Machine Learning–Based Three-Dimensional Echocardiographic Quantification of Right Ventricular Size and Function: Validation Against Cardiac Magnetic Resonance

Evolving perspectives on aortic stenosis: the increasing importance of evaluating the right ventricle before aortic valve intervention

Aortic stenosis (AS) was historically considered a disease of the left side of the heart, with the main pathophysiological impact being predominantly on the left ventricle (LV). However, progressive pressure overload in AS can initiate a cascade of extra-valvular myocardial remodeling that could also precipitate maladaptive alterations in the structure and function of the right ventricle (RV). The haemodynamic and clinical importance of these changes in patients with AS have been largely underappreciated in the past. Contemporary data indicates that RV dilatation or impairment identifies the AS patients who are at increased risk of adverse clinical outcomes after aortic valve replacement (AVR). It is now increasingly recognised that effective quantitative assessment of the RV plays a key role in delineating the late clinical stage of AS, which could improve patient risk stratification. Despite the increasing emphasis on the pathological significance of RV changes in AS, it remains to be established if earlier detection of these changes can improve the timing for intervention. This review will summarise the features of normal RV physiology and the mechanisms responsible for RV impairment in AS. In addition, we will discuss the multimodality approach to the comprehensive assessment of RV size, function and mechanics in AS patients. Finally, we will review the emerging evidence reinforcing the negative impact of RV dysfunction on clinical outcomes in AS patients treated with AVR.

RETRACTED ARTICLE: Short-term memory neural network-based cognitive computing in sports training complexity pattern recognition

With the development of Chinese sports, many sports training researchers try to use artificial intelligence technology to study the training methods and training elements of athletes. However, in reality, these methods are often based on different basic training principles, resulting in the reduction in the generalization ability of artificial intelligence networks. This paper studies the complexity of sports training principles by using an artificial intelligence network model. Based on the improved model of dropout optimization algorithm, this paper proposes an artificial intelligence sports training node prediction method based on the combination of dropout optimization algorithm and short-term memory neural network (LSTM), which avoids the establishment of complex sports training models. Based on artificial intelligence operation and maintenance records and sports training core capacity experimental data, the maximum node static estimation of artificial intelligence sports training is realized. The research shows that the node prediction model is established by using the method described in this paper. Through experimental comparison and analysis, the model has high prediction accuracy. Due to the state memory function of LSTM, it has advantages in the prediction of 2000 data on a long time scale. The mean absolute error percentage of the prediction results is less than 3.4%, and the maximum absolute error percentage is less than 5.2%. The artificial intelligence network model in this paper has good generalization ability. Compared with other models, the model proposed in this paper can get more accurate prediction results in sports training of different groups and effectively alleviate the problem of overfitting. Therefore, traditional stadiums and gymnasiums should actively introduce artificial intelligence technology with a more positive attitude, to realize the development and innovation in technology application, service innovation, management efficiency, and function integration.

Automated Evaluation for Pericardial Effusion and Cardiac Tamponade with Echocardiographic Artificial Intelligence

Background

Timely and accurate detection of pericardial effusion and assessment cardiac tamponade remain challenging and highly operator dependent.

Objectives

Artificial intelligence has advanced many echocardiographic assessments, and we aimed to develop and validate a deep learning model to automate the assessment of pericardial effusion severity and cardiac tamponade from echocardiogram videos.

Methods

We developed a deep learning model (EchoNet-Pericardium) using temporal-spatial convolutional neural networks to automate pericardial effusion severity grading and tamponade detection from echocardiography videos. The model was trained using a retrospective dataset of 1,427,660 videos from 85,380 echocardiograms at Cedars-Sinai Medical Center (CSMC) to predict PE severity and cardiac tamponade across individual echocardiographic views and an ensemble approach combining predictions from five standard views. External validation was performed on 33,310 videos from 1,806 echocardiograms from Stanford Healthcare (SHC).

Results

In the held out CSMC test set, EchoNet-Pericardium achieved an AUC of 0.900 (95% CI: 0.884-0.916) for detecting moderate or larger pericardial effusion, 0.942 (95% CI: 0.917-0.964) for large pericardial effusion, and 0.955 (95% CI: 0.939-0.968) for cardiac tamponade. In the SHC external validation cohort, the model achieved AUCs of 0.869 (95% CI: 0.794-0.933) for moderate or larger pericardial effusion, 0.959 (95% CI: 0.945-0.972) for large pericardial effusion, and 0.966 (95% CI: 0.906-0.995) for cardiac tamponade. Subgroup analysis demonstrated consistent performance across ages, sexes, left ventricular ejection fraction, and atrial fibrillation statuses.

Conclusions

Our deep learning-based framework accurately grades pericardial effusion severity and detects cardiac tamponade from echocardiograms, demonstrating consistent performance and generalizability across different cohorts. This automated tool has the potential to enhance clinical decision-making by reducing operator dependence and expediting diagnosis.

Prospective clinical evaluation of deep learning for ultrasonographic screening of abdominal aortic aneurysms

Abdominal aortic aneurysm (AAA) often remains undetected until rupture due to limited access to diagnostic ultrasound. This trial evaluated a deep learning (DL) algorithm to guide AAA screening by novice nurses with no prior ultrasonography experience. Ten nurses performed 15 scans each on patients over 65, assisted by a DL object detection algorithm, and compared against physician-performed scans. Ultrasound scan quality, assessed by three blinded expert physicians, was the primary outcome. Among 184 patients, DL-guided novices achieved adequate scan quality in 87.5% of cases, comparable to the 91.3% by physicians (p = 0.310). The DL model predicted AAA with an AUC of 0.975, 100% sensitivity, and 97.8% specificity, with a mean absolute error of 2.8 mm in predicting aortic width compared to physicians. This study demonstrates that DL-guided POCUS has the potential to democratize AAA screening, offering performance comparable to experienced physicians and improving early detection.

Noninvasive Estimation of Right Atrial, Right Ventricular, and Pulmonary Systolic Pressure: "A Good Story Never Ends"

The precise evaluation of pulmonary artery, right atrial, and ventricular pressures is essential for the diagnosis, ongoing management, and therapeutic decision-making in a wide range of cardiovascular conditions, including pulmonary hypertension. Since the early 1980s, the accuracy and consistency of echocardiography in estimating pulmonary artery pressure have been a subject of debate, with multiple formulas developed over time to improve reliability. Despite initial concerns, echocardiography has now been widely accepted as a noninvasive, safe, and readily available alternative to the more invasive right heart catheterization, which remains the gold standard. The growing recognition of echocardiography's role in clinical practice has led to significant advancements in its methodology. This review explores the contribution of echo-Doppler techniques to the assessment of right heart hemodynamics, highlighting their importance in daily practice. It also examines the historical milestones that have facilitated the standardization of various formulas and paved the way for the development of current guidelines. By tracing these developments, the review underscores the relevance of echocardiography in modern cardiology and the importance of continuing to refine its application to ensure accurate and reliable assessments.

