Three-dimensional echocardiographic quantification of the left-heart chambers using an automated adaptive analytics algorithm: multicentre validation study

Estimation of Systolic and Diastolic Left Ventricular Blood Flow From Derivatives of Transesophageal Echocardiographic 3D Volume Curves in Cardiac Surgery Patients: A Proof-of-Concept Study

OBJECTIVES

To examine whether estimates of peak global systolic (S') and diastolic (E') left ventricular (LV) flow rates based on 3D echocardiographic volumes are feasible and match physiology.

METHODS

In this retrospective feasibility study, we included patients undergoing major cardiac surgery. S' and E' were derived from 190 patients by taking the first derivative of the volume-time relationship of 3D ecg-gated transesophageal echocardiography (TEE) images. To examine the quality of images upon which the estimates of flow were based we correlated intraoperative 3D TEE and preoperative 2D transthoracic echocardiography (TTE) volumes. As a proof-of-concept, we then correlated S' flow with stroke volume and S' and E' were compared by valve pathology.

RESULTS

In each of the 190 images, S' and E' were derived. There was good correlation between 1) the ejection fraction (EF) of 3D LV images obtained intraoperatively by TEE and preoperatively by TTE (Pearson's r = 0.65) and also 2) S' and stroke volume (Pearson's r = 0.73). Patients with aortic or mitral regurgitation showed higher S' than patients without valve pathologies (-315 mL/s [95% CI -388 mL/s to -264 mL/s]P = 0.001, -319 mL/s [95% CI -397 mL/s to -246 mL/s]P = 0.001 vs -242 mL/s [95% CI -300 mL/s to -196 mL/s]). These patients also showed higher E' than patients without valve pathologies (302 mL/s [95% CI 237 mL/s to 384 mL/s]P = 0.006, 341 mL/s [95%CI 227 mL/s to 442 mL/s]P = 0.001 vs 240 mL/s [95%CI 185 mL/s to 315 mL/s]). Patients with aortic stenosis showed no difference in S' or E' (-263 mL/s [95%CI -300 mL/s to -212 mL/s]P = 0.793, 255 mL/s [95%CI 188 mL/s to 344 mL/s]P = 0.400).

CONCLUSIONS

Estimates of global peak systolic and diastolic LV flow based on 3D TEE are feasible, promising, and match valve pathologies.

The Role of Artificial Intelligence and Machine Learning in Cardiovascular Imaging and Diagnosis: Current Insights and Future Directions

Cardiovascular diseases (CVDs) are the major cause of mortality worldwide, emphasizing the critical need for timely and accurate diagnosis. Artificial intelligence (AI) and machine learning (ML) have become revolutionary tools in the healthcare system with significant potential for cardiovascular diagnosis and imaging. AI and ML techniques, including supervised and unsupervised learning, logistic regression, deep learning models, neural networks, and convolutional neural networks (CNNs), have significantly advanced cardiovascular imaging. Applications in echocardiography include left and right ventricular segmentation, ejection fraction measurement, and wall motion analysis. AI and ML hold substantial promise for revolutionizing cardiovascular imaging, demonstrating improvements in diagnostic accuracy and efficiency. This narrative review aims to explore the current applications, advantages, challenges, and future pathways of AI and ML in cardiovascular imaging, highlighting their impact on different imaging modalities and their integration into clinical practice.

Charting the Unseen: How Non-Invasive Imaging Could Redefine Cardiovascular Prevention

Cardiovascular diseases (CVDs) remain a major global health challenge, leading to significant morbidity and mortality while straining healthcare systems. Despite progress in medical treatments for CVDs, their increasing prevalence calls for a shift towards more effective prevention strategies. Traditional preventive approaches have centered around lifestyle changes, risk factors management, and medication. However, the integration of imaging methods offers a novel dimension in early disease detection, risk assessment, and ongoing monitoring of at-risk individuals. Imaging techniques such as supra-aortic trunks ultrasound, echocardiography, cardiac magnetic resonance, and coronary computed tomography angiography have broadened our understanding of the anatomical and functional aspects of cardiovascular health. These techniques enable personalized prevention strategies by providing detailed insights into the cardiac and vascular states, significantly enhancing our ability to combat the progression of CVDs. This review focuses on amalgamating current findings, technological innovations, and the impact of integrating advanced imaging modalities into cardiovascular risk prevention, aiming to offer a comprehensive perspective on their potential to transform preventive cardiology.

How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease?

Improvements in medical technology have significantly changed the management of congenital heart disease (CHD), offering novel tools to predict outcomes and personalize follow-up care. By using sophisticated imaging modalities, computational models and machine learning algorithms, clinicians can experiment with unprecedented insights into the complex anatomy and physiology of CHD. These tools enable early identification of high-risk patients, thus allowing timely, tailored interventions and improved outcomes. Additionally, the integration of genetic testing offers valuable prognostic information, helping in risk stratification and treatment optimisation. The birth of telemedicine platforms and remote monitoring devices facilitates customised follow-up care, enhancing patient engagement and reducing healthcare disparities. Taking into consideration challenges and ethical issues, clinicians can make the most of the full potential of artificial intelligence (AI) to further refine prognostic models, personalize care and improve long-term outcomes for patients with CHD. This narrative review aims to provide a comprehensive illustration of how AI has been implemented as a new technological method for enhancing the management of CHD.

Pragmatic Evaluation of a Deep-Learning Algorithm to Automate Ejection Fraction on Hand-Held, Point-of-Care Echocardiography in a Cardiac Surgical Operating Room

OBJECTIVE

To test the correlation of ejection fraction (EF) estimated by a deep-learning-based, automated algorithm (Auto EF) versus an EF estimated by Simpson's method.

DESIGN

A prospective observational study.

SETTING

A single-center study at the Hospital of the University of Pennsylvania.

PARTICIPANTS

Study participants were ≥18 years of age and scheduled to undergo valve, aortic, coronary artery bypass graft, heart, or lung transplant surgery.

INTERVENTIONS

This noninterventional study involved acquiring apical 4-chamber transthoracic echocardiographic clips using the Philips hand-held ultrasound device, Lumify.

MEASUREMENTS AND MAIN RESULTS

In the primary analysis of 54 clips, compared to Simpson's method for EF estimation, bias was similar for Auto EF (-10.17%) and the experienced reader-estimated EF (-9.82%), but the correlation was lower for Auto EF (r = 0.56) than the experienced reader-estimated EF (r = 0.80). In the secondary analyses, the correlation between EF estimated by Simpson's method and Auto EF increased when applied to 27 acquisitions classified as adequate (r = 0.86), but decreased when applied to 27 acquisitions classified as inadequate (r = 0.46).

CONCLUSIONS

Applied to acquisitions of adequate image quality, Auto EF produced a numerical EF estimate equivalent to Simpson's method. However, when applied to acquisitions of inadequate image quality, discrepancies arose between EF estimated by Auto EF and Simpson's method. Visual EF estimates by experienced readers correlated highly with Simpson's method in both variable and inadequate imaging conditions, emphasizing its enduring clinical utility.

Volume and function changes of left atrium and left ventricle in patients with ejection fraction preserved heart failure measured by a three dimensional dynamic heart model

The accurate diagnosis of HFpEF is still challenging and controversial. In this study, we used 3D-DHM technology to compare the differences of cardiac structure and function between HFpEF patients and healthy controls, as well as the differences of two-dimensional and three-dimensional cardiac function in HFpEF patients. Echocardiography with 3D-DHM and conventional two-dimensional (2D) methods were applied to measure the volume and function parameters of left atrium and ventricle of patients with HFpEF and healthy controls. Significant differences of 3D cardiac function indexes including LVESV, 3D-LVEF, ESL, SV, CI, EDmass, LAVmax, LAVmin, LAEF, and LAVI were observed between patients with HFpEF and controls (P < 0.05). However, no significant difference of LVEDV and EDL were observed (P > 0.05). In addition, we found no significant between-group difference in 2D cardiac function indexes such as LVDD and 2D-LVEF (P > 0.05), but the LAD, LVSD, LVPW, IVS, E, E/A, and E/e ' were significantly different between groups (P < 0.05). There was no significant difference between 3D-LVEF and 2D-LVEF in the control group (P > 0.05), while 3D-LVEF in the HFpEF group was lower than 2D-LVEF(P < 0.05). Among the two-dimensional and three-dimensional parameters of HFpEF patients, the parameters related to diastolic function changed more significantly than those of the normal group, and the three-dimensional LVEF of HFpEF patients decreased. The three-dimensional cardiac function parameters analyzed by DHM can provide more information regarding myocardial mechanics.

