Artificial intelligence based left ventricular ejection fraction and global longitudinal strain in cardiac amyloidosis.

BACKGROUND: Assessment of left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) plays a key role in the diagnosis of cardiac amyloidosis (CA). However, manual measurements are time consuming and prone to variability. We aimed to assess whether fully automated artificial intelligence (AI) calculation of LVEF and GLS provide similar estimates and can identify abnormalities in agreement with conventional manual methods, in patients with pre-clinical and clinical CA. METHODS: We identified 51 patients (age 80 ± 10 years, 53% male) with confirmed CA according to guidelines, who underwent echocardiography before and/or at the time of CA diagnosis (median (IQR) time between observations 3.87 (1.93, 5.44 years). LVEF and GLS were quantified from the apical 2- and 4-chamber views using both manual and fully automated methods (EchoGo Core 2.0, Ultromics). Inter-technique agreement was assessed using linear regression and Bland-Altman analyses and two-way ANOVA. The diagnostic accuracy and time for detecting abnormalities (defined as LVEF ≤ 50% and GLS ≥ -15.1%, respectively) using AI was assessed by comparisons to manual measurements as a reference. RESULTS: There were no significant differences in manual and automated LVEF and GLS values in either pre-CA (p = .791 and p = .105, respectively) or at diagnosis (p = .463 and p = .722). The two methods showed strong correlation on both the pre-CA (r = .78 and r = .83) and CA echoes (r = .74 and r = .80) for LVEF and GLS, respectively. The sensitivity and specificity of AI-derived indices for detecting abnormal LVEF were 83% and 86%, respectively, in the pre-CA echo and 70% and 79% at CA diagnosis. The sensitivity and specificity of AI-derived indices for detecting abnormal GLS was 82% and 86% in the pre-CA echo and 100% and 67% at the time of CA diagnosis. There was no significant difference in the relationship between LVEF (p = .99) and GLS (p = .19) and time to abnormality between the two methods. CONCLUSION: Fully automated AI-calculated LVEF and GLS are comparable to manual measurements in patients pre-CA and at the time of CA diagnosis. The widespread implementation of automated LVEF and GLS may allow for more rapid assessment in different disease states with comparable accuracy and reproducibility to manual methods.

Chikungunya and the Heart.

INTRODUCTION: Neglected tropical diseases are a group of communicable diseases that occur in tropical and subtropical conditions and are closely related to poverty and inadequate sanitation conditions. Among these entities, chikungunya remains one of the most widely spread diseases. Although the main symptoms are related to a febrile syndrome, cardiovascular (CV) involvement has been reported, with short- and long-term implications. As part of the "Neglected Tropical Diseases and other Infectious Diseases involving the Heart" (NET-Heart) Project, the aim of this review is to compile all the information available regarding CV involvement of this disease, to help healthcare providers gain knowledge in this field, and contribute to improving early diagnosis, treatment, and prevention strategies. METHODS: We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement in conducting and reporting this systematic review. The search was conducted using MEDLINE/PubMed, SciELO, and LILACS databases to identify any relevant studies or reviews detailing an association between chikungunya and cardiac involvement published from January 1972 to May 31, 2020. RESULTS: Despite its mechanism not being fully understood, CV involvement has been described as the most frequent atypical presentation of chikungunya (54.2%). Myocarditis is the most prevalent CV complication. Different rhythm disturbances have been reported in 52% of cases, whereas heart failure was reported in 15% of cases, pericarditis in 5%, and acute myocardial infarction in 2%. Overall estimated CV mortality is 10%, although in patients with other comorbidities, it may increase up to 20%. In the proper clinical setting, the presence of fever, polyarthralgia, and new-onset arrhythmia suggests chikungunya virus-related myocarditis. CONCLUSION: Although most cases are rarely fatal, CV involvement in chikungunya infection remains the most frequent atypical presentation of this disease and may have severe manifestations. Timely diagnosis and appropriate management are necessary to improve patient outcomes.

Three-dimensional echocardiographic evaluation of longitudinal and non-longitudinal components of right ventricular contraction: results from the World Alliance of Societies of Echocardiography study.