Accuracy of Shunt Volume Measured by Three-Dimensional Echocardiography and Cardiac Magnetic Resonance in Patients With an Atrial Septal Defect and a Dilated Right Ventricle

BACKGROUND

The accuracy of right ventricular (RV) quantification by three-dimensional echocardiography (3DE) has been reported mainly in patients with a normal right ventricle (RV). However, there are no data regarding the accuracy of 3DE in patients with a dilated RV, as in shunt diseases. In this study, we evaluated the accuracy of 3DE and that of volumetric (Vol) cardiac magnetic resonance (CMR) for assessment of RV and left ventricular (LV) stroke volume (SV) and the pulmonary (Qp)/systemic (Qs) blood flow ratio in patients with an atrial septal defect (ASD) using the two-dimensional phase contrast (2DPC) method as the gold standard.

METHODS

We retrospectively investigated 83 patients with ASD who underwent transcatheter closure and clinically indicated CMR and 3DE examinations. The ratio Qp/Qs was calculated using RV and LV SV measured by full-volume volumetric 3DE (Vol-3DE) and CMR (Vol-CMR) and by two-dimensional pulsed Doppler quantification (2D-Dop); the parameters were compared using 2DPC-CMR as the gold standard.

RESULTS

There was no significant difference in the Qp/Qs value between 2DPC-CMR and Vol-3DE (2.29 ± 0.70 vs 2.21 ± 0.63, P = .79) and 2D-Dop (vs 2.21 ± 0.65, P = 1.00); however, a significant difference was found between 2DPC-CMR and Vol-CMR (P < .001). The Qp/Qs value obtained using Vol-3DE showed the best correlation with 2DPC-CMR (r = 0.93, P < .001). The RV and LV SV values obtained by Vol-3DE showed the best correlation with 2DPC-CMR (RV SV, r = 0.82, P < .001; LV SV, r = 0.73, P < .001), although the absolute values were underestimated.

CONCLUSION

Qp/Qs was more accurately evaluated by Vol-3DE than by Vol-CMR or 2D-Dop. Three-dimensional echocardiography assessment was feasible and reproducible even in a dilated RV.

Artificial intelligence: The future for multimodality imaging of right ventricle

The crucial pathophysiological and prognostic roles of the right ventricle in various diseases have been well-established. Nonetheless, conventional cardiovascular imaging modalities are frequently associated with intrinsic limitations when evaluating right ventricular (RV) morphology and function. The integration of artificial intelligence (AI) in multimodality imaging presents a promising avenue to circumvent these obstacles, paving the way for future fully automated imaging paradigms. This review aimed to address the current challenges faced by clinicians and researchers in integrating RV imaging and AI technology, to provide a comprehensive overview of the current applications of AI in RV imaging, and to offer insights into future directions, opportunities, and potential challenges in this rapidly advancing field.

Artificial intelligence to analyze magnetic resonance imaging in rheumatology

Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a means to improve and advance MRI analysis. This review examines current AI applications in rheumatology MRI analysis, addressing diagnostic support, disease classification, activity assessment, and progression monitoring. AI demonstrates promise, with high sensitivity, specificity, and accuracy, achieving or surpassing expert performance. The review also discusses clinical implementation challenges and future research directions to enhance rheumatic disease diagnosis and management.

Role and application of three-dimensional transthoracic echocardiography in the assessment of left and right ventricular volumes and ejection fraction: a UK nationwide survey

Three-dimensional echocardiography (3DE) imaging has permitted advancements in the quantification of left ventricular (LV) and right ventricular (RV) volumes and ejection fraction. We evaluated the availability of 3DE equipment / analysis software, the integration of 3DE assessment of the LV and RV in routine clinical practice, current training provisions in 3DE, and aimed to ascertain barriers preventing the routine use of 3DE for volumetric analysis. Through the British Society of Echocardiography (BSE) regional representatives' network, echocardiographers were invited to participate in an open online survey. A total of 181 participants from echocardiography departments in the United Kingdom (UK), the majority from tertiary centres (61%), completed the 28-question survey. For 3DE quantification, 3DE-LV was adopted more frequently than 3DE-RV (48% vs 11%, respectively). Imaging feasibility was a recognised factor in 3DE RV and LV adoption. Many respondents had access to 3D probes (93%). The largest observed barriers to 3DE routine use were training deficiencies, with 83% reporting they would benefit from additional training opportunities and the duration of time permitted for the scan, with 68% of responders reporting allowances of less than the BSE standard of 45-60 min per patient (8% < 30-min). Furthermore, of those respondents who had undertaken professional accreditation, competence in 3DE was not formally assessed in 89%. This UK survey also reported good accessibility to magnetic resonance imaging (72%), which was related to overall 3DE adoption. In summary, although 3DE is now readily available, it remains underutilised. Further training opportunities, integrated formal assessment, improved adoption of BSE minimum recommended scanning times, alongside industry and societal support, may increase 3DE utilisation in routine practice.

Added value of 3D echocardiography in the diagnosis and prognostication of patients with right ventricular dysfunction

Recent inroads into percutaneous-based options for the treatment of tricuspid valve disease has brought to light how little we know about the behavior of the right ventricle in both health and disease and how incomplete our assessment of right ventricular (RV) physiology and function is using current non-invasive technology, in particular echocardiography. The purpose of this review is to provide an overview of what three-dimensional echocardiography (3DE) can offer currently to enhance RV evaluation and what the future may hold if we continue to improve the 3D evaluation of the right heart.

Multiparametric assessment of right ventricular function in heart transplant recipients by echocardiography and relations with pulmonary hemodynamics

OBJECTIVE

Right ventricular (RV) dilatation and dysfunction are usually present in heart transplant (HTx) patients and worsened with residual pulmonary hypertension (PH). We aimed to determine the ability of different echocardiographic modalities to evaluate RV function in comparison with cardiac magnetic resonance (CMR) and their relations with pulmonary hemodynamics in HTx patients.

METHODS

A total of 62 data sets [echocardiographic, hemodynamic, and CMR] were acquired from 35 HTx patients. Comprehensive echocardiography, including two-dimensional (2D) transthoracic echocardiography, speckle tracking echocardiography, and three-dimensional (3D) echocardiography, was performed. Mean pulmonary artery pressure (mPAP) was obtained invasively from right heart catheterization. The correlations between all echocardiographic parameters and CMR imaging data and the differences between patients with and without residual PH were evaluated.

