Automated Echocardiographic Detection of Severe Coronary Artery Disease Using Artificial Intelligence

Revolutionizing Acute Cardiac Care with Artificial Intelligence: Opportunities and Challenges

This manuscript reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of the global burden of cardiovascular diseases. It explores how AI algorithms can rapidly and accurately process data for the prediction and diagnosis of acute cardiac conditions. The paper examines AI's impact on patient health across various diagnostic tools such as echocardiography, electrocardiography, coronary angiography, cardiac CT, and MRI and discusses the regulatory landscape for AI in healthcare, categorizes AI algorithms by their risk levels. Furthermore, it addresses the challenges of data quality, generalizability, bias, transparency, and regulatory considerations, underscoring the necessity for inclusive data and robust validation processes. The review concludes with future perspectives on integrating AI into clinical workflows and the ongoing need for research, regulation, and innovation to harness AI's full potential in improving acute cardiac care.

Interpretation of SPECT wall motion with deep learning

OBJECTIVES

We sought to develop a novel deep learning (DL) workflow to interpret single-photon emission computed tomography (SPECT) wall motion.

BACKGROUND

Wall motion assessment with SPECT is limited by image temporal and spatial resolution. Visual interpretation of wall motion can be subjective and prone to error. Artificial intelligence (AI) may improve accuracy of wall motion assessment.

METHODS

A total of 1038 patients undergoing rest electrocardiogram (ECG)-gated SPECT and echocardiography were included. Using echocardiography as truth, a DL-model (DL-model 1) was trained to predict the probability of abnormal wall motion. Of the 1038 patients, 317 were used to train a DL-model (DL-model 2) to assess regional wall motion. A 10-fold cross-validation was adopted. Diagnostic performance of DL was compared with human readers and quantitative parameters.

RESULTS

The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) of DL model (AUC: .82 [95% CI: .79-.85]; ACC: .88) were higher than human (AUC: .77 [95% CI: .73-.81]; ACC: .82; P < .001) and quantitative parameter (AUC: .74 [95% CI: .66-.81]; ACC: .78; P < .05). The net reclassification index (NRI) was 7.7%. The AUC and accuracy of DL model for per-segment and per-vessel territory diagnosis were also higher than human reader. The DL model generated results within 30 seconds with operable guided user interface (GUI) and therefore could provide preliminary interpretation.

CONCLUSIONS

DL can be used to improve interpretation of rest SPECT wall motion as compared with current human readers and quantitative parameter diagnosis.

Stenosis Detection and Quantification of Coronary Artery Using Machine Learning and Deep Learning

The aim of this review is to introduce some applications of artificial intelligence (AI) algorithms for the detection and quantification of coronary stenosis using computed tomography angiography (CTA). The realization of automatic/semi-automatic stenosis detection and quantification includes the following steps: vessel central axis extraction, vessel segmentation, stenosis detection, and quantification. Many new AI techniques, such as machine learning and deep learning, have been widely used in medical image segmentation and stenosis detection. This review also summarizes the recent progress regarding coronary stenosis detection and quantification, and discusses the development trends in this field. Through evaluation and comparison, researchers can better understand the research frontier in related fields, compare the advantages and disadvantages of various methods, and better optimize the new technologies. Machine learning and deep learning will promote the process of automatic detection and quantification of coronary artery stenosis. However, the machine learning and the deep learning methods need a large amount of data, so they also face some challenges because of the lack of professional image annotations (manually add labels by experts).

Left Ventricular Segmentation, Warping, and Myocardial Registration for Automated Strain Measurement