Factors associated with reporting left ventricular ejection fraction with 3D echocardiography in real-world practice

BACKGROUND

Guidelines recommend 3D echocardiography (3DE) to assess left ventricular ejection fraction (LVEF) on transthoracic echocardiogram (TTE) when possible, but it is unclear which factors are most strongly associated with reporting 3DE LVEF in real-world practice.

METHODS

We evaluated 3DE LVEF reporting by age, sex, BMI, TTE location and variation in reporting by sonographer and reader. All TTEs were performed without contrast enhancement agent at a large medical center from 9/2015 to 12/2020 using ultrasound machines capable of 3DE. We used multivariable logistic regression to assess which factors were most associated with reporting 3DE LVEF.

RESULTS

Among 35 641 TTEs included in this study, 57.4% were performed on women. 3DE LVEF was reported on 18 391 TTEs (51.6% of cohort; 50.5% for women and 52.4% for men). Portable inpatient TTEs (n = 5569) had the lowest rates of 3DE LVEF reporting (30.9%), while general outpatient TTEs (n = 15 933) had greater reporting (56.9%). Outpatient TTEs with an indication for chemotherapy (n = 3244) had the highest rates of 3DE LVEF (87.2%). The median (IQR) percentage of TTEs reporting 3D LVEF was 52.7% (43.1%-68.1%) among sonographers and 51.6% (46.5%-59.6%) among readers. Among 20082 (56.3%) TTEs with 3DE LVEF measured by sonographers, 91.6% were included by readers in the final report. After adjustment, performing sonographer in the highest reporting quartile was most strongly associated with reporting 3DE LVEF (OR 7.04, 95% CI 6.55-7.56), while an inpatient portable study had the strongest negative association for reporting (OR .38, 95% CI .35-.40).

CONCLUSIONS

Use of 3DE LVEF in real-world practice varies substantially based on performing sonographer and is low for hospitalized patients, but can be frequently used for chemotherapy. Initiatives are needed to increase sonographer 3DE acquisition in most clinical settings.

Using Artificial Intelligence for Rheumatic Heart Disease Detection by Echocardiography: Focus on Mitral Regurgitation

BACKGROUND

Identification of children with latent rheumatic heart disease (RHD) by echocardiography, before onset of symptoms, provides an opportunity to initiate secondary prophylaxis and prevent disease progression. There have been limited artificial intelligence studies published assessing the potential of machine learning to detect and analyze mitral regurgitation or to detect the presence of RHD on standard portable echocardiograms.

METHODS AND RESULTS

We used 511 echocardiograms in children, focusing on color Doppler images of the mitral valve. Echocardiograms were independently reviewed by an expert adjudication panel. Among 511 cases, 229 were normal, and 282 had RHD. Our automated method included harmonization of echocardiograms to localize the left atrium during systole using convolutional neural networks and RHD detection using mitral regurgitation jet analysis and deep learning models with an attention mechanism. We identified the correct view with an average accuracy of 0.99 and the correct systolic frame with an average accuracy of 0.94 (apical) and 0.93 (parasternal long axis). It localized the left atrium with an average Dice coefficient of 0.88 (apical) and 0.9 (parasternal long axis). Maximum mitral regurgitation jet measurements were similar to expert manual measurements (P value=0.83) and a 9-feature mitral regurgitation analysis showed an area under the receiver operating characteristics curve of 0.93, precision of 0.83, recall of 0.92, and F1 score of 0.87. Our deep learning model showed an area under the receiver operating characteristics curve of 0.84, precision of 0.78, recall of 0.98, and F1 score of 0.87.

CONCLUSIONS

Artificial intelligence has the potential to detect RHD as accurately as expert cardiologists and to improve with more data. These innovative approaches hold promise to scale echocardiography screening for RHD.

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.

Pressure Overload and Right Ventricular Failure: From Pathophysiology to Treatment

Right ventricular failure (RVF) is often caused by increased afterload and disrupted coupling between the right ventricle (RV) and the pulmonary arteries (PAs). After a phase of adaptive hypertrophy, pressure-overloaded RVs evolve towards maladaptive hypertrophy and finally ventricular dilatation, with reduced stroke volume and systemic congestion. In this article, we review the concept of RV-PA coupling, which depicts the interaction between RV contractility and afterload, as well as the invasive and non-invasive techniques for its assessment. The current principles of RVF management based on pathophysiology and underlying etiology are subsequently discussed. Treatment strategies remain a challenge and range from fluid management and afterload reduction in moderate RVF to vasopressor therapy, inotropic support and, occasionally, mechanical circulatory support in severe RVF.

The Use of Artificial Intelligence Guidance for Rheumatic Heart Disease Screening by Novices

INTRODUCTION

A novel technology utilizing artificial intelligence (AI) to provide real-time image-acquisition guidance, enabling novices to obtain diagnostic echocardiographic images, holds promise to expand the reach of echo screening for rheumatic heart disease (RHD). We evaluated the ability of nonexperts to obtain diagnostic-quality images in patients with RHD using AI guidance with color Doppler.

METHODS

Novice providers without prior ultrasound experience underwent a 1-day training curriculum to complete a 7-view screening protocol using AI guidance in Kampala, Uganda. All trainees then scanned 8 to 10 volunteer patients using AI guidance, half RHD and half normal. The same patients were scanned by 2 expert sonographers without the use of AI guidance. Images were evaluated by expert blinded cardiologists to assess (1) diagnostic quality to determine presence/absence of RHD and (2) valvular function and (3) to assign an American College of Emergency Physicians score of 1 to 5 for each view.

RESULTS

Thirty-six novice participants scanned a total of 50 patients, resulting in a total of 462 echocardiogram studies, 362 obtained by nonexperts using AI guidance and 100 obtained by expert sonographers without AI guidance. Novice images enabled diagnostic interpretation in >90% of studies for presence/absence of RHD, abnormal MV morphology, and mitral regurgitation (vs 99% by experts, P ≤ .001). Images were less diagnostic for aortic valve disease (79% for aortic regurgitation, 50% for aortic stenosis, vs 99% and 91% by experts, P < .001). The American College of Emergency Physicians scores of nonexpert images were highest in the parasternal long-axis images (mean, 3.45; 81% ≥ 3) compared with lower scores for apical 4-chamber (mean, 3.20; 74% ≥ 3) and apical 5-chamber images (mean, 2.43; 38% ≥ 3).

CONCLUSIONS

Artificial intelligence guidance with color Doppler is feasible to enable RHD screening by nonexperts, performing significantly better for assessment of the mitral than aortic valve. Further refinement is needed to optimize acquisition of color Doppler apical views.

Assessment of Right Ventricle Function and Tricuspid Regurgitation in Heart Failure: Current Advances in Diagnosis and Imaging

Right ventricular (RV) systolic dysfunction increases mortality among heart failure patients, and therefore, accurate diagnosis and monitoring is paramount. RV anatomy and function are complex, usually requiring a combination of imaging modalities to completely quantitate volumes and function. Tricuspid regurgitation usually occurs with RV dysfunction, and quantifying this valvular lesion also may require multiple imaging modalities. Echocardiography is the first-line imaging tool for identifying RV dysfunction, with cardiac MRI and cardiac computed tomography adding valuable additional information.

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.