RESULTS

Diastolic and systolic RV volumes and RV ejection fraction (RVEF) by 3D echocardiography correlated strongly with CMR-derived volumes and RVEF (r = .91, r = .79, r = .64; p < .0001 for each, respectively). Among other parameters, RV fractional area change (r = .439; p < .001) and RV free wall longitudinal strain (RVFW-LS) (r = -.34; p < .05) correlated moderately with CMR-RVEF, whereas tricuspid annulus S' velocity (r = .29; p < .05) and tricuspid annular systolic plane excursion (r = .27; p < .05) correlated weakly with CMR-RVEF. Additionally, 3D-RVEF and RVFW-LS were significantly decreased in studies with mPAP ≥ 20 mm Hg in comparison to those with mPAP < 20 mm Hg (47.7 ± 3.7 vs. 50.9 ± 5.3, p = .04 and -15.5 ± 3.1 vs. -17.5 ± 3, p = .03, respectively).

CONCLUSION

The best method for the evaluation of RV function in HTx recipients is 3D echocardiography. Besides, the subclinical impact of residual PH on RV function can be best determined by RVFW-LS and 3D-RVEF in these patients.

Assessment of mitral valve geometry in nonvalvular atrial fibrillation patients with or without ventricular dysfunction: insights from high volume rate three-dimensional transesophageal echocardiography

Meticulous understanding of the mechanisms underpinning mitral regurgitation in atrial fibrillation (AF) patients is crucial to optimize therapeutic strategies. The morphologic characteristics of mitral valves in atrial functional mitral regurgitation (FMR) patients with and without left ventricular (LV) dysfunction were evaluated by high volume rate (HVR) three-dimensional transesophageal echocardiography (3D-TEE). In our study, 68 of 265 AF patients who underwent 3D-TEE were selected, including 36 patients with AF, FMR, and preserved LV function (AFMR group) and 32 patients with AF, FMR, and LV dysfunction (VFMR group). In addition, 36 fever patients without heart disease were included in the control group. Group comparisons were performed by one-way analysis of variance for continuous variables. The left atrium (LA) was enlarged in the AFMR and VFMR groups compared with the control group. The mitral annulus (MA) in the AFMR group was enlarged and flattened compared with the control group and was smaller than in the VFMR group. The annulus area fraction was significantly diminished in the AFMR and VFMR groups, indicative of reduced MA contractility. The posterior mitral leaflet (PML) angle was smallest in the AFMR group and largest in the control group, whereas the distal anterior mitral leaflet angle did not significantly differ among the three groups. LA remodeling causes expansion of the MA and reduced MA contractility, disruption of the annular saddle shape, and atriogenic PML tethering. Comparison of atrial FMR patients with and without LV dysfunction indicates that atriogenic PML tethering is an important factor that aggravates FMR. HVR 3D-TEE improves the 3D temporal resolution greatly.

Three-Dimensional, Right Ventricular Surface Strain Computation From Three-Dimensional Echocardiographic Images From Patients With Pediatric Pulmonary Hypertension

Right Ventricular (RV) dysfunction is routinely assessed with echocardiographic-derived global longitudinal strain (GLS). GLS is measured from a two-dimensional echo image and is increasingly accepted as a means for assessing RV function. However, any two-dimensional (2D) analysis cannot visualize the asymmetrical deformation of the RV nor visualize strain over the entire RV surface. We believe three-dimensional surface (3DS) strain, obtained from 3D echo will better evaluate myocardial mechanics. Components of 3DS strain (longitudinal, LS; circumferential, CS; longitudinal-circumferential shear, ɣCL; principal strains PSMax and PSMin; max shear, ɣMax; and principal angle θMax) were computed from RV surface meshes obtained with 3D echo from 50 children with associated pulmonary arterial hypertension (PAH), 43 children with idiopathic PAH, and 50 healthy children by computing strains from a discretized displacement field. All 3DS freewall (FW) normal strain (LS, CS, PSMax, and PSMin) showed significant decline at end-systole in PH groups (p < 0.0001 for all), as did FW-ɣMax (p = 0.0012). FW-θMax also changed in disease (p < 0.0001). Limits of agreement analysis suggest that 3DS LS, PSMax, and PSMin are related to GLS. 3DS strains showed significant heterogeneity over the 3D surface of the RV. Components of 3DS strain agree with existing clinical strain measures, well classify normal -versus- PAH subjects, and suggest that strains change direction on the myocardial surface due to disease. This last finding is similar to that of myocardial fiber realignment in disease, but further work is needed to establish true associations.

Emerging Roles for Artificial Intelligence in Heart Failure Imaging

Artificial intelligence (AI) applications are expanding in cardiac imaging. AI research has shown promise in workflow optimization, disease diagnosis, and integration of clinical and imaging data to predict patient outcomes. The diagnostic and prognostic paradigm of heart failure is heavily reliant on cardiac imaging. As AI becomes increasingly validated and integrated into clinical practice, AI influence on heart failure management will grow. This review discusses areas of current research and potential clinical applications in AI as applied to heart failure cardiac imaging.

Right ventricular ejection fraction derived from intraoperative three-dimensional transesophageal echocardiography versus cardiac magnetic resonance imaging

PURPOSE

Right ventricle (RV) assessment is critical during cardiac surgery. Traditional assessment consists of visual estimation and measurement of validated parameters. Cardiac magnetic resonance imaging (cMRI) is the gold standard for RV analysis, and transthoracic three-dimensional (3D) echocardiography is validated against this. We aimed to show that intraoperative 3D transesophageal echocardiography (TEE) RV assessment is feasible and can produce results that correlate with cMRI.

METHODS

We recruited cardiac surgery patients who underwent cMRI within the preceding twelve preoperative months. An anesthetic protocol was followed pre-sternotomy and a 3D RV data set was acquired. We used TOMTEC 4D RV-Function to derive RV end-diastolic volume (EDV), end-systolic volume (ESV), and ejection fraction (EF). We compared these data with the corresponding MRI values.

RESULTS

Twenty-five patients were included. Transesophageal echocardiography EDV and ESV differed from MRI measurements with a mean bias of -53 mL (95% confidence interval [CI], -80 to 26) and -21 mL (95% CI, -34 to -9). Transesophageal echocardiography EF did not differ significantly, with a mean bias of -4% (95% CI, -8 to 1). Results were unchanged after excluding MRIs older than 180 days. Correlation coefficients for EDV, ESV, and EF were r = 0.85, 0.91, and 0.80, respectively. Interclass correlation coefficients for EDV, ESV, and EF were 0.86, 0.89, and 0.96, respectively.

CONCLUSIONS

Intraoperative TEE RV, EDV, and ESV are underestimated relative to cMRI because of analysis, anesthetic, and ventilation factors. The EF showed a low mean difference, and all values showed strong correlation with MRI. Reproducibility and feasibility were excellent and increased use in clinical practice should be considered.