The left ventricular global longitudinal strain (LVGLS) is a crucial prognostic indicator. However, inconsistencies in measurements due to the speckle tracking algorithm and manual adjustments have hindered its standardization and democratization. To solve this issue, we proposed a fully automated strain measurement by artificial intelligence-assisted LV segmentation contours. The LV segmentation model was trained from echocardiograms of 368 adults (11,125 frames). We compared the registration-like effects of dynamic time warping (DTW) with speckle tracking on a synthetic echocardiographic dataset in experiment-1. In experiment-2, we enrolled 80 patients to compare the DTW method with commercially available software. In experiment-3, we combined the segmentation model and DTW method to create the artificial intelligence (AI)-DTW method, which was then tested on 40 patients with general LV morphology, 20 with dilated cardiomyopathy (DCMP), and 20 with transthyretin-associated cardiac amyloidosis (ATTR-CA), 20 with severe aortic stenosis (AS), and 20 with severe mitral regurgitation (MR). Experiments-1 and -2 revealed that the DTW method is consistent with dedicated software. In experiment-3, the AI-DTW strain method showed comparable results for general LV morphology (bias - 0.137 ± 0.398%), DCMP (- 0.397 ± 0.607%), ATTR-CA (0.095 ± 0.581%), AS (0.334 ± 0.358%), and MR (0.237 ± 0.490%). Moreover, the strain curves showed a high correlation in their characteristics, with R-squared values of 0.8879-0.9452 for those LV morphology in experiment-3. Measuring LVGLS through dynamic warping of segmentation contour is a feasible method compared to traditional tracking techniques. This approach has the potential to decrease the need for manual demarcation and make LVGLS measurements more efficient and user-friendly for daily practice.

The Year in Perioperative Echocardiography: Selected Highlights from 2023

This article is the eighth in an annual series reviewing the research highlights of the year pertaining to the subspecialty of perioperative echocardiography for the Journal of Cardiothoracic and Vascular Anesthesia. The authors thank the editor-in-chief, Dr Kaplan, and the editorial board for the opportunity to continue this series. In most cases, these will be research articles targeted at the perioperative echocardiographic diagnosis and treatment of patients after cardiothoracic surgery; but in some cases, the articles will target the use of perioperative echocardiography in general.

Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review

BACKGROUND

Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI.

OBJECTIVE

This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals.

METHODS

We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles.

RESULTS

Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care.

CONCLUSIONS

GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.

Unlocking the potential of artificial intelligence in sports cardiology: does it have a role in evaluating athlete's heart?

The integration of artificial intelligence (AI) technologies is evolving in different fields of cardiology and in particular in sports cardiology. Artificial intelligence offers significant opportunities to enhance risk assessment, diagnosis, treatment planning, and monitoring of athletes. This article explores the application of AI in various aspects of sports cardiology, including imaging techniques, genetic testing, and wearable devices. The use of machine learning and deep neural networks enables improved analysis and interpretation of complex datasets. However, ethical and legal dilemmas must be addressed, including informed consent, algorithmic fairness, data privacy, and intellectual property issues. The integration of AI technologies should complement the expertise of physicians, allowing for a balanced approach that optimizes patient care and outcomes. Ongoing research and collaborations are vital to harness the full potential of AI in sports cardiology and advance our management of cardiovascular health in athletes.

Artificial intelligence-assisted diagnosis of congenital heart disease and associated pulmonary arterial hypertension from chest radiographs: A multi-reader multi-case study

OBJECTIVES

To explore the possibility of automatic diagnosis of congenital heart disease (CHD) and pulmonary arterial hypertension associated with CHD (PAH-CHD) from chest radiographs using artificial intelligence (AI) technology and to evaluate whether AI assistance could improve clinical diagnostic accuracy.

MATERIALS AND METHODS

A total of 3255 frontal preoperative chest radiographs (1174 CHD of any type and 2081 non-CHD) were retrospectively obtained. In this study, we adopted ResNet18 pretrained with the ImageNet database to establish diagnostic models. Radiologists diagnosed CHD/PAH-CHD from 330/165 chest radiographs twice: the first time, 50% of the images were accompanied by AI-based classification; after a month, the remaining 50% were accompanied by AI-based classification. Diagnostic results were compared between the radiologists and AI models, and between radiologists with and without AI assistance.

RESULTS

The AI model achieved an average area under the receiver operating characteristic curve (AUC) of 0.948 (sensitivity: 0.970, specificity: 0.982) for CHD diagnoses and an AUC of 0.778 (sensitivity: 0.632, specificity: 0.925) for identifying PAH-CHD. In the 330 balanced (165 CHD and 165 non-CHD) testing set, AI achieved higher AUCs than all 5 radiologists in the identification of CHD (0.670-0.858) and PAH-CHD (0.610-0.688). With AI assistance, the mean ± standard error AUC of radiologists was significantly improved for CHD (ΔAUC + 0.096, 95 % CI: 0.001-0.190; P = 0.048) and PAH-CHD (ΔAUC + 0.066, 95 % CI: 0.010-0.122; P = 0.031) diagnosis.

CONCLUSION

Chest radiograph-based AI models can detect CHD and PAH-CHD automatically. AI assistance improved radiologists' diagnostic accuracy, which may facilitate a timely initial diagnosis of CHD and PAH-CHD.