Echocardiographic evaluation of left ventricular function using an automated analysis algorithm is feasible for beginners and experts: comparison with invasive and non-invasive methods

AIMS

Echocardiographic measurement of left ventricular function using a user-friendly automated three-dimensional algorithm is highly attractive as it promises quick and accurate diagnosis, circumventing limitations associated with visual estimation or manual biplane measurements. We sought to assess the feasibility and correlation of such automated analysis with clinically established methods.

METHODS

A total of 198 patients undergoing transthoracic echocardiography (TTE) with assessment of left ventricular parameters by automated software algorithm (Philips 3D-Heartmodel; 3D-HM) which additionally had either left ventricular angiography (LVA) or cardiac magnetic resonance (CMR) within 24 h of the TTE examination were analyzed. Left ventricular parameters (left ventricular end-diastolic volume, LVEDV, left ventricular end-systolic volume, LVESV as well as left ventricular ejection fraction, LVEF) were compared between 3D-HM, CMR and LVA.

RESULTS

Correlation of left ventricular measurements was overall good to excellent and stronger for CMR (EF r = 0.824) than for LVA (EF r = 0.746). Unexperienced and expert clinicians yielded comparable good results. For CMR, highest correlation was detected in patients with BMI < 25 and excellent image quality. High agreement was seen between 3D-HM and CMR or LVA when stratifying patients according to heart failure categories.

CONCLUSIONS

Echocardiographic quantification of left ventricular parameters using a software-based algorithm correlated well with established invasive and non-invasive modalities in the clinical setting, even for unexperienced clinicians. Such automated approaches are promising as they allow a reliable, more observer-independent as well as reproducible assessment of left ventricular function.

Artificial intelligence in cardiovascular imaging: enhancing image analysis and risk stratification

In this review, we summarize state-of-the-art artificial intelligence applications for non-invasive cardiovascular imaging modalities including CT, MRI, echocardiography, and nuclear myocardial perfusion imaging.

Left ventricular volume and ejection fraction measurements by fully automated 3D echocardiography left chamber quantification software versus CMR: A systematic review and meta-analysis

BACKGROUND

Although transthoracic three-dimensional echocardiography (3DE) is now recommended by guidelines for left ventricular (LV) volumetric measurements, widespread implementation has been limited due to time constraints and required expertise. We hypothesized that fully automated 3DE left chamber quantification software might provide accurate measurements, and that its application could eliminate these obstacles.

METHODS

To address this hypothesis, we conducted a systematic review and meta-analysis following a search for studies that compared LV volumes and ejection fraction (EF) using fully automated 3DE software (HeartModel or Dynamic HeartModel, Philips Healthcare, Andover, MA, USA) with cardiac magnetic resonance (CMR), from 2015 to 2021. A random effects model was used to determine biases, correlations, and 95 % confidence intervals (CI) of LV end-diastolic volume (EDV), end-systolic volume (ESV), and EF. Subgroup and meta-regression analyses were performed to determine effects of moderators on the outcome.

RESULTS

Of 12 studies (616 subjects), mean differences and 95 % CIs in EDV, ESV, and EF between fully automated 3DE software and CMR were -19.6 mL (95 % CI; -27.6 to -11.5 mL), -11.4 mL (-16.7 to -6.2 mL), and 0.4 % (-1.1 to 2.0 %), respectively. Corresponding correlation values between the two methods were 0.91 (0.86-0.94), 0.89 (0.82-0.93), and 0.85 (0.81-0.88), respectively. Meta-regression analysis revealed that there were no effects of either publication year, type of software, or type of analysis on the outcome of LV volumetric and functional parameters except for publication year on LVESV correlation values.

CONCLUSIONS

Although 3DE still underestimates LV volumes, the observed differences were no >20 mL. EF showed similar values to CMR. Excellent correlations between the two techniques make fully automated 3DE left chamber quantification software useful for routine clinical practice in adult population.

Interchangeability of right ventricular longitudinal shortening fraction assessed by transthoracic and transoesophageal echocardiography in the perioperative setting: A prospective study

Background

Conventional transthoracic (TTE) and transoesophageal echocardiography (TEE) parameters assessing right ventricle (RV) systolic function are daily used assuming their clinical interchangeability. RV longitudinal shortening fraction (RV-LSF) is a two-dimensional speckle tracking parameter used to assess RV systolic function. RV-LSF is based on tricuspid annular displacement analysis and could be measured with TTE or TEE.

Objective

The aim of the study was to determine if RV-LSF_TTE and RV-LSF_TEE measurements were interchangeable in the perioperative setting.

Methods

Prospective perioperative TTE and TEE echocardiography were performed under general anesthesia during scheduled cardiac surgery in 90 patients. RV-LSF was measured by semi-automatic software. Comparisons were performed using Pearson correlation and Bland-Altman plots. RV-LSF clinical agreement was determined as a range of -5 to 5%.

Results

Of the 114 patients who met the inclusion criteria, 90 were included. The mean preoperative RV-LSF_TTE was 20.4 ± 4.3 and 21.1 ± 4.1% for RV-LSF_TEE. The agreement between RV-LSF measurements was excellent, with a bias at -0.61 and limits of agreement of -4.18 to 2.97 %. All measurements fell within the determined clinical agreement interval in the Bland-Altman plot. Linear regression analysis showed a high correlation between RV-LSF_TTE and RV-LSF_TEE measurement (r = 0.9; confidence interval [CI] 95%: [0.87-0.94], p < 0.001).

Conclusion

RV-LSF_TTE and RV-LSF_TEE measurements are interchangeable, allowing RV-LSF to be a helpful parameter for assessing perioperative changes in RV systolic function.

NCT

NCT05404737. https://www.clinicaltrials.gov/ct2/show/NCT05404737.

Three-Dimensional Automated, Machine-Learning-Based Left Heart Chamber Metrics: Associations with Prevalent Vascular Risk Factors and Cardiovascular Diseases

Background. Three-dimensional transthoracic echocardiography (3DE) powered by artificial intelligence provides accurate left chamber quantification in good accordance with cardiac magnetic resonance and has the potential to revolutionize our clinical practice. Aims. To evaluate the association and the independent value of dynamic heart model (DHM)-derived left atrial (LA) and left ventricular (LV) metrics with prevalent vascular risk factors (VRFs) and cardiovascular diseases (CVDs) in a large, unselected population. Materials and Methods. We estimated the association of DHM metrics with VRFs (hypertension, diabetes) and CVDs (atrial fibrillation, stroke, ischemic heart disease, cardiomyopathies, >moderate valvular heart disease/prosthesis), stratified by prevalent disease status: participants without VRFs or CVDs (healthy), with at least one VRFs but without CVDs, and with at least one CVDs. Results. We retrospectively included 1069 subjects (median age 62 [IQR 49−74]; 50.6% women). When comparing VRFs with the healthy, significant difference in maximum and minimum indexed atrial volume (LAVi max and LAVi min), left atrial ejection fraction (LAEF), left ventricular mass/left ventricular end-diastolic volume ratio, and left ventricular global function index (LVGFI) were recorded (p < 0.05). In the adjusted logistic regression, LAVi min, LAEF, LV ejection fraction, and LVGFI showed the most robust association (OR 3.03 [95% CI 2.48−3.70], 0.45 [95% CI 0.39−0.51], 0.28 [95% CI 0.22−0.35], and 0.22 [95% CI 0.16−0.28], respectively, with CVDs. Conclusions. The present data suggested that novel 3DE left heart chamber metrics by DHM such as LAEF, LAVi min, and LVGFI can refine our echocardiographic disease discrimination capacity.