Current national availability of advanced echocardiography imaging: real world data from an Italian Society of Echocardiography and Cardiovascular Imaging survey

Aims

Advanced echocardiographic imaging (AEI) techniques, such as three-dimensional (3D) and multi-chamber speckle-tracking deformation imaging (strain) analysis, have been shown to be more accurate in assessing heart chamber geometry and function when compared with conventional echocardiography providing additional prognostic value. However, incorporating AEI alongside standard examinations may be heterogeneous between echo laboratories (echo labs). Thus, our goal was to gain a better understanding of the many AEI modalities that are available and employed in Italy.

Methods and results

The Italian Society of Echocardiography and Cardiovascular Imaging (SIECVI) conducted a national survey over a month (November 2022) to describe the use of AEI in Italy. Data were retrieved via an electronic survey based on a structured questionnaire uploaded on the SIECVI website. Data obtained from 173 echo labs were divided into 3 groups, according to the numbers of echocardiograms performed: <250 exams (low-volume activity, 53 centres), between 251 and 550 exams (moderate-volume activity, 62 centres), and ≥550 exams (high-volume activity, 58 centres). Transthoracic echocardiography (TTE) 3D was in use in 75% of centres with a consistent difference between low (55%), medium (71%), and high activity volume (85%) (P = 0.002), while 3D transoesophageal echocardiography (TEE) was in use in 84% of centres, reaching the 95% in high activity volume echo labs (P = 0.006). In centres with available 3D TTE, it was used for the left ventricle (LV) analysis in 67%, for the right ventricle (RV) in 45%, and for the left atrium (LA) in 40%, showing greater use in high-volume centres compared with low- and medium-volume centres (all P < 0.04). Strain analysis was utilized in most echo labs (80%), with a trend towards greater use in high-volume centres than low- and medium-volume centres (77%, 74%, and 90%, respectively; P = 0.08). In centres with available strain analysis, it was mainly employed for the LV (80%) and much less frequently for the RV and LA (49% and 48%, respectively).

Conclusion

In Italy, the AEI modalities are more frequently available in centres with high-volume activity but employed only in a few applications, being more frequent in analysing the LV compared with the RV and LA. Therefore, the echocardiography community and SIECVI should promote uniformity and effective training across the Italian centres. Meanwhile, collaborations across centres with various resources and expertise should be encouraged to use the benefits of the AEI.

Validation of American Society of Echocardiography Guideline-Recommended Parameters of Right Ventricular Dysfunction Using Artificial Intelligence Compared With Cardiac Magnetic Resonance Imaging

BACKGROUND

Right ventricular (RV) function is important in the evaluation of cardiac function, but its assessment using standard transthoracic echocardiography (TTE) remains challenging. Cardiac magnetic resonance imaging (CMR) is considered the gold standard. The American Society of Echocardiography recommends surrogate measures of RV function and RV ejection fraction (RVEF) by TTE, including fractional area change (FAC), free wall strain (FWS), and tricuspid annular planar systolic excursion (TAPSE), but they require technical expertise in acquisition and quantification.

METHODS

The aim of this study was to evaluate the sensitivity, specificity, and positive and negative predictive values of FAC, FWS, and TAPSE derived using a rapid, novel artificial intelligence (AI) software (LVivoRV) from a single-plane transthoracic echocardiographic apical four-chamber, RV-focused view without ultrasound-enhancing agents for detecting abnormal RV function compared with CMR-derived RVEF. RV dysfunction was defined as RVEF < 50% and RVEF < 40% on CMR.

RESULTS

TTE and CMR were performed within a median of 10 days (interquartile range, 2-32 days) of each other in 225 consecutive patients without interval procedural or pharmacologic intervention. The sensitivity and negative predictive value to detect CMR-defined RV dysfunction when all three AI-derived parameters (FAC, FWS, and TAPSE) were abnormal were 91% and 96%, while those of expert physician reads were 91% and 97%. Specificity and positive predictive value were lower (50% and 32%) compared with expert physician-read echocardiograms (82% and 56%).

CONCLUSIONS

AI-derived measurements of FAC, FWS, and TAPSE had excellent sensitivity and negative predictive value for ruling out significant RV dysfunction (CMR RVEF < 40%), comparable with that of expert physician readers, but lower specificity. Thus AI, using American Society of Echocardiography guidelines, may serve as a useful screening tool for rapid bedside assessment to exclude significant RV dysfunction.

Echocardiography Imaging of the Right Ventricle: Focus on Three-Dimensional Echocardiography

Right ventricular function strongly predicts cardiac death and adverse cardiac events in patients with cardiac diseases. However, the accurate right ventricular assessment by two-dimensional echocardiography is limited due to its complex anatomy, shape, and load dependence. Advances in cardiac imaging and three-dimensional echocardiography provided more reliable information on right ventricular volumes and function without geometrical assumptions. Furthermore, the pathophysiology of right ventricular dysfunction and tricuspid regurgitation is frequently connected. Three-dimensional echocardiography allows a more in-depth structural and functional evaluation of the tricuspid valve. Understanding the anatomy and pathophysiology of the right side of the heart may help in diagnosing and managing the disease by using reliable imaging tools. The present review describes the challenging echocardiographic assessment of the right ventricle and tricuspid valve apparatus in clinical practice with a focus on three-dimensional echocardiography.

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.

Right ventricular dysfunction in patients with acute respiratory distress syndrome receiving venovenous extracorporeal membrane oxygenation

Acute respiratory distress syndrome is characterized by non-cardiogenic pulmonary edema, decreased pulmonary compliance, and abnormalities in gas exchange, especially hypoxemia. Patients with acute respiratory distress syndrome (ARDS) who receive support with venovenous (V-V) extracorporeal membrane oxygenation (ECMO) usually have severe lung disease. Many patients with ARDS have associated pulmonary vascular injury which can result in elevated pulmonary vascular resistance and right heart dysfunction. Since V-V ECMO relies upon preserved cardiac function, right heart failure has important implications for patient evaluation, management, and outcomes. Worsening right heart function complicates ARDS and disease processes. Given the increasing use of ECMO to support patients with ARDS, an understanding of right ventricular-ECMO and cardiopulmonary interactions is essential for the clinician. A narrative review of the manifestations of right heart dysfunction, as well as diagnosis and management strategies for the patient with ARDS on ECMO, is provided.

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.

Multimodality Imaging of Right Heart Function: JACC Scientific Statement

Right ventricular (RV) size and function assessed by multimodality imaging are associated with outcomes in a variety of cardiovascular diseases. Understanding RV anatomy and physiology is essential in appreciating the strengths and weaknesses of current imaging methods and gives these measurements greater context. The adaptation of the right ventricle to different types and severity of stress, particularly over time, is specific to the cardiovascular disease process. Multimodality imaging parameters, which determine outcomes, reflect the ability to image the initial and longitudinal RV response to stress. This paper will review the standard and novel imaging methods for assessing RV function and the impact of these parameters on outcomes in specific disease states.