Exercise Stress Echocardiography in Athletes: Applications, Methodology, and Challenges

This comprehensive review explores the role of exercise stress echocardiography (ESE) in assessing cardiovascular health in athletes. Athletes often exhibit cardiovascular adaptations because of rigorous physical training, making the differentiation between physiological changes and potential pathological conditions challenging. ESE is a crucial diagnostic tool, offering detailed insights into an athlete's cardiac function, reserve, and possible arrhythmias. This review highlights the methodology of ESE, emphasizing its significance in detecting exercise-induced anomalies and its application in distinguishing between athlete's heart and other cardiovascular diseases. Recent advancements, such as LV global longitudinal strain (GLS) and myocardial work (MW), are introduced as innovative tools for the early detection of latent cardiac dysfunctions. However, the use of ESE also subsumes limitations and possible pitfalls, particularly in interpretation and potential false results, as explained in this article.

Artificial intelligence calculated global longitudinal strain and left ventricular ejection fraction predicts cardiac events and all-cause mortality in patients with chest pain

BACKGROUND

Assessment of left ventricular ejection fraction (LVEF) and myocardial deformation with global longitudinal strain (GLS) has shown promise in predicting adverse cardiovascular events. The aim of this study was to evaluate whether artificial intelligence (AI) calculated LVEF and GLS is associated with major adverse cardiac events (MACE) and all-cause mortality in patients presenting with chest pain.

METHODS

We studied 296 patients presenting with chest pain, who underwent transthoracic echocardiography (TTE). Clinical data, downstream clinical investigations and patient outcomes were collected. Resting TTE images underwent AI contouring for automated calculation of LVEF and GLS with Ultromics EchoGo Core 2.0. Regression analysis was performed to identify clinical and AI calculated parameters associated with MACE and all-cause mortality.

RESULTS

During a median follow-up period of 7.8 years (IQR 6.4, 8.8), MACE occurred in 34 (11.5%) patients and all-cause mortality in 60 (20%) patients. AI calculated LVEF (Odds Ratio [OR] .96; 95% CI .93-.99 and .96; 95% CI .93-.99) and GLS (1.11; 95% CI 1.01-1.21 and 1.08; 95% CI 1.00-1.16) were independently associated with MACE and all-cause mortality, respectively. According to Cox proportional hazards, a LVEF < 50% was associated with a 3.7 times MACE and 2.8 times all-cause mortality hazard rate compared to those with a LVEF ≥ 50%. Those with a GLS ≥ 15% was associated with a 2.5 times MACE and 2.3 times all-cause mortality hazard rate compared to those with a GLS ≤ 15.

CONCLUSION

AI calculated resting LVEF and GLS is independently associated with MACE and all-cause mortality in high CVD risk patients. These results may have significant clinical implications through improved risk stratification of patients with chest pain, accelerated workflow of labour-intensive technical measures, and reduced healthcare costs.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

Clinical service evaluation of the feasibility and reproducibility of novel artificial intelligence based-echocardiographic quantification of global longitudinal strain and left ventricular ejection fraction in trastuzumab-treated patients

Introduction

Cardiotoxicity is a potential prognostically important complication of certain chemotherapeutic agents that may result in preclinical or overt clinical heart failure. In some cases, chemotherapy must be withheld when left ventricular (LV) systolic function becomes significantly impaired, to protect cardiac function at the expense of a change in the oncological treatment plan, leading to associated changes in oncological prognosis. Accordingly, patients receiving potentially cardiotoxic chemotherapy undergo routine surveillance before, during and following completion of therapy, usually with transthoracic echocardiography (TTE). Recent advancements in AI-based cardiac imaging reveal areas of promise but key challenges remain. There are ongoing questions as to whether the ability of AI to detect subtle changes in individual patients is at a level equivalent to manual analysis. This raises the question as to whether AI-based left ventricular strain analysis could provide a potential solution to left ventricular systolic function analysis in a manner equivocal to or superior to conventional assessment, in a real-world clinical service. AI based automated analyses may represent a potential solution for addressing the pressure of increasing echocardiographic demands within limited service-capacity healthcare systems, in addition to facilitating more accurate diagnoses.

