and quantitative image analysis. While there are many milestones still to be reached before radiomics can be integrated into current clinical practice, such techniques hold great promise for improving the imaging phenotyping of coronary artery disease.

Artificial Intelligence in Cardiovascular Imaging for Risk Stratification in Coronary Artery Disease

Artificial intelligence (AI) describes the use of computational techniques to perform tasks that normally require human cognition. Machine learning and deep learning are subfields of AI that are increasingly being applied to cardiovascular imaging for risk stratification. Deep learning algorithms can accurately quantify prognostic biomarkers from image data. Additionally, conventional or AI-based imaging parameters can be combined with clinical data using machine learning models for individualized risk prediction. The aim of this review is to provide a comprehensive review of state-of-the-art AI applications across various noninvasive imaging modalities (coronary artery calcium scoring CT, coronary CT angiography, and nuclear myocardial perfusion imaging) for the quantification of cardiovascular risk in coronary artery disease. © RSNA, 2021.