Deep Learning for Echocardiography: Introduction for Clinicians and Future Vision: State-of-the-Art Review

Exponential growth in data storage and computational power is rapidly narrowing the gap between translating findings from advanced clinical informatics into cardiovascular clinical practice. Specifically, cardiovascular imaging has the distinct advantage in providing a great quantity of data for potentially rich insights, but nuanced interpretation requires a high-level skillset that few individuals possess. A subset of machine learning, deep learning (DL), is a modality that has shown promise, particularly in the areas of image recognition, computer vision, and video classification. Due to a low signal-to-noise ratio, echocardiographic data tend to be challenging to classify; however, utilization of robust DL architectures may help clinicians and researchers automate conventional human tasks and catalyze the extraction of clinically useful data from the petabytes of collected imaging data. The promise is extending far and beyond towards a contactless echocardiographic exam-a dream that is much needed in this time of uncertainty and social distancing brought on by a stunning pandemic culture. In the current review, we discuss state-of-the-art DL techniques and architectures that can be used for image and video classification, and future directions in echocardiographic research in the current era.

Evaluation of right ventricle systolic function after tetralogy of Fallot repair: A systematic review comparing cardiac magnetic resonance and global longitudinal strain

BACKGROUND

Most patients who undergo tetralogy of Fallot (TOF) repair experience late right ventricle (RV) dysfunction due to pulmonary valve regurgitation (PVR). Cardiac magnetic resonance (CMR) is the gold standard method for evaluating RV during follow-up. Global longitudinal strain (GLS) has been introduced as a novel method for the assessment of RV dysfunction. We aimed to compare the feasibility of GLS and CMR for assessing RV function after TOF repair.

METHODS

We systematically reviewed the English literature using PubMed, SciELO and Google Scholar for articles published between January 1, 2015, and December 31, 2020. Articles evaluating RV function comparing by GLS and CMR after TOF repair were included.

RESULTS

Nine studies including 465 patients were analyzed. Most patients were men (280; 60%), the male:female ratio was 1.5:1, and the age range was .8 to 57.7 years. The mean follow-up time was 6 to 32 months. The correlation between RV GLS and RV ejection fraction (EF) by CMR was negative for the articles and varied from moderate to strong (r = -.45, r = -.60, r = -.76).

CONCLUSION

Right ventricle GLS can be considered for routine follow-up of TOF repair patients, even though CMR remains the noninvasive gold standard method. Using a single parameter may not allow comparison of the accuracy of 3D RV EF by using CMR and GLS. Further studies with a larger number of patients undergoing TOF repair are required to evaluate the correlation between these examinations.

Application of AI in cardiovascular multimodality imaging

Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging.

Right ventricular size and function evaluated by various echocardiographic indices in dogs with pulmonary hypertension

BACKGROUND

Three-dimensional (3D) echocardiography and 2-dimensional (2D) strain measurements of the right ventricle (RV) are important indices in humans with pulmonary hypertension (PH) and need further evaluation in dogs with PH.

OBJECTIVES

To evaluate various RV size and function indices in dogs with PH and to examine differences between pre- and postcapillary PH.

ANIMALS

A total of 311 client-owned dogs: 100 dogs with PH, 31 with postcapillary and 69 with precapillary PH, and 211 healthy control dogs.

METHODS

Retro- and prospective, multicenter study. Size and function of the RV was determined using several indices, derived using dedicated RV software, including 3D RV end-diastolic volume (EDVn), end-systolic volume (ESVn), ejection fraction, 2D global and free wall RV longitudinal strain (RVLS), end-diastolic area, end-systolic area, fractional area change, tricuspid annular plane systolic excursion, and tissue Doppler imaging-derived systolic myocardial velocity of the lateral tricuspid annulus (S'n).

RESULTS

The EDVn (1.8 vs 2.5 mL/kg^0.942 , P < .01) and ESVn (0.8 vs 1.2 mL/kg^0.962 , P < .001) were significantly larger in the PH group compared to healthy controls. Free wall RVLS was decreased in dogs with severe PH compared to controls (-24% vs -29.6%, P < .001). Dogs with precapillary PH had worse RV systolic function than dogs with postcapillary PH.

CONCLUSION

Three-dimensional echocardiography of the RV is a promising tool to detect RV changes in dogs with PH. Also, 2D strain measurements are able to detect decreased RV function and offer several advantages compared to conventional indices.

Lateral annular systolic excursion ratio: A novel measurement of right ventricular systolic function by two-dimensional echocardiography

Introduction

Accurate assessment of right ventricular (RV) systolic function has prognostic and therapeutic implications in many disease states. Echocardiography remains the most frequently deployed imaging modality for this purpose, but estimation of RV systolic function remains challenging. The purpose of this study was to evaluate the diagnostic performance of a novel measurement of RV systolic function called lateral annular systolic excursion ratio (LASER), which is the fractional shortening of the lateral tricuspid annulus to apex distance, compared to right ventricular ejection fraction (RVEF) derived by cardiac magnetic resonance imaging (CMR).

Methods

A retrospective cohort of 78 consecutive patients who underwent clinically indicated CMR and transthoracic echocardiography within 30 days were identified from a database. Parameters of RV function measured included: tricuspid annular plane systolic excursion (TAPSE) by M-mode, tissue Doppler S', fractional area change (FAC) and LASER. These measurements were compared to RVEF derived by CMR using Pearson's correlation coefficients and receiver operating characteristic curves.

Results

LASER was measurable in 75 (96%) of patients within the cohort. Right ventricular systolic dysfunction, by CMR measurement, was present in 37% (n = 29) of the population. LASER has moderate positive correlation with RVEF (r = 0.54) which was similar to FAC (r = 0.56), S' (r = 0.49) and TAPSE (r = 0.37). Receiver operating characteristic curves demonstrated that LASER (AUC = 0.865) outperformed fractional area change (AUC = 0.767), tissue Doppler S' (AUC = 0.744) and TAPSE (AUC = 0.645). A cohort derived dichotomous cutoff of 0.2 for LASER was shown to provide optimal diagnostic characteristics (sensitivity of 75%, specificity of 87% and accuracy of 83%) for identifying abnormal RV function. LASER had the highest sensitivity, accuracy, positive and negative predictive values among the parameters studied in the cohort.

Conclusions

Within the study cohort, LASER was shown to have moderate positive correlation with RVEF derived by CMR and more favorable diagnostic performance for detecting RV systolic dysfunction compared to conventional echocardiographic parameters while being simple to obtain and less dependent on image quality than FAC and emerging techniques.

Contemporary Applications of Machine Learning for Device Therapy in Heart Failure

Despite a better understanding of the underlying pathogenesis of heart failure (HF), pharmacotherapy, surgical, and percutaneous interventions do not prevent disease progression in all patients, and a significant proportion of patients end up requiring advanced therapies. Machine learning (ML) is gaining wider acceptance in cardiovascular medicine because of its ability to incorporate large, complex, and multidimensional data and to potentially facilitate the creation of predictive models not constrained by many of the limitations of traditional statistical approaches. With the coexistence of "big data" and novel advanced analytic techniques using ML, there is ever-increasing research into applying ML in the context of HF with the goal of improving patient outcomes. Through this review, the authors describe the basics of ML and summarize the existing published reports regarding contemporary applications of ML in device therapy for HF while highlighting the limitations to widespread implementation and its future promises.