Methods

This clinical service evaluation aims to establish whether AI-automated analysis compared to conventional methods (1) is a feasible method for assessing LV-GLS and LVEF, (2) yields moderate to good correlation between the two approaches, and (3) would lead to different clinical recommendations with serial surveillance in a real-world clinical population.

Results and Discussion

We observed a moderate correlation (r = 0.541) in GLS between AI automated assessment compared to conventional methods. The LVEF quantification between methods demonstrated a strong correlation (r = 0.895). AI-generated GLS and LVEF values compared reasonably well with conventional methods, demonstrating a similar temporal pattern throughout echocardiographic surveillance. The apical-three chamber view demonstrated the lowest correlation (r = 0.423) and revealed to be least successful for acquisition of GLS and LVEF. Compared to conventional methodology, AI-automated analysis has a significantly lower feasibility rate, demonstrating a success rate of 14% (GLS) and 51% (LVEF).

Deep Learning for Cardiovascular Imaging: A Review

Importance

Artificial intelligence (AI), driven by advances in deep learning (DL), has the potential to reshape the field of cardiovascular imaging (CVI). While DL for CVI is still in its infancy, research is accelerating to aid in the acquisition, processing, and/or interpretation of CVI across various modalities, with several commercial products already in clinical use. It is imperative that cardiovascular imagers are familiar with DL systems, including a basic understanding of how they work, their relative strengths compared with other automated systems, and possible pitfalls in their implementation. The goal of this article is to review the methodology and application of DL to CVI in a simple, digestible fashion toward demystifying this emerging technology.

Observations

At its core, DL is simply the application of a series of tunable mathematical operations that translate input data into a desired output. Based on artificial neural networks that are inspired by the human nervous system, there are several types of DL architectures suited to different tasks; convolutional neural networks are particularly adept at extracting valuable information from CVI data. We survey some of the notable applications of DL to tasks across the spectrum of CVI modalities. We also discuss challenges in the development and implementation of DL systems, including avoiding overfitting, preventing systematic bias, improving explainability, and fostering a human-machine partnership. Finally, we conclude with a vision of the future of DL for CVI.

Conclusions and Relevance

Deep learning has the potential to meaningfully affect the field of CVI. Rather than a threat, DL could be seen as a partner to cardiovascular imagers in reducing technical burden and improving efficiency and quality of care. High-quality prospective evidence is still needed to demonstrate how the benefits of DL CVI systems may outweigh the risks.

PROTEUS Study: A Prospective Randomized Controlled Trial Evaluating the Use of Artificial Intelligence in Stress Echocardiography

BACKGROUND

Stress echocardiography (SE) is one of the most commonly used diagnostic imaging tests for coronary artery disease (CAD) but requires clinicians to visually assess scans to identify patients who may benefit from invasive investigation and treatment. EchoGo Pro provides an automated interpretation of SE based on artificial intelligence (AI) image analysis. In reader studies, use of EchoGo Pro when making clinical decisions improves diagnostic accuracy and confidence. Prospective evaluation in real world practice is now important to understand the impact of EchoGo Pro on the patient pathway and outcome.

METHODS

PROTEUS is a randomized, multicenter, 2-armed, noninferiority study aiming to recruit 2,500 participants from National Health Service (NHS) hospitals in the UK referred to SE clinics for investigation of suspected CAD. All participants will undergo a stress echocardiogram protocol as per local hospital policy. Participants will be randomized 1:1 to a control group, representing current practice, or an intervention group, in which clinicians will receive an AI image analysis report (EchoGo Pro, Ultromics Ltd, Oxford, UK) to use during image interpretation, indicating the likelihood of severe CAD. The primary outcome will be appropriateness of clinician decision to refer for coronary angiography. Secondary outcomes will assess other health impacts including appropriate use of other clinical management approaches, impact on variability in decision making, patient and clinician qualitative experience and a health economic analysis.

DISCUSSION

This will be the first study to assess the impact of introducing an AI medical diagnostic aid into the standard care pathway of patients with suspected CAD being investigated with SE.

TRIAL REGISTRATION

Clinicaltrials.gov registration number NCT05028179, registered on 31 August 2021; ISRCTN: ISRCTN15113915; IRAS ref: 293515; REC ref: 21/NW/0199.