The application of artificial intelligence in the sonography profession: Professional and educational considerations

The integration of artificial intelligence (AI) technology within the health industry is increasing. This educational piece discusses the implementation of AI and its impact on sonography. The authors investigate how AI may influence the profession and provide examples of how ultrasound imaging may be enhanced and innovated by integrating AI technology. This article highlights challenges related to the application of AI and provides insight into how they could be addressed. The critical distinction between the role of a sonographer and the reporting specialist in the context of AI is highlighted as a key issue for those developing, researching, and evaluating AI systems. A key recommendation is for the sonography community to address ultrasound education, particularly how AI knowledge could be incorporated into university education. This is an important consideration that should be extended to practising professionals as they may be involved in evaluating the efficiency and methodologies used in new research that may incorporate AI technologies.

Comparison between Automatic and Semiautomatic System for the 3D Echocardiographic Multiparametric Evaluation of RV Function and Dimension

Background: The right ventricle (RV) plays a pivotal role in cardiovascular diseases and 3-dimensional echocardiography (3DE) has gained acceptance for the evaluation of RV volumes and function. Recently, a new artificial intelligence (AI)–based automated 3DE software for RV evaluation has been proposed and validated against cardiac magnetic resonance. The aims of this study were three-fold: (i) feasibility of the AI-based 3DE RV quantification, (ii) comparison with the semi-automatic 3DE method and (iii) assessment of 2-dimensional echocardiography (2DE) and strain measurements obtained automatically. Methods: A total of 203 subject (122 normal and 81 patients) underwent a 2DE and both the semi-automatic and automatic 3DE methods for Doppler standard, RV volumes and ejection fraction (RVEF) measurements. Results: The automatic 3DE method was highly feasible, faster than 2DE and semi-automatic 3DE and data obtained were comparable with traditional measurements. Both in normal subjects and patients, the RVEF was similar to the two 3DE methods and 2DE and strain measurements obtained by the automated system correlated very well with the standard 2DE and strain ones. Conclusions: results showed that rapid analysis and excellent reproducibility of AI-based 3DE RV analysis supported the routine adoption of this automated method in the daily clinical workflow.

Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects

Cardiovascular disease (CVD) represents a significant and increasing burden on healthcare systems. Artificial intelligence (AI) is a rapidly evolving transdisciplinary field employing machine learning (ML) techniques, which aim to simulate human intuition to offer cost-effective and scalable solutions to better manage CVD. ML algorithms are increasingly being developed and applied in various facets of cardiovascular medicine, including and not limited to heart failure, electrophysiology, valvular heart disease and coronary artery disease. Within heart failure, AI algorithms can augment diagnostic capabilities and clinical decision-making through automated cardiac measurements. Occult cardiac disease is increasingly being identified using ML from diagnostic data. Improved diagnostic and prognostic capabilities using ML algorithms are enhancing clinical care of patients with valvular heart disease and coronary artery disease. The growth of AI techniques is not without inherent challenges, most important of which is the need for greater external validation through multicenter, prospective clinical trials.

Human vs Artificial Intelligence-Based Echocardiography Analysis as Predictor of Outcomes: An analysis from the World Alliance Societies of Echocardiography COVID study

Background

Transthoracic echocardiography is the leading cardiac imaging modality for patients admitted with COVID-19, a condition of high short-term mortality. The aim of this study was to test the hypothesis that artificial intelligence (AI)–based analysis of echocardiographic images could predict mortality more accurately than conventional analysis by a human expert.

Methods

Patients admitted to 13 hospitals for acute COVID-19 who underwent transthoracic echocardiography were included. Left ventricular ejection fraction (LVEF) and left ventricular longitudinal strain (LVLS) were obtained manually by multiple expert readers and by automated AI software. The ability of the manual and AI analyses to predict all-cause mortality was compared.

Results

In total, 870 patients were enrolled. The mortality rate was 27.4% after a mean follow-up period of 230 ± 115 days. AI analysis had lower variability than manual analysis for both LVEF (P = .003) and LVLS (P = .005). AI-derived LVEF and LVLS were predictors of mortality in univariable and multivariable regression analysis (odds ratio, 0.974 [95% CI, 0.956-0.991; P = .003] for LVEF; odds ratio, 1.060 [95% CI, 1.019-1.105; P = .004] for LVLS), but LVEF and LVLS obtained by manual analysis were not. Direct comparison of the predictive value of AI versus manual measurements of LVEF and LVLS showed that AI was significantly better (P = .005 and P = .003, respectively). In addition, AI-derived LVEF and LVLS had more significant and stronger correlations to other objective biomarkers of acute disease than manual reads.

Conclusions

AI-based analysis of LVEF and LVLS had similar feasibility as manual analysis, minimized variability, and consequently increased the statistical power to predict mortality. AI-based, but not manual, analyses were a significant predictor of in-hospital and follow-up mortality.

Artificial intelligence applied to cardiovascular imaging, a critical focus on echocardiography: The point-of-view from "the other side of the coin"

Cardiovascular imaging has achieved a crucial role in the management of cardiovascular diseases. In this field, echocardiography advantages include wide availability, portability, and affordability, at a relatively low cost. However, echocardiographic assessment requires highly trained operators, and implies high observer variability, as compared with the other cardiac imaging modalities. Hence, artificial intelligence might be extremely helpful. From the point-of-view of the peripheral "Spoke" Hospital potential user ("the other side of the coin"), artificial intelligence development appears very slow in the clinical arena. Many limitations are still present, and require full involvement, cooperation, and coordination of professional operators into Hub-and-Spoke network.

Additional value of left atrium remodeling assessed by three-dimensional echocardiography for the prediction of atrial fibrillation recurrence after cryoballoon ablation

Cryoballoon ablation (CBA) is a safe and efficient therapeutic option for atrial fibrillation (AF). However, AF recurrence occurs in 25% of the patients, leading to repeated ablations and complications. Previous reports have shown that left atrium (LA) assessed by M-Mode and two-dimensional echocardiography (2DE) predicts AF recurrence. Nevertheless, these methods imply geometrical assumptions of the LA remodeling, which is a three-dimensional process. We hypothesized that LA remodeling by three-dimensional echocardiography (3DE) has an additional value for AF recurrence prediction post-CBA. 172 consecutive patients (62.2 ± 12.2 years, 61% male) were prospectively recruited. Echocardiography was performed before CBA. Blanking period was defined as the first three months post-ablation. The primary endpoint was AF recurrence after the blanking period. 50 (29%) patients had AF recurrence. 3DE LA maximum volume index (LAVI) had the highest incremental predictive value for AF recurrence (HR 5.50, 95% CI 1.34 -22.45, p < 0.001). In patients with non-dilated LA diameter index and LAVI by 2DE, LAVI by 3DE was able to discriminate AF recurrence with a sensitivity of 90% and a specificity of 66%, for an optimal cut-off value of 30.4 ml/m2. LA remodeling by 3DE predicted AF recurrence, even in patients with non-dilated LA by M-Mode and 2DE, suggesting that 3DE might reflect better and earlier the asymmetric and variable nature of LA remodeling and it should be considered for systematic use to evaluate AF recurrence risk post-CBA.

Left Heart Chamber Volumetric Assessment by Automated Three-Dimensional Echocardiography in Heart Transplant Recipients

Background

Recently, a new automated software (Heart Model) was developed to obtain three-dimensional (3D) left heart chamber volumes. The aim of this study was to verify the feasibility and accuracy of the automated 3D echocardiographic algorithm in heart transplant (HTx) patients. Conventional manual 3D transthoracic echocardiographic (TTE) tracings and cardiac magnetic resonance (CMR) images were used as a reference for comparison.

Methods

This study enrolled 103 healthy HTx patients prospectively. In protocol 1, left ventricular end-diastolic volume (LVEDV), LV end-systolic volume (LVESV), left atrial max volume (LAVmax), LA minimum volume (LAVmin) and LV ejection fraction (LVEF) were obtained using the automated 3D echocardiography (3DE) and compared with corresponding values obtained through the manual 3DE. In protocol 2, 28 patients’ automated 3DE measurements were compared with CMR reference values. The impacts of contour edit and surgical technique were also tested.