AIMS: Right ventricular (RV) functional assessment is mainly limited to its longitudinal contraction. Dedicated three-dimensional echocardiography (3DE) software enabled the separate assessment of the non-longitudinal components of RV ejection fraction (EF). The aims of this study were (i) to establish normal values for RV 3D-derived longitudinal, radial, and anteroposterior EF (LEF, REF, and AEF, respectively) and their relative contributions to global RVEF, (ii) to calculate 3D RV strain normal values, and (iii) to determine sex-, age-, and race-related differences in these parameters in a large group of normal subjects (WASE study). METHODS AND RESULTS: 3DE RV wide-angle datasets from 1043 prospectively enrolled healthy adult subjects were analysed to generate a 3D mesh model of the RV cavity (TomTec). Dedicated software (ReVISION) was used to analyse RV motion along the three main anatomical planes. The EF values corresponding to each plane were identified as LEF, REF, and AEF. Relative contributions were determined by dividing each EF component by the global RVEF. RV strain analysis included longitudinal, circumferential, and global area strains (GLS, GCS, and GAS, respectively). Results were categorized by sex, age (18-40, 41-65, and >65 years), and race. Absolute REF, AEF, LEF, and global RVEF were higher in women than in men (P < 0.001). With aging, both sexes exhibited a decline in all components of longitudinal shortening (P < 0.001), which was partially compensated in elderly women by an increase in radial contraction. Black subjects showed lower RVEF and GAS values compared with white and Asian subjects of the same sex (P < 0.001), and black men showed significantly higher RV radial but lower longitudinal contributions to global RVEF compared with Asian and white men. CONCLUSION: 3DE evaluation of the non-longitudinal components of RV contraction provides additional information regarding RV physiology, including sex-, age-, and race-related differences in RV contraction patterns that may prove useful in disease states involving the right ventricle.

Echocardiographic Detection of Regional Wall Motion Abnormalities Using Artificial Intelligence Compared to Human Readers.

BACKGROUND: Although regional wall motion abnormality (RWMA) detection is foundational to transthoracic echocardiography, current methods are prone to interobserver variability. We aimed to develop a deep learning (DL) model for RWMA assessment and compare it to expert and novice readers. METHODS: We used 15,746 transthoracic echocardiography studies-including 25,529 apical videos-which were split into training, validation, and test datasets. A convolutional neural network was trained and validated using apical 2-, 3-, and 4-chamber videos to predict the presence of RWMA in 7 regions defined by coronary perfusion territories, using the ground truth derived from clinical transthoracic echocardiography reports. Within the test cohort, DL model accuracy was compared to 6 expert and 3 novice readers using F1 score evaluation, with the ground truth of RWMA defined by expert readers. Significance between the DL model and novices was assessed using the permutation test. RESULTS: Within the test cohort, the DL model accurately identified any RWMA with an area under the curve of 0.96 (0.92-0.98). The mean F1 scores of the experts and the DL model were numerically similar for 6 of 7 regions: anterior (86 vs 84), anterolateral (80 vs 74), inferolateral (83 vs 87), inferoseptal (86 vs 86), apical (88 vs 87), inferior (79 vs 81), and any RWMA (90 vs 94), respectively, while in the anteroseptal region, the F1 score of the DL model was lower than the experts (75 vs 89). Using F1 scores, the DL model outperformed both novices 1 (P = .002) and 2 (P = .02) for the detection of any RWMA. CONCLUSIONS: Deep learning provides accurate detection of RWMA, which was comparable to experts and outperformed a majority of novices. Deep learning may improve the efficiency of RWMA assessment and serve as a teaching tool for novices.

Limitations of apical sparing pattern in cardiac amyloidosis: a multicentre echocardiographic study.

AIMS: Although impaired left ventricular (LV) global longitudinal strain (GLS) with apical sparing is a feature of cardiac amyloidosis (CA), its diagnostic accuracy has varied across studies. We aimed to determine the ability of apical sparing ratio (ASR) and most common echocardiographic parameters to differentiate patients with confirmed CA from those with clinical and/or echocardiographic suspicion of CA but with this diagnosis ruled out. METHODS AND RESULTS: We identified 544 patients with confirmed CA and 200 controls (CTRLs) as defined above (CTRL patients). Measurements from transthoracic echocardiograms were performed using artificial intelligence software (Us2.AI, Singapore) and audited by an experienced echocardiographer. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance and optimal cut-offs for the differentiation of CA patients from CTRL patients. Additionally, a group of 174 healthy subjects (healthy CTRL) was included to provide insight on how patients and healthy CTRLs differed echocardiographically. LV GLS was more impaired (-13.9 ± 4.6% vs. -15.9 ± 2.7%, P < 0.0005), and ASR was higher (2.4 ± 1.2 vs. 1.7 ± 0.9, P < 0.0005) in the CA group vs. CTRL patients. Relative wall thickness and ASR were the most accurate parameters for differentiating CA from CTRL patients [area under the curve (AUC): 0.77 and 0.74, respectively]. However, even with the optimal cut-off of 1.67, ASR was only 72% sensitive and 66% specific for CA, indicating the presence of apical sparing in 32% of CTRL patients and even in 6% healthy subjects. CONCLUSION: Apical sparing did not prove to be a CA-specific biomarker for accurate identification of CA, when compared with clinically similar CTRLs with no CA.