Automated Detection of Apical Foreshortening in Echocardiography Using Statistical Shape Modelling

OBJECTIVE

Automated detection of foreshortening, a common challenge in routine 2-D echocardiography, has the potential to improve quality of acquisitions and reduce the variability of left ventricular measurements. Acquiring and labelling the required training data is challenging due to the time-intensive and highly subjective nature of foreshortened apical views. We aimed to develop an automatic pipeline for the detection of foreshortening. To this end, we propose a method to generate synthetic apical-four-chamber (A4C) views with matching ground truth foreshortening labels.

METHODS

A statistical shape model of the four chambers of the heart was used to synthesise idealised A4C views with varying degrees of foreshortening. Contours of the left ventricular endocardium were segmented in the images, and a partial least squares (PLS) model was trained to learn the morphological traits of foreshortening. The predictive capability of the learned synthetic features was evaluated on an independent set of manually labelled and automatically curated real echocardiographic A4C images.

RESULTS

Acceptable classification accuracy for identification of foreshortened views in the testing set was achieved using logistic regression based on 11 PLS shape modes, with a sensitivity, specificity and area under the receiver operating characteristic curve of 0.84, 0.82 and 0.84, respectively. Both synthetic and real cohorts showed interpretable traits of foreshortening within the first two PLS shape modes, reflected as a shortening in the long-axis length and apical rounding.

CONCLUSION

A contour shape model trained only on synthesized A4C views allowed accurate prediction of foreshortening in real echocardiographic images.

Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm

As the demand for quality healthcare increases, healthcare systems worldwide are grappling with time constraints and excessive workloads, which can compromise the quality of patient care. Artificial intelligence (AI) has emerged as a powerful tool in clinical medicine, revolutionizing various aspects of patient care and medical research. The integration of AI in clinical medicine has not only improved diagnostic accuracy and treatment outcomes, but also contributed to more efficient healthcare delivery, reduced costs, and facilitated better patient experiences. This review article provides an extensive overview of AI applications in history taking, clinical examination, imaging, therapeutics, prognosis and research. Furthermore, it highlights the critical role AI has played in transforming healthcare in developing nations.

Current role and future perspectives of artificial intelligence in echocardiography

Echocardiography is an essential tool in diagnostic cardiology and is fundamental to clinical care. Artificial intelligence (AI) can help health care providers serving as a valuable diagnostic tool for physicians in the field of echocardiography specially on the automation of measurements and interpretation of results. In addition, it can help expand the capabilities of research and discover alternative pathways in medical management specially on prognostication. In this review article, we describe the current role and future perspectives of AI in echocardiography.

Real world hospital costs following stress echocardiography in the UK: a costing study from the EVAREST/BSE-NSTEP multi-entre study

BACKGROUND

Stress echocardiography is widely used to detect coronary artery disease, but little evidence on downstream hospital costs in real-world practice is available. We examined how stress echocardiography accuracy and downstream hospital costs vary across NHS hospitals and identified key factors that affect costs to help inform future clinical planning and guidelines.

METHODS

Data on 7636 patients recruited from 31 NHS hospitals within the UK between 2014 and 2020 as part of EVAREST/BSE-NSTEP clinical study, were used. Data included all diagnostic tests, procedures, and hospital admissions for 12 months after a stress echocardiogram and were costed using the NHS national unit costs. A decision tree was built to illustrate the clinical pathway and estimate average downstream hospital costs. Multi-level regression analysis was performed to identify variation in accuracy and costs at both patient, procedural, and hospital level. Linear regression and extrapolation were used to estimate annual hospital cost-savings associated with increasing predictive accuracy at hospital and national level.

RESULTS

Stress echocardiography accuracy varied with patient, hospital and operator characteristics. Hypertension, presence of wall motion abnormalities and higher number of hospital cardiology outpatient attendances annually reduced accuracy, adjusted odds ratio of 0.78 (95% CI 0.65 to 0.93), 0.27 (95% CI 0.15 to 0.48), 0.99 (95% CI 0.98 to 0.99) respectively, whereas a prior myocardial infarction, angiotensin receptor blocker medication, and greater operator experience increased accuracy, adjusted odds ratio of 1.77 (95% CI 1.34 to 2.33), 1.64 (95% CI 1.22 to 2.22), and 1.06 (95% CI 1.02 to 1.09) respectively. Average downstream costs were £646 per patient (SD 1796) with significant variation across hospitals. The average downstream costs between the 31 hospitals varied from £384-1730 per patient. False positive and false negative tests were associated with average downstream costs of £1446 (SD £601) and £4192 (SD 3332) respectively, driven by increased non-elective hospital admissions, adjusted odds ratio 2.48 (95% CI 1.08 to 5.66), 21.06 (95% CI 10.41 to 42.59) respectively. We estimated that an increase in accuracy by 1 percentage point could save the NHS in the UK £3.2 million annually.