Results

Heart Model was feasible in 97.1% of the data sets. In protocol 1, there was strong correlation between 3DE and manual 3DE for all the parameters (r = 0.77 to 0.96, p<0.01). Compared to values obtained through manual measurements, LV volumes and LVEF were overestimated by the automated algorithm and LA volumes were underestimated. All the biases were small except for that of LAVmin. After contour adjustment, the biases reduced and all the limits of agreement were clinically acceptable. In protocol 2, the correlations for LV and LA volumes were strong between automated 3DE with contour edit and CMR (r = 0.74 to 0.93, p<0.01) but correlation for LVEF remained moderate (r = 0.65, p < 0.01). Automated 3DE overestimated LV volumes but underestimated LVEF and LA volumes compared with CMR. The limits of agreement were clinically acceptable only for LVEDV and LAVmax.

Conclusion

Simultaneous quantification of left heart volumes and LVEF with the automated Heart Model program is rapid, feasible and to a great degree it is accurate in HTx recipients. Nevertheless, only LVEDV and LAVmax measured by automated 3DE with contour edit seem applicable for clinical practice when compared with CMR. Automated 3DE for HTx recipients is a worthy attempt, though further verification and optimization are needed.

Quantification of chronic aortic regurgitation using left and right ventricular stroke volumes obtained from two new automated three-dimensional transthoracic echocardiographic software: feasibility and accuracy

The present study aimed to evaluate the feasibility and accuracy of chronic aortic regurgitation (CAR) quantification using left and right ventricular stroke volumes (LVSV and RVSV, respectively) obtained from two new automated three-dimensional transthoracic echocardiographic software-Dynamic HeartModel (DHM) and 3D Auto RV. Patients (n=116) with more than mild isolated CAR were included and divided into two groups: central (n=53) and eccentric CAR (n=63) groups. LVSV and RVSV were automatically measured by DHM and 3D Auto RV. Next, aortic regurgitant volume (ARVol) was calculated three ways: as the difference between LVSV and RVSV, by the two-dimensional proximal isovelocity surface area (PISA) method, and using effective regurgitant orifice area derived from real-time three-dimensional echocardiography (RT3DE) multiplied by CAR velocity time integral (the reference standard). DHM plus 3D Auto RV correlated well with RT3DE in ARVol measurement in both groups (central, r = 0.90; eccentric, r = 0.96), with no significant difference based on consistency analysis. In the eccentric group, PISA led to an obvious underestimation (mean difference= - 4.20 ml, P < 0.05). The kappa agreement between DHM plus 3D Auto RV and RT3DE in grading CAR severity in both groups was good (central, k = 0.89; eccentric, k = 0.86), but that between PISA and RT3DE in the eccentric CAR group was suboptimal (k = 0.74). This study indicates that ARVol quantification using DHM plus 3D Auto RV is feasible and reproducible in patients with more than mild isolated CAR. This new method has great correlation and agreement with RT3DE in ARVol measurement, with evident advantages over PISA in eccentric CAR.

Automated three-dimensional echocardiographic quantification for left ventricular volume and function in patients with hypertrophic cardiomyopathy

BACKGROUND

Accurate, reproducible, noninvasive determination of left ventricular (LV) volumes and ejection fraction (EF) is important for clinical assessment, selection of therapy, and serial monitoring of patients with hypertrophic cardiomyopathy (HCM). Current clinical Two-dimensional echocardiography (2DE) may cause inaccurate measurements in patients with HCM because of their asymmetric ventricles and limitations of 2DE technology. Three-dimensional echocardiography (3DE) have demonstrated significantly greater accuracy. However, the time-consuming workflow limits the clinical utility of 3DE.

AIM

We aim to compare the performance of a novel automated 3DE system (HeartModel, Philips Healthcare) with 2DE in a group of patients with HCM. Cardiac magnetic resonance (CMR) was reference standard.

METHODS

Fifty-three patients with HCM were examined by automated 3DE (3DEA), two-dimensional biplane Simpson's method (2DBP), manual 3DE method, and CMR, respectively. For patients with poor automated quantification, manual correction was performed. The Pearson correlation coefficient and Bland-Altman analysis and paired Student t tests were used to assess inter-technique agreement.

RESULTS

3DEA measurements with contour editing correlate well with CMR and manual 2DE and 3DE measurements (r = .80-.96). The analysis time of 3DEA was shorter than that of 2DBP (3DEA, 141 ± 15s; 2DBP, 174 ± 17 s). Inter-observer variability was reduced significantly with use of 3DEA.

CONCLUSION

Compared with current clinical 2DBP method, the analysis time of automated 3DE was much shorter with the added benefit of enhanced accuracy and reproducibility. Patients with asymmetric chamber may rely more on the timesaving automated 3DE quantification in the future.

Diagnostic accuracy and performance of artificial intelligence in measuring left atrial volumes and function on multiphasic CT in patients with atrial fibrillation

OBJECTIVES

To evaluate the effectiveness of a novel artificial intelligence (AI) algorithm for fully automated measurement of left atrial (LA) volumes and function using cardiac CT in patients with atrial fibrillation.

METHODS

We included 79 patients (mean age 63 ± 12 years; 35 with atrial fibrillation (AF) and 44 controls) between 2017 and 2020 in this retrospective study. Images were analyzed by a trained AI algorithm and an expert radiologist. Left atrial volumes were obtained at cardiac end-systole, end-diastole, and pre-atrial contraction, which were then used to obtain LA function indices. Intraclass correlation coefficient (ICC) analysis of the LA volumes and function parameters was performed and receiver operating characteristic (ROC) curve analysis was used to compare the ability to detect AF patients.

RESULTS

The AI was significantly faster than manual measurement of LA volumes (4 s vs 10.8 min, respectively). Agreement between the manual and automated methods was good to excellent overall, and there was stronger agreement in AF patients (all ICCs ≥ 0.877; p < 0.001) than controls (all ICCs ≥ 0.799; p < 0.001). The AI comparably estimated LA volumes in AF patients (all within 1.3 mL of the manual measurement), but overestimated volumes by clinically negligible amounts in controls (all by ≤ 4.2 mL). The AI's ability to distinguish AF patients from controls using the LA volume index was similar to the expert's (AUC 0.81 vs 0.82, respectively; p = 0.62).

CONCLUSION

The novel AI algorithm efficiently performed fully automated multiphasic CT-based quantification of left atrial volume and function with similar accuracy as compared to manual quantification. Novel CT-based AI algorithm efficiently quantifies left atrial volumes and function with similar accuracy as manual quantification in controls and atrial fibrillation patients.

KEY POINTS

• There was good-to-excellent agreement between manual and automated methods for left atrial volume quantification. • The AI comparably estimated LA volumes in AF patients, but overestimated volumes by clinically negligible amounts in controls. • The AI's ability to distinguish AF patients from controls was similar to the manual methods.

Artificial Intelligence in Echocardiography

Artificial intelligence in diagnostic cardiac-imaging platforms is advancing rapidly. In particular, artificial intelligence algorithms have increased the efficiency and accuracy of echocardiographic cardiovascular imaging, resulting in more complex echocardiographic imaging techniques and expanded use among noncardiologists. Here, we provide an overview of real-world applications of artificial intelligence in echocardiography including automatic high-quality computer-optimized image acquisition sequences, automated measurements, and algorithms for the rapid and accurate interpretation of cardiac physiology. These advances will not replace physicians but will improve their productivity, workflow, and diagnostic performance.

Heart Model A.I. Three-Dimensional Echocardiographic Evaluation of Left Ventricular Function and Parameter Setting

Objective

This study aims to explore the feasibility of HeartModel ^A.I. (HM) three-dimensional echocardiography (3DE) to assess left ventricular function and discover suitable border parameter settings.

Methods

A total of 113 patients that underwent echocardiography in our hospital were eligible for inclusion. The HM 3DE (HM method) and conventional 3DE (3D method) were used to analyze echocardiography images. The HM was set to different border settings (end-diastolic [ED] and end-systolic [ES] settings) to assess different left ventricular systolic function parameters including left ventricular end diastolic volume (LVEDV), left ventricular end systolic volume (LVESV), and left ventricular ejection fraction (LVEF), and left ventricular diastolic function parameters including maximal left atrium volume (LAV_MAX). All of these parameters were evaluated using the HM method and then compared with the 3D method.