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.

Diagnostic value of miRNA expression and right ventricular echocardiographic functional parameters for chronic thromboembolic pulmonary hypertension with right ventricular dysfunction and injury

Background

We aimed to establish the relationships between the expression of microRNAs (miRNAs) and echocardiographic right ventricular (RV) function parameters, and to explore the effectiveness and clinical value of miRNA expression in predicting RV injury and dysfunction in patients with chronic thromboembolic pulmonary hypertension (CTEPH).

Methods

In this retrospective study, clinical data were collected from eight CTEPH patients and eight healthy individuals. RV parameters on echocardiography were analyzed, and the expression levels of specific miRNAs were measured by quantitative real-time PCR. Correlation analysis was performed on structural and functional RV parameters and five candidate miRNAs (miR-20a-5p, miR-17-5p, miR-93-5p, miR-3202 and miR-665). The diagnostic value of RV functional parameters and miRNAs expression was assessed by receiver operating characteristic (ROC) curve analysis and C statistic.

Results

Among the tested miRNAs, miR-20a-5p expression showed the best correlation with echocardiographic RV functional parameters (P < 0.05), although the expression levels of miR-93-5p, miR-17-5p and miR-3202 showed positive associations with some RV parameters. ROC curve analysis demonstrated the ability of miR-20a-5p expression to predict RV dysfunction, with a maximum area under the curve of 0.952 (P = 0.003) when the predicted RV longitudinal strain was less than –20%. The C index for RV dysfunction prediction by the combination of miRNAs (miR-20a-5p, miR-93-5p and miR-17-5p) was 1.0, which was significantly larger than the values for miR-93-5p and miR-17-5p individually (P = 0.0337 and 0.0453, respectively).

Conclusion

Among the tested miRNAs, miR -20a-5p, miR -93-5p and miR -17-5p have potential value in the diagnosis of CTEPH based on the correlation between the abnormal expression of these miRNAs and echocardiographic parameters in CTEPH patients. miR-20a-5p showed the strongest correlation with echocardiographic RV functional parameters. Moreover, expression of a combination of miRNAs seemed to show excellent predictive power for RV dysfunction.

Assessment of right ventricular size and function from cardiovascular magnetic resonance images using artificial intelligence

BACKGROUND

Theoretically, artificial intelligence can provide an accurate automatic solution to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic resonance (CMR) images, despite the complex RV geometry. However, in our recent study, commercially available deep learning (DL) algorithms for RVEF quantification performed poorly in some patients. The current study was designed to test the hypothesis that quantification of RV function could be improved in these patients by using more diverse CMR datasets in addition to domain-specific quantitative performance evaluation metrics during the cross-validation phase of DL algorithm development.

METHODS

We identified 100 patients from our prior study who had the largest differences between manually measured and automated RVEF values. Automated RVEF measurements were performed using the original version of the algorithm (DL1), an updated version (DL2) developed from a dataset that included a wider range of RV pathology and validated using multiple domain-specific quantitative performance evaluation metrics, and conventional methodology performed by a core laboratory (CORE). Each of the DL-RVEF approaches was compared against CORE-RVEF reference values using linear regression and Bland-Altman analyses. Additionally, RVEF values were classified into 3 categories: ≤ 35%, 35-50%, and ≥ 50%. Agreement between RVEF classifications made by the DL approaches and the CORE measurements was tested.

RESULTS

CORE-RVEF and DL-RVEFs were obtained in all patients (feasibility of 100%). DL2-RVEF correlated with CORE-RVEF better than DL1-RVEF (r = 0.87 vs. r = 0.42), with narrower limits of agreement. As a result, DL2 algorithm also showed increasing accuracy from 0.53 to 0.80 for categorizing RV function.

CONCLUSIONS

The use of a new DL algorithm cross-validated on a dataset with a wide range of RV pathology using multiple domain-specific metrics resulted in a considerable improvement in the accuracy of automated RVEF measurements. This improvement was demonstrated in patients whose images were the most challenging and resulted in the largest RVEF errors. These findings underscore the critical importance of this strategy in the development of DL approaches for automated CMR measurements.

Semi-supervised learning with natural language processing for right ventricle classification in echocardiography-a scalable approach

We created a deep learning model, trained on text classified by natural language processing (NLP), to assess right ventricular (RV) size and function from echocardiographic images. We included 12,684 examinations with corresponding written reports for text classification. After manual annotation of 1489 reports, we trained an NLP model to classify the remaining 10,651 reports. A view classifier was developed to select the 4-chamber or RV-focused view from an echocardiographic examination (n = 539). The final models were two image classification models trained on the predicted labels from the combined manual annotation and NLP models and the corresponding echocardiographic view to assess RV function (training set n = 11,008) and size (training set n = 9951. The text classifier identified impaired RV function with 99% sensitivity and 98% specificity and RV enlargement with 98% sensitivity and 98% specificity. The view classification model identified the 4-chamber view with 92% accuracy and the RV-focused view with 73% accuracy. The image classification models identified impaired RV function with 93% sensitivity and 72% specificity and an enlarged RV with 80% sensitivity and 85% specificity; agreement with the written reports was substantial (both κ = 0.65). Our findings show that models for automatic image assessment can be trained to classify RV size and function by using model-annotated data from written echocardiography reports. This pipeline for auto-annotation of the echocardiographic images, using a NLP model with medical reports as input, can be used to train an image-assessment model without manual annotation of images and enables fast and inexpensive expansion of the training dataset when 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.

Three-Dimensional Echocardiography Assessment of Right Ventricular Volumes and Function: Technological Perspective and Clinical Application

Right ventricular (RV) function has important prognostic value in a variety of cardiovascular diseases. Due to complex anatomy and mode of contractility, conventional two-dimensional echocardiography does not provide sufficient and accurate RV function assessment. Currently, three-dimensional echocardiography (3DE) allows for an excellent and reproducible assessment of RV function owing to overcoming these limitations of traditional echocardiography. This review focused on 3DE and discussed the following points: (i) acquisition of RV dataset for 3DE images, (ii) reliability, feasibility, and reproducibility of RV volumes and function measured by 3DE with different modalities, (iii) the clinical application of 3DE for RV function quantification.

Quantitative assessment of right ventricular size and function with multiple parameters from artificial intelligence-based three-dimensional echocardiography: A comparative study with cardiac magnetic resonance

AIMS

This study aimed to explore the validation and the diagnostic value of multiple right ventricle (RV) volumes and functional parameters derived from a novel artificial intelligence (AI)-based three-dimensional echocardiography (3DE) algorithm compared to cardiac magnetic resonance (CMR).