CONCLUSION

This study provides real-world evidence of downstream costs associated with stress echocardiography practice in the UK and estimates how improvements in accuracy could impact healthcare expenditure in the NHS. A real-world downstream costing approach could be adopted more widely in evaluation of imaging tests and interventions to reflect actual value for money and support realistic planning.

Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease

Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide, and it is associated with considerable economic burden. In an ageing, multimorbid population, it has become increasingly important to develop reliable, consistent, low-risk, non-invasive means of diagnosing CAD. The evolution of multiple cardiac modalities in this field has addressed this dilemma to a large extent, not only in providing information regarding anatomical disease, as is the case with coronary computed tomography angiography (CCTA), but also in contributing critical details about functional assessment, for instance, using stress cardiac magnetic resonance (S-CMR). The field of artificial intelligence (AI) is developing at an astounding pace, especially in healthcare. In healthcare, key milestones have been achieved using AI and machine learning (ML) in various clinical settings, from smartwatches detecting arrhythmias to retinal image analysis and skin cancer prediction. In recent times, we have seen an emerging interest in developing AI-based technology in the field of cardiovascular imaging, as it is felt that ML methods have potential to overcome some limitations of current risk models by applying computer algorithms to large databases with multidimensional variables, thus enabling the inclusion of complex relationships to predict outcomes. In this paper, we review the current literature on the various applications of AI in the assessment of CAD, with a focus on multimodality imaging, followed by a discussion on future perspectives and critical challenges that this field is likely to encounter as it continues to evolve in cardiology.

The Role of Artificial Intelligence in Echocardiography

Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.

Recognized and Unrecognized Value of Echocardiography in Guideline and Consensus Documents Regarding Patients With Chest Pain

Guideline and consensus documents have recently been published on the important topic of the noninvasive evaluation of patients presenting with chest pain (CP) or patients with known acute or chronic coronary syndromes. Authors for these documents have included members representing multispecialty imaging societies, yet the process of generating consensus and the need to produce concise written documents have led to a situation where the particular advantages of echocardiography are overlooked. Broad guidelines such as these can be helpful when it comes to "when to do" noninvasive cardiac testing, but they do not pretend to offer nuances on "how to do" noninvasive cardiac testing. This report details the particular value of echocardiography and potential explanations for its understated role in recent guidelines. This report is categorized into the following sections: (1) impact of the level of evidence on guideline creation; (2) versatility of echocardiography in the assessment of CP and the inimitable role for echo Doppler echocardiography in the assessment of dyspnea; (3) value of point-of-care ultrasound in assessing CP and dyspnea; and (4) the future role of echocardiography in ischemic heart disease.

Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective

Recent advances in machine learning (ML) have made it possible to analyze high-dimensional and complex data-such as free text, images, waveforms, videos, and sound-in an automated manner by successfully learning complex associations within these data. Cardiovascular medicine is particularly well poised to take advantage of these ML advances, due to the widespread digitization of medical data and the large number of diagnostic tests used to evaluate cardiovascular disease. Various ML approaches have successfully been applied to cardiovascular tests and diseases to automate interpretation, accurately perform measurements, and, in some cases, predict novel diagnoses from less invasive tests, effectively expanding the utility of more widely accessible diagnostic tests. Here, we present examples of some impactful advances in cardiovascular medicine using ML across a variety of modalities, with a focus on deep learning applications.

Focused Cardiac Ultrasound to Guide the Diagnosis of Heart Failure in Pregnant Women in India

BACKGROUND

Cardiac complications are a leading cause of maternal death. Cardiac imaging with echocardiography is important for prompt diagnosis, but it is not available in many low-resource settings. The aim of this study was to determine whether focused cardiac ultrasound performed by trained obstetricians and interpreted remotely by experts can identify cardiac abnormalities in pregnant women in low-resource settings.