Results

The differences in LVEDV, LVESV, and LVEF measured with different HM border settings were statistically significant (P<0.05) and were strongly correlated with the 3D method. For LVEF, the reading using the HM method with ED and ES = 70 and 30 showed the best agreement with the 3D method, and the difference in the readings was not statistically significant (P > 0.05). For LVEDV and LVESV, the reading using the HM method with ED and ES = 40 and 20 showed the best agreement with the 3D method, but the difference in the readings was statistically significant (P < 0.05). The measurements taken using the HM method were more reproducible than those taken using the 3D method (P<0.05). The measurement time when using the HM method was significantly less than the 3D method (P<0.05). In terms of LAV_MAX, the correlation between the HM and 3D methods was strong, but the requirements for agreement were not satisfied.

Conclusion

Evaluation of the left ventricular function using HM 3DE is feasible, saves time, and is reproducible. To assess the left ventricular function, the border parameter setting of ED and ES = 70 and 30 provided the best fit for the Chinese population.

Feasibility and Accuracy of the Automated Software for Dynamic Quantification of Left Ventricular and Atrial Volumes and Function in a Large Unselected Population

We aimed to evaluate the feasibility and accuracy of machine learning-based automated dynamic quantification of left ventricular (LV) and left atrial (LA) volumes in an unselected population. We enrolled 600 unselected patients (12% in atrial fibrillation) clinically referred for transthoracic echocardiography (2DTTE), who also underwent 3D echocardiography (3DE) imaging. LV ejection fraction (EF), LV, and LA volumes were obtained from 2D images; 3D images were analyzed using dynamic heart model (DHM) software (Philips) resulting in LV and LA volume–time curves. A subgroup of 140 patients also underwent cardiac magnetic resonance (CMR) imaging. Average time of analysis, feasibility, and image quality were recorded, and results were compared between 2DTTE, DHM, and CMR. The use of DHM was feasible in 522/600 cases (87%). When feasible, the boundary position was considered accurate in 335/522 patients (64%), while major (n = 38) or minor (n = 149) border corrections were needed. The overall time required for DHM datasets was approximately 40 seconds. As expected, DHM LV volumes were larger than 2D ones (end-diastolic volume: 173 ± 64 vs. 142 ± 58 mL, respectively), while no differences were found for LV EF and LA volumes (EF: 55% ± 12 vs. 56% ± 14; LA volume 89 ± 36 vs. 89 ± 38 mL, respectively). The comparison between DHM and CMR values showed a high correlation for LV volumes (r = 0.70 and r = 0.82, p < 0.001 for end-diastolic and end-systolic volume, respectively) and an excellent correlation for EF (r = 0.82, p < 0.001) and LA volumes. The DHM software is feasible, accurate, and quick in a large series of unselected patients, including those with suboptimal 2D images or in atrial fibrillation.

Manual zur Indikation und Durchführung spezieller echokardiographischer Anwendungen

Das zweite Manual zur Indikation und Durchführung der Echokardiographie bezieht sich auf spezifische Anwendungen der Echokardiographie und besondere Fragestellungen bei speziellen Patientengruppen. Dabei stehen v. a. praktische Aspekte im Vordergrund. Methodisch etabliert sind die transösophageale Echokardiographie, die Stressechokardiographie und die Kontrastechokardiographie. Bei nahezu allen echokardiographischen Untersuchungen spielen aktuell 3‑D-Echokardiographie und Deformationsbildgebung eine Rolle. Das gesamte Spektrum der echokardiographischen Möglichkeiten wird derzeit in Notfall- und Intensivmedizin, bei der Überwachung und Führung von Katheterinterventionen, bei strukturellen Herzerkrankungen, bei herzchirurgischen Operationen, bei der Nachsorge von kardialen Unterstützungssystemen, bei kongenitalen Vitien im Erwachsenenalter und bei der Versorgung von hochinfektiösen Patienten in Pandemiezeiten angewandt. Die diagnostischen Fortschritte der konventionellen und modernen echokardiographischen Anwendungen stehen im Fokus dieses Manuals. Die 3‑D-Echokardiographie zur Charakterisierung der kardialen Morphologie und die Deformationsbildgebung zur Objektivierung der kardialen Funktion sind bei vielen Indikationen im klinischen Alltag etabliert. Die Stressechokardiographie zur Ischämie‑, Vitalitäts- und Vitiendiagnostik, die Bestimmung der koronaren Flussreserve und die Kontrastechokardiographie bei der linksventrikulären Wandbewegungsanalyse und kardialen Tumordetektion finden zunehmend klinische Anwendung. Wie für die konventionelle Echokardiographie im ersten Manual der Echokardiographie 2009 beschrieben, erfordert der Einsatz moderner echokardiographischer Verfahren die standardisierte Dokumentation und Akquisition bestimmter Bildsequenzen bei optimierter Geräteeinstellung, da korrekte und reproduzierbare Auswertungen nur bei guter Bildqualität möglich sind.

Deep learning-based automated left ventricular ejection fraction assessment using 2-D echocardiography

Deep learning (DL) has been applied for automatic left ventricle (LV) ejection fraction (EF) measurement, but the diagnostic performance was rarely evaluated for various phenotypes of heart disease. This study aims to evaluate a new DL algorithm for automated LVEF measurement using two-dimensional echocardiography (2DE) images collected from three centers. The impact of three ultrasound machines and three phenotypes of heart diseases on the automatic LVEF measurement was evaluated. Using 36890 frames of 2DE from 340 patients, we developed a DL algorithm based on U-Net (DPS-Net) and the biplane Simpson's method was applied for LVEF calculation. Results showed a high performance in LV segmentation and LVEF measurement across phenotypes and echo systems by using DPS-Net. Good performance was obtained for LV segmentation when DPS-Net was tested on the CAMUS data set (Dice coefficient of 0.932 and 0.928 for ED and ES). Better performance of LV segmentation in study-wise evaluation was observed by comparing the DPS-Net v2 to the EchoNet-dynamic algorithm (P = 0.008). DPS-Net was associated with high correlations and good agreements for the LVEF measurement. High diagnostic performance was obtained that the area under receiver operator characteristic curve was 0.974, 0.948, 0.968, and 0.972 for normal hearts and disease phenotypes including atrial fibrillation, hypertrophic cardiomyopathy, dilated cardiomyopathy, respectively. High performance was obtained by using DPS-Net in LV detection and LVEF measurement for heart failure with several phenotypes. High performance was observed in a large-scale dataset, suggesting that the DPS-Net was highly adaptive across different echocardiographic systems.NEW & NOTEWORTHY A new strategy of feature extraction and fusion could enhance the accuracy of automatic LVEF assessment based on multiview 2-D echocardiographic sequences. High diagnostic performance for the determination of heart failure was obtained by using DPS-Net in cases with different phenotypes of heart diseases. High performance for left ventricle segmentation was obtained by using DPS-Net, suggesting the potential for a wider range of application in the interpretation of 2DE images.

Prognostic value of the left ventricular - left atrial volume ratio assessed using three-dimensional echocardiography with fully automated analytical software

BACKGROUND

we investigated the prognostic value of a new 3D echocardiography (3DE) parameter, the left ventricular-left atrial volume ratio (LVLAVR) in a diverse group of subjects.

METHODS

3DE full-volume datasets were analyzed in 307 patients using fully automated LV and LA quantification software (Dynamic Heart Model, Philips Medical Systems, Andover, MA, USA), which generated LV and LA volume curves using artificial intelligence and 3D speckle tracking technology. We measured LVLAVR at LV end-diastole (edLVLAVR; LV end-diastolic volume / LA minimal volume), LVLAVR at LV end-systole (esLVLAVR: LV end-systolic volume / LA maximal volume), and their differences (ΔLVLAVR: edLVLAVR - esLVLAVR). No manual editing was performed on data of any patient. The primary endpoint was a major adverse cardiac event (MACE), including cardiac death, heart failure resulting in hospitalization, myocardial infarction, or ventricular tachyarrhythmia.