METHODS AND RESULTS

A total of 51 patients with a broad spectrum of clinical diagnoses were finally included in this study. AI-based RV 3DE was performed in a single-beat HeartModel mode within 24 hours after CMR. In the entire population, RV volumes and right ventricular ejection fraction (RVEF) measured by AI-based 3DE showed statistically significant correlations with the corresponding CMR analysis (p < 0.05 for all). However, the Bland-Altman plots indicated that these parameters were slightly underestimated by AI-based 3DE. Based on CMR derived RVEF < 45% as RV dysfunction, end-systolic volume (ESV), end-systolic volume index (ESVi), stroke volume (SV), and RVEF showed great diagnostic performance in identifying RV dysfunction, as well as some non-volumetric parameters, including tricuspid annular systolic excursion (TAPSE), fractional area change (FAC), and free-wall longitudinal strains (LS) (p < 0.05 for all). The cutoff value was 43% for RVEF with a sensitivity of 94% and specificity of 67%.

CONCLUSION

AI-based 3DE could provide rapid and accurate quantitation of the RV volumes and function with multiple parameters. Both volumetric and non-volumetric measurements derived from AI-based 3DE contributed to the identification of the RV dysfunction.

Echocardiographic reference intervals for right ventricular indices, including 3-dimensional volume and 2-dimensional strain measurements in healthy dogs

BACKGROUND

There is currently a lack of reference intervals (RIs) for the novel measures like 3-dimensional (3D) echocardiography or speckle-tracking strain for assessment of right ventricular (RV) structure and function.

OBJECTIVES

To generate RIs and to determine the influence of age, heart rate, and body weight (BW) on various RV function indices using a dedicated RV software for 3D RV end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), global and free wall RV longitudinal strain (RVLS), end-diastolic area (RVEDA), end-systolic area (RVESA), fractional area change (FAC), tricuspid annular plane systolic excursion (TAPSE), and tissue Doppler imaging (TVI)-derived systolic myocardial velocity of the lateral tricuspid annulus (S').

ANIMALS

Healthy adult client-owned dogs (n = 211) of various breeds and ages.

METHODS

Prospective study. Reference intervals were estimated as statistical prediction intervals using allometric scaling for BW-dependent variables. Right-sided (upper limit) or left-sided (lower limit) 95% RIs were calculated for every variable. Inter- and intraobserver variability was determined.

RESULTS

Most variables showed clinically acceptable repeatability with coefficient of variation less than 10. Upper or respectively lower RI after allometric scaling to normalize for different BWs were: EDVn ≤ 2.5 mL/kg^0.942 , ESVn ≤ 1.2 mL/kg^0.962 , TAPSEn ≥ 4.5 mm^0.285 , RVEDAn ≤ 1.4 cm² /kg^0.665 , RVESAn ≤ 0.8 cm² /kg^0.695 , and TVI S'n ≥ 5.6 cm/s/kg^0.186 . The calculated limits for indices without allometric normalization were: EF > 42.1%, FAC > 30.0%, free wall RVLS < -20.8%, and global RVLS < -18.3%.

CONCLUSIONS

Echocardiographic RIs for RV structure and function are provided.

Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects

The human right ventricle is barely monitored during open-chest surgery due to the absence of intraoperative imaging techniques capable of elaborating its complex function. Accordingly, artificial intelligence could not be adopted for this specific task. We recently proposed a video-based approach for the real-time evaluation of the epicardial kinematics to support medical decisions. Here, we employed two supervised machine learning algorithms based on our technique to predict the patients' outcomes before chest closure. Videos of the beating hearts were acquired before and after pulmonary valve replacement in twelve Tetralogy of Fallot patients and recordings were properly labeled as the "unhealthy" and "healthy" classes. We extracted frequency-domain-related features to train different supervised machine learning models and selected their best characteristics via 10-fold cross-validation and optimization processes. Decision surfaces were built to classify two additional patients having good and unfavorable clinical outcomes. The k-nearest neighbors and support vector machine showed the highest prediction accuracy; the patients' class was identified with a true positive rate ≥95% and the decision surfaces correctly classified the additional patients in the "healthy" (good outcome) or "unhealthy" (unfavorable outcome) classes. We demonstrated that classifiers employed with our video-based technique may aid cardiac surgeons in decision making before chest closure.

Feasibility and Accuracy of a Fully Automated Right Ventricular Quantification Software With Three-Dimensional Echocardiography: Comparison With Cardiac Magnetic Resonance

Background: A novel, fully automated right ventricular (RV) software for three-dimensional quantification of RV volumes and function was developed. The direct comparison of the software performance with cardiac magnetic resonance (CMR) was limited. Therefore, the aim of this study was to test the feasibility, accuracy, and reproducibility of a fully automated RV quantification software against CMR imaging as a reference.

Methods: A total of 170 patients who underwent both CMR and three-dimensional echocardiography were enrolled. RV end-diastolic volume (RVEDV), RV end-systolic volume (RVESV), and RV ejection fraction (RVEF) were obtained using fully automated three-dimensional RV quantification software and compared with a CMR reference. For inter-technical agreement, Spearman correlation and Bland–Altman analysis were used.

Results: The fully automated RV quantification software was feasible in 149 patients. RVEDV and RVESV were underestimated, and RVEF was overestimated compared with CMR values. RV measurements obtained from the manual editing method correlated better with CMR values than that without manual editing (RVEDV, 0.924 vs. 0.794: RVESV, 0.955 vs. 0.854; RVEF, 0.941 vs. 0.781 respectively, all p < 0.0001) with less bias and narrower limit of agreement (LOA). The bias and LOA for RV volumes and EF using the automated software without and with manual editing were greater in patients with severely impaired RV function or low frame rate than those with normal and mild impaired RV function, or high frame rate. The fully automated RV three-dimensional measurements were highly reproducible.

Conclusion: The novel fully automated RV software shows good feasibility and reproducibility, and the measurements had a high correlation with CMR values. These findings support the routine application of the novel 3D automated RV software in clinical practice.