METHODS

A cross-sectional study was conducted among 301 pregnant and postpartum women recruited from 10 hospitals across three states in India. Twenty-two obstetricians were trained in image acquisition using a portable cardiac ultrasound device following a simplified protocol adapted from focus-assessed transthoracic echocardiography protocol. It included parasternal long-axis, parasternal short-axis, and apical four-chamber views on two-dimensional and color Doppler. Independent image interpretation was performed remotely by two experts, in the United Kingdom and India, using a standard semiquantitative assessment protocol. Interrater agreement between the experts was examined using Cohen's κ. Diagnostic accuracy of the method was examined in a subsample for whom both focused and conventional scans were available.

RESULTS

Cardiac abnormalities identified using the focused method included valvular abnormalities (27%), rheumatic heart disease (6.6%), derangements in left ventricular size (4.7%) and function (22%), atrial dilatation (19.5%), and pericardial effusion (30%). There was substantial agreement on the cardiac parameters between the two experts, ranging from 93.6% (κ = 0.84) for left ventricular ejection fraction to 100% (κ = 1) for valvular disease. Image quality was graded as good in 79% of parasternal long-axis, 77% of parasternal short-axis and 64% of apical four-chamber views. The chance-corrected κ coefficients indicated fair to moderate agreement (κ = 0.28-0.51) for the image quality parameters. There was good agreement on diagnosis between the focused method and standard echocardiography (78% agreement), compared in 36 participants.

CONCLUSIONS

The focused method accurately identified cardiac abnormalities in pregnant women and could be used for screening cardiac problems in obstetric settings.

Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review

Background

Coronary artery disease (CAD) is a progressive disease of the blood vessels supplying the heart, which leads to coronary artery stenosis or obstruction and is life-threatening. Early diagnosis of CAD is essential for timely intervention. Imaging tests are widely used in diagnosing CAD, and artificial intelligence (AI) technology is used to shed light on the development of new imaging diagnostic markers.

Objective

We aim to investigate and summarize how AI algorithms are used in the development of diagnostic models of CAD with imaging markers.

Methods

This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline. Eligible articles were searched in PubMed and Embase. Based on the predefined included criteria, articles on coronary heart disease were selected for this scoping review. Data extraction was independently conducted by two reviewers, and a narrative synthesis approach was used in the analysis.

Results

A total of 46 articles were included in the scoping review. The most common types of imaging methods complemented by AI included single-photon emission computed tomography (15/46, 32.6%) and coronary computed tomography angiography (15/46, 32.6%). Deep learning (DL) (41/46, 89.2%) algorithms were used more often than machine learning algorithms (5/46, 10.8%). The models yielded good model performance in terms of accuracy, sensitivity, specificity, and AUC. However, most of the primary studies used a relatively small sample (n < 500) in model development, and only few studies (4/46, 8.7%) carried out external validation of the AI model.

Conclusion

As non-invasive diagnostic methods, imaging markers integrated with AI have exhibited considerable potential in the diagnosis of CAD. External validation of model performance and evaluation of clinical use aid in the confirmation of the added value of markers in practice.

Systematic review registration

[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022306638], identifier [CRD42022306638].

The role of cardiac magnetic resonance in identifying appropriate candidates for cardiac resynchronization therapy - a systematic review of the literature

Despite the strict indications for cardiac resynchronization therapy (CRT) implantation, a significant proportion of patients will fail to adequately respond to the treatment. This systematic review aims to present the existing evidence about the role of cardiac magnetic resonance (CMR) in identifying patients who are likely to respond better to the CRT. A systematic search in the MedLine database and Cochrane Library from their inception to August 2021 was performed, without any limitations, by two independent investigators. We considered eligible observational studies or randomized clinical trials (RCTs) that enrolled patients > 18 years old with heart failure (HF) of ischaemic or non-ischaemic aetiology and provided data about the association of baseline CMR variables with clinical or echocardiographic response to CRT for at least 3 months. This systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA Statement). Following our search strategy, 47 studies were finally included in our review. CMR appears to have an additive role in identifying the subgroup of patients who will respond better to CRT. Specifically, the presence and the extent of myocardial scar were associated with increased non-response rates, while those with no scar respond better. Furthermore, existing data show that scar location can be associated with CRT response rates. CMR-derived markers of mechanical desynchrony can also be used as predictors of CRT response. CMR data can be used to optimize the position of the left ventricular lead during the CRT implantation procedure. Specifically, positioning the left ventricular lead in a branch of the coronary sinus that feeds an area with transmural scar was associated with poorer response to CRT. CMR can be used as a non-invasive optimization tool to identify patients who are more likely to achieve better clinical and echocardiographic response following CRT implantation.

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.