RESULTS

feasibility of LVLAVR measurements was 90%. During a median follow-up of 21 months, 43 patients developed a primary endpoint. Univariate Cox proportional hazard analysis revealed that edLVLAVR [hazard ratio (HR): 0.72, p < 0.01] and ΔLVLAVR (HR: 0.62, p < 0.01) were significantly associated with MACE. Median values of both edLVLAVR (4.59) and ΔLVLAVR (2.90) successfully stratified patients into high- and low-risk populations for future MACEs. ΔLVLAVR was still significantly associated with MACEs after adjusting for age, chronic kidney disease (CKD) and LV ejection fraction or after adjusting for age, CKD, and E/ε'.

CONCLUSIONS

LVLAVR provided incremental value over traditional LV systolic and diastolic function parameters to predict future adverse outcomes. The analysis was fully automated, thereby eliminating measurement variability.

Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use

Importance

Artificial intelligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acquisition of ultrasonography images is a novel area of investigation. A novel deep-learning (DL) algorithm, trained on more than 5 million examples of the outcome of ultrasonographic probe movement on image quality, can provide real-time prescriptive guidance for novice operators to obtain limited diagnostic transthoracic echocardiographic images.

Objective

To test whether novice users could obtain 10-view transthoracic echocardiographic studies of diagnostic quality using this DL-based software.

Design, Setting, and Participants

This prospective, multicenter diagnostic study was conducted in 2 academic hospitals. A cohort of 8 nurses who had not previously conducted echocardiograms was recruited and trained with AI. Each nurse scanned 30 patients aged at least 18 years who were scheduled to undergo a clinically indicated echocardiogram at Northwestern Memorial Hospital or Minneapolis Heart Institute between March and May 2019. These scans were compared with those of sonographers using the same echocardiographic hardware but without AI guidance.

Interventions

Each patient underwent paired limited echocardiograms: one from a nurse without prior echocardiography experience using the DL algorithm and the other from a sonographer without the DL algorithm. Five level 3-trained echocardiographers independently and blindly evaluated each acquisition.

Main Outcomes and Measures

Four primary end points were sequentially assessed: qualitative judgement about left ventricular size and function, right ventricular size, and the presence of a pericardial effusion. Secondary end points included 6 other clinical parameters and comparison of scans by nurses vs sonographers.

Results

A total of 240 patients (mean [SD] age, 61 [16] years old; 139 men [57.9%]; 79 [32.9%] with body mass indexes >30) completed the study. Eight nurses each scanned 30 patients using the DL algorithm, producing studies judged to be of diagnostic quality for left ventricular size, function, and pericardial effusion in 237 of 240 cases (98.8%) and right ventricular size in 222 of 240 cases (92.5%). For the secondary end points, nurse and sonographer scans were not significantly different for most parameters.

Conclusions and Relevance

This DL algorithm allows novices without experience in ultrasonography to obtain diagnostic transthoracic echocardiographic studies for evaluation of left ventricular size and function, right ventricular size, and presence of a nontrivial pericardial effusion, expanding the reach of echocardiography to clinical settings in which immediate interrogation of anatomy and cardiac function is needed and settings with limited resources.

Artificial Intelligence and Echocardiography

Artificial intelligence (AI) is evolving in the field of diagnostic medical imaging, including echocardiography. Although the dynamic nature of echocardiography presents challenges beyond those of static images from X-ray, computed tomography, magnetic resonance, and radioisotope imaging, AI has influenced all steps of echocardiography, from image acquisition to automatic measurement and interpretation. Considering that echocardiography often is affected by inter-observer variability and shows a strong dependence on the level of experience, AI could be extremely advantageous in minimizing observer variation and providing reproducible measures, enabling accurate diagnosis. Currently, most reported AI applications in echocardiographic measurement have focused on improved image acquisition and automation of repetitive and tedious tasks; however, the role of AI applications should not be limited to conventional processes. Rather, AI could provide clinically important insights from subtle and non-specific data, such as changes in myocardial texture in patients with myocardial disease. Recent initiatives to develop large echocardiographic databases can facilitate development of AI applications. The ultimate goal of applying AI to echocardiography is automation of the entire process of echocardiogram analysis. Once automatic analysis becomes reliable, workflows in clinical echocardiographic will change radically. The human expert will remain the master controlling the overall diagnostic process, will not be replaced by AI, and will obtain significant support from AI systems to guide acquisition, perform measurements, and integrate and compare data on request.

[Role of the cardiologist in the management of oncology patients. Where are we standing, and what to expect in the future?]

Cardiovascular and oncological diseases are the main causes of death worldwide. Cancer patients have an increased risk of cardiovascular diseases but, at the same time, cardiovascular patients experience a higher risk of cancer. This relationship goes beyond the toxicity concerning cancer treatment. Cardio-oncology goal is to facilitate cancer therapy by implementing preventive strategies that allow early diagnosis and treatment of potential cancer therapy-induced cardiovascular complications, being heart failure the most fearest one. The creation of Cardio-oncology services has the potential to impact daily clinical practice and public health, with clear implications into the future.

Routine Echocardiography and Artificial Intelligence Solutions

Introduction: Echocardiography is widely used because of its portability, high temporal resolution, absence of radiation, and due to the low-costs. Over the past years, echocardiography has been recommended by the European Society of Cardiology in most cardiac diseases for both diagnostic and prognostic purposes. These recommendations have led to an increase in number of performed studies each requiring diligent processing and reviewing. The standard work pattern of image analysis including quantification and reporting has become highly resource intensive and time consuming. Existence of a large number of datasets with digital echocardiography images and recent advent of AI technology have created an environment in which artificial intelligence (AI) solutions can be developed successfully to automate current manual workflow. Methods and Results: We report on published AI solutions for echocardiography analysis on methods' performance, characteristics of the used data and imaged population. Contemporary AI applications are available for automation and advent in the image acquisition, analysis, reporting and education. AI solutions have been developed for both diagnostic and predictive tasks in echocardiography. Left ventricular function assessment and quantification have been most often performed. Performance of automated image view classification, image quality enhancement, cardiac function assessment, disease classification, and cardiac event prediction was overall good but most studies lack external evaluation. Conclusion: Contemporary AI solutions for image acquisition, analysis, reporting and education are developed for relevant tasks with promising performance. In the future major benefit of AI in echocardiography is expected from improvements in automated analysis and interpretation to reduce workload and improve clinical outcome. Some of the challenges have yet to be overcome, however, none of them are insurmountable.

Artificial Intelligence and Machine Learning in Cardiovascular Imaging

Cardiovascular disease is the leading cause of mortality in Western countries and leads to a spectrum of complications that can complicate patient management. The emergence of artificial intelligence (AI) has garnered significant interest in many industries, and the field of cardiovascular imaging is no exception. Machine learning (ML) especially is showing significant promise in various diagnostic imaging modalities. As conventional statistics are reaching their apex in computational capabilities, ML can explore new possibilities and unravel hidden relationships. This will have a positive impact on diagnosis and prognosis for cardiovascular imaging. In this in-depth review, we highlight the role of AI and ML for various cardiovascular imaging modalities.

Novelties in 3D Transthoracic Echocardiography

Cardiovascular imaging is developing at a rapid pace and the newer modalities, in particular three-dimensional echocardiography, allow better analysis of heart structures. Identifying valve lesions and grading their severity represents crucial information and nowadays is strengthened by the introduction of new software, such as transillumination, which provide detailed morphology descriptions. Chambers quantification has never been so rapid and accurate: machine learning algorithms generate automated volume measurements, including left ventricular systolic and diastolic function, which is extremely important for clinical decisions. This review provides an overview of the latest innovations in the echocardiography field, and is helpful by providing a better insight into heart diseases.