Artificial intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A systematic review

The echocardiogram is a test that is widely used in Heart Disease Diagnoses. However, its analysis is largely dependent on the physician's experience. In this regard, artificial intelligence has become an essential technology to assist physicians. This study is a Systematic Literature Review (SLR) of primary state-of-the-art studies that used Artificial Intelligence (AI) techniques to automate echocardiogram analyses. Searches on the leading scientific article indexing platforms using a search string returned approximately 1400 articles. After applying the inclusion and exclusion criteria, 118 articles were selected to compose the detailed SLR. This SLR presents a thorough investigation of AI applied to support medical decisions for the main types of echocardiogram (Transthoracic, Transesophageal, Doppler, Stress, and Fetal). The article's data extraction indicated that the primary research interest of the studies comprised four groups: 1) Improvement of image quality; 2) identification of the cardiac window vision plane; 3) quantification and analysis of cardiac functions, and; 4) detection and classification of cardiac diseases. The articles were categorized and grouped to show the main contributions of the literature to each type of ECHO. The results indicate that the Deep Learning (DL) methods presented the best results for the detection and segmentation of the heart walls, right and left atrium and ventricles, and classification of heart diseases using images/videos obtained by echocardiography. The models that used Convolutional Neural Network (CNN) and its variations showed the best results for all groups. The evidence produced by the results presented in the tabulation of the studies indicates that the DL contributed significantly to advances in echocardiogram automated analysis processes. Although several solutions were presented regarding the automated analysis of ECHO, this area of research still has great potential for further studies to improve the accuracy of results already known in the literature.

Right Ventricular Function Predicts Adverse Clinical Outcomes in Patients With Chronic Thromboembolic Pulmonary Hypertension: A Three-Dimensional Echocardiographic Study

Background: Right ventricular (RV) function plays a vital role in the prognosis of patients with chronic thromboembolic pulmonary hypertension (CTEPH). We used new machine learning (ML)-based fully automated software to quantify RV function using three-dimensional echocardiography (3DE) to predict adverse clinical outcomes in CTEPH patients.

Methods: A total of 151 consecutive CTEPH patients were registered in this prospective study between April 2015 and July 2019. New ML-based methods were used for data management, and quantitative analysis of RV volume and ejection fraction (RVEF) was performed offline. RV structural and functional parameters were recorded using 3DE. CTEPH was diagnosed using right heart catheterization, and 62 patients underwent cardiac magnetic resonance to assess right heart function. Adverse clinical outcomes were defined as PH-related hospitalization with hemoptysis or increased RV failure, including conditions requiring balloon pulmonary angioplasty or pulmonary endarterectomy, as well as death.

Results: The median follow-up time was 19.7 months (interquartile range, 0.5–54 months). Among the 151 CTEPH patients, 72 experienced adverse clinical outcomes. Multivariate Cox proportional-hazard analysis showed that ML-based 3DE analysis of RVEF was a predictor of adverse clinical outcomes (hazard ratio, 1.576; 95% confidence interval (CI), 1.046~2.372; P = 0.030).

Conclusions: The new ML-based 3DE algorithm is a promising technique for rapid 3D quantification of RV function in CTEPH patients.

Role of Echocardiography for the Perioperative Assessment of the Right Ventricle

Purpose of Review

This review aims to highlight the perioperative echocardiographic evaluation of right ventricular (RV) function with strengths and limitations of commonly used and evolving techniques. It explains the value of transthoracic echocardiography (TTE) and transesophageal echocardiography (TEE) and describes the perioperative changes of RV function echocardiographers should be aware of.

Recent Findings

RV dysfunction is an entity with strong influence on outcome. However, its definition and assessment in the perioperative interval are not well-defined. Moreover, values assessed by TTE and TEE are not interchangeable; while some parameters seem to correlate well, others do not. Myocardial strain analysis and three-dimensional echocardiography may overcome the limitations of conventional echocardiographic measures and provide further insight into perioperative cardiac mechanics.

Summary

Echocardiography has become an essential part of modern anesthesiology in patients with RV dysfunction. It offers the opportunity to evaluate not only global but also regional RV function and distinguish alterations of RV contraction.

Right Ventricular Function and Its Coupling With Pulmonary Circulation in Precapillary Pulmonary Hypertension: A Three-Dimensional Echocardiographic Study

Objective: To assess right ventricular (RV) function and RV-pulmonary arterial (PA) coupling by three-dimensions echocardiography and investigate the ability of RV-PA coupling to predict adverse clinical outcomes in patients with precapillary pulmonary hypertension (PH). Methods: We retrospectively collected a longitudinal cohort of 203 consecutive precapillary PH patients. RV volume, RV ejection fraction (RVEF), and RV longitudinal strain (RVLS) were quantitatively determined offline by 3D echocardiography. RV-PA coupling parameters including the RVEF/PA systolic pressure (PASP) ratio, pulmonary arterial compliance (PAC), and total pulmonary resistance (TPR) were recorded. Results: Over a median follow-up period of 20.9 months (interquartile range, 0.1-67.4 months), 87 (42.9%) of 203 patients experienced adverse clinical outcomes. With increasing World Health Organization functional class (WHO-FC), significant trends were observed in increasing RV volume, decreasing RVEF, and worsening RVLS. RV arterial coupling (RVAC) and PAC were lower and TPR was higher for WHO-FC III+IV than WHO-FC I or II. The RVEF/PASP ratio showed a significant correlation with RVLS. RVAC had a stronger correlation with the RVEF/PASP ratio than other indices. Multivariate Cox proportional-hazard analysis identified a lower 3D RVEF and worse RVLS as strong predictors of adverse clinical events. RVAC, TPR, and PAC had varying degrees of predictive value, with optimal cutoff values of 0.74, 11.64, and 1.18, respectively. Conclusions: Precapillary-PH with RV-PA uncoupling as expressed by a RVEF/PASP ratio <0.44 was associated with adverse clinical outcomes. PAC decreased and TPR increased with increasing WHO-FC, with TPR showing better independent predictive value.

Accuracy of fully automated right ventricular quantification software with 3D echocardiography: direct comparison with cardiac magnetic resonance and semi-automated quantification software

AIMS

The aim of this study was to determine the accuracy and reproducibility of a novel, fully automated 3D echocardiography (3DE) right ventricular (RV) quantification software compared with cardiac magnetic resonance (CMR) and semi-automated 3DE RV quantification software.

METHODS AND RESULTS

RV volumes and the RV ejection fraction (RVEF) were measured using a fully automated software (Philips), a semi-automated software (TomTec), and CMR in 100 patients who had undergone both CMR and 3DE examinations on the same day. The feasibility of the fully automated software was 91%. Although the fully automated software, without any manual editing, significantly underestimated RV end-diastolic volume (bias: -12.6 mL, P < 0.001) and stroke volume (-5.1 mL, P < 0.001) compared with CMR, there were good correlations between the two modalities (r = 0.82 and 0.78). No significant differences in RVEF between the fully automated software and CMR were observed, and there was a fair correlation (r = 0.72). The RVEF determined by the semi-automated software was significantly larger than that by CMR or the fully automated software (P < 0.001). The fully automated software had a shorter analysis time compared with the semi-automated software (15 s vs. 120 s, P < 0.001) and had a good reproducibility.

CONCLUSION

A novel, fully automated 3DE RV quantification software underestimated RV volumes but successfully approximated RVEF when compared with CMR. No inferiority of this software was observed when compared with the semi-automated software. Rapid analysis and higher reproducibility also support the routine adoption of this method in the daily clinical workflow.