Narrative review of the role of artificial intelligence to improve aortic valve disease management

Valvular heart disease (VHD) is a chronic progressive condition with an increasing prevalence in the Western world due to aging populations. VHD is often diagnosed at a late stage when patients are symptomatic and the outcomes of therapy, including valve replacement, may be sub-optimal due the development of secondary complications, including left ventricular (LV) dysfunction. The clinical application of artificial intelligence (AI), including machine learning (ML), has promise in supporting not only early and more timely diagnosis, but also hastening patient referral and ensuring optimal treatment of VHD. As physician auscultation lacks accuracy in diagnosis of significant VHD, computer-aided auscultation (CAA) with the help of a commercially available digital stethoscopes improves the detection and classification of heart murmurs. Although used little in current clinical practice, CAA can screen large populations at low cost with high accuracy for VHD and faciliate appropriate patient referral. Echocardiography remains the next step in assessment and planning management and AI is delivering major changes in speeding training, improving image quality by pattern recognition and image sorting, as well as automated measurement of multiple variables, thereby improving accuracy. Furthermore, AI then has the potential to hasten patient disposal, by automated alerts for red-flag findings, as well as decision support in dealing with results. In management, there is great potential in ML-enabled tools to support comprehensive disease monitoring and individualized treatment decisions. Using data from multiple sources, including demographic and clinical risk data to image variables and electronic reports from electronic medical records, specific patient phenotypes may be identified that are associated with greater risk or modeled to the estimate trajectory of VHD progression. Finally, AI algorithms are of proven value in planning intervention, facilitating transcatheter valve replacement by automated measurements of anatomical dimensions derived from imaging data to improve valve selection, valve size and method of delivery.

Integration of three-dimensional echocardiography into the modern-day echo laboratory

Three-dimensional echocardiography (3DE) has emerged in recent decades from a conceptual, research tool to an important, useful imaging technique that can informatively impact daily clinical practice. However, its adoption into the modern-day echo laboratory requires the acknowledgment of its value, coupled with proper leadership, education, and resources to implement and integrate its use with conventional echo techniques. 3DE integration involves important updates regarding equipment and patient selection, assimilation of 3D protocols into current clinical routine, laboratory workflow adaptation, storage, and reporting. This review will provide a practical blueprint and key points of how to integrate 3DE into today's echo laboratory, necessary resources to implement 3D workflow, logistical challenges that remain, and future directions to further improve assimilation of this relevant echo technique into the laboratory.

Feasibility and Reproducibility of Left Atrium Measurements Using Different Three-Dimensional Echocardiographic Modalities

Left atrium (LA) volume is a biomarker of cardiovascular outcomes. Three-dimensional echocardiography (3DE) provides an accurate LA evaluation, but data regarding the optimal 3DE method is scarce. We assessed the feasibility and reproducibility of LA measurements using different 3DE methods. One hundred and ninety-four patients were prospectively analyzed. Conventional 3DE and two semi-automatic 3DE algorithms (Tomtec™ and Dynamic Heart Model (DHM)) were used in 110 patients. Intra- and interobserver reproducibility and intervendor comparison were performed in additional patients' subsets. Forty patients underwent cardiac magnetic resonance (CMR). Feasibility was 100% for Tomtec, 98.2% for DHM, and 72.8% for conventional 3DE. Tomtec volumes were higher than 3DE and DHM (p < 0.001). Reproducibility was better for DHM (intraobserver LA maximum volume (LAmax) ICC 0.99 (95% CI 1.0-0.99), LA minimum volume (LAmin) 0.98 (95% CI 0.95-0.99), LApreA 0.96 (95% CI 0.91-0.98); interobserver LAmax ICC 0.98 (95% CI 0.96-0.99), LAmin 0.99 (95% CI 0.99-1.00), and LApreA 0.97 (95% CI 0.94-0.99)). Intervendor comparison showed differences between left ventricle (LV) software adapted for LA (p < 0.001). Tomtec underestimated the least LA volumes compared to CMR. These findings emphasize that dedicated software should be used for LA assessment, for consistent clinical longitudinal follow-up and research.

A comparison of artificial intelligence-based algorithms for the identification of patients with depressed right ventricular function from 2-dimentional echocardiography parameters and clinical features

Background

Recognizing low right ventricular (RV) function from 2-dimentiontial echocardiography (2D-ECHO) is challenging when parameters are contradictory. We aim to develop a model to predict low RV function integrating the various 2D-ECHO parameters in reference to cardiac magnetic resonance (CMR)-the gold standard.

Methods

We retrospectively identified patients who underwent a 2D-ECHO and a CMR within 3 months of each other at our institution (American University of Beirut Medical Center). We extracted three parameters (TAPSE, S' and FAC_RV) that are classically used to assess RV function. We have assessed the ability of 2D-ECHO derived parameters and clinical features to predict RV function measured by the gold standard CMR. We compared outcomes from four machine learning algorithms, widely used in the biomedical community to solve classification problems.

Results

One hundred fifty-five patients were identified and included in our study. Average age was 43±17.1 years old and 52/156 (33.3%) were females. According to CMR, 21 patients were identified to have RV dysfunction, with an RVEF of 34.7%±6.4%, as opposed to 54.7%±6.7% in the normal RV population (P<0.0001). The Random Forest model was able to detect low RV function with an AUC =0.80, while general linear regression performed poorly in our population with an AUC of 0.62.

Conclusions

In this study, we trained and validated an ML-based algorithm that could detect low RV function from clinical and 2D-ECHO parameters. The algorithm has two advantages: first, it performed better than general linear regression, and second, it integrated the various 2D-ECHO parameters.

Three-dimensional echocardiographic acquisition and validity of left ventricular volumes and ejection fraction

Transthoracic (TTE) and transesophageal (TEE) three-dimensional echocardiography (3DE) is now used in daily clinical practice. Advancements in technology have improved image acquisition with higher frame rates and increased resolution. Different 3DE acquisition techniques can be used depending upon the structure of interest and if volumetric analysis is required. Measurements of left ventricular (LV) volumes are the most common use of 3DE clinically but are highly dependent upon image quality. Three-dimensional LV function analysis has been made easier with the development of automated software, which has been found to be highly reproducible. However, further research is needed to develop normal reference range values of LV function for both 3D TTE and TEE.

Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance

Cardiovascular disease (CVD), despite the significant advances in the diagnosis and treatments, still represents the leading cause of morbidity and mortality worldwide. In order to improve and optimize CVD outcomes, artificial intelligence techniques have the potential to radically change the way we practice cardiology, especially in imaging, offering us novel tools to interpret data and make clinical decisions. AI techniques such as machine learning and deep learning can also improve medical knowledge due to the increase of the volume and complexity of the data, unlocking clinically relevant information. Likewise, the use of emerging communication and information technologies is becoming pivotal to create a pervasive healthcare service through which elderly and chronic disease patients can receive medical care at their home, reducing hospitalizations and improving quality of life. The aim of this review is to describe the contemporary state of artificial intelligence and digital health applied to cardiovascular medicine as well as to provide physicians with their potential not only in cardiac imaging but most of all in clinical practice.

Artificial Intelligence and Echocardiography: A Primer for Cardiac Sonographers

Artificial intelligence (AI) is emerging as a key component in diagnostic medical imaging, including echocardiography. AI with deep learning has already been used with automated view labeling, measurements, and interpretation. As the development and use of AI in echocardiography increase, potential concerns may be raised by cardiac sonographers and the profession. This report, from a sonographer's perspective, focuses on defining AI, the basics of the technology, identifying some current applications of AI, and how the use of AI may improve patient care in the future.

•

AI will have a strong role in echocardiography.

•

AI will guide image acquisition and optimization.

•

AI for image analysis may aid in interpretation.

•

AI is a tool that will not replace sonographers but will help them be more efficient.