Cardiovascular computed tomography for the detection of quadricuspid aortic valve: A case report.

Aortic regurgitation (AR) represents a significant cause of morbidity and mortality. Due to its low cost and widespread availability, echocardiography remains the frontline for aortic valve (AV) assessment. However, poor sonographic windows may limit the assessment of valve morphology with this technique. Cardiovascular computed tomography (CCT) is increasingly utilized prior to structural AV interventions. Due to its excellent spatial resolution, CCT provides exceptional characterization of aortic leaflets. Accordingly, we present a case of a quadricuspid valve diagnosed by CCT. Here, CCT led to a new diagnosis of quadricuspid valve, highlighting the potential for CCT for the characterization of aortic leaflet morphology. CCT may be particularly useful in patients with contraindications to transesophageal echocardiography or those undergoing structural or robotic interventions.

How Often Does Apical Sparing of Longitudinal Strain Indicate the Presence of Cardiac Amyloidosis?

Echocardiographic diagnosis of cardiac amyloidosis (CA) is frequently suggested by the presence of a left ventricular (LV) apical sparing pattern (ASP) on longitudinal strain (LS) assessment, the so-called "cherry on top" pattern, defined by strain magnitude preserved exclusively at the apex. However, it is unclear how frequently this strain pattern truly represents CA. This study aimed to evaluate the predictive value of ASP in the diagnosis of CA. We retrospectively identified consecutive adult patients who had the following studies performed within an 18-month period: (1) transthoracic echocardiogram and (2) either (a) cardiac magnetic resonance imaging, (b) Technetium-Pyrophosphate (PYP) imaging, or (c) endomyocardial biopsy. LS was retrospectively measured in the apical 4-, 3-, and 2-chamber views in patients who had adequate noncontrast images (n = 466). An apical sparing ratio (ASR) was calculated as (average apical strain)/[(average basal strain) + (average midventricular strain)]. Patients with ASR ≥1 were evaluated for the presence/absence of CA, using established criteria. Basic LV parameters were also measured. A total of 33 patients (7.1%) had ASP. Nine of these patients (27%) had "confirmed" CA, 2 (6.1%) "highly probable" CA, 1 (3.0%) "possible" CA, and 21 (64%) no evidence of CA. When comparing patients with and without confirmed CA, there were no significant differences in ASR, average global LS, ejection fraction, or LV mass. Patients with confirmed CA were older (76 ± 9 vs 59 ± 18 years, p = 0.01) and had thicker posterior wall (15 ± 3 vs 11 ± 3 mm, p = 0.004) with a trend toward thicker septal wall (15 ± 2 vs 12 ± 4 mm, p = 0.05). In conclusion, the presence of ASP on LS represents confirmed or highly probable CA in only 1/3 of patients and is more likely to indicate true CA in older patients with increased LV wall thickness. Although a larger, prospective study is needed to confirm these findings, 1/3 should be considered as a large diagnostic yield that justifies further testing, given the poor outcomes associated with CA diagnosis.

Noninvasive assessment of left ventricular end-diastolic pressure using machine learning-derived phasic left atrial strain.

AIMS: While transthoracic echocardiography (TTE) assessment of left ventricular end-diastolic pressure (LVEDP) is critically important, the current paradigm is subject to error and indeterminate classification. Recently, peak left atrial strain (LAS) was found to be associated with LVEDP. We aimed to test the hypothesis that integration of the entire LAS time curve into a single parameter could improve the accuracy of peak LAS in the noninvasive assessment of LVEDP with TTE. METHODS AND RESULTS: We retrospectively identified 294 patients who underwent left heart catheterization and TTE within 24 h. LAS curves were trained using machine learning (100 patients) to detect LVEDP ≥ 15 mmHg, yielding the novel parameter LAS index (LASi). The accuracy of LASi was subsequently validated (194 patients), side by side with peak LAS and ASE/EACVI guidelines, against invasive filling pressures. Within the validation cohort, invasive LVEDP was elevated in 116 (59.8%) patients. The overall accuracy of LASi, peak LAS, and American Society of Echocardiography/European Association for Cardiovascular Imaging (ASE/EACVI) algorithm was 79, 75, and 76%, respectively (excluding 37 patients with indeterminate diastolic function by ASE/EACVI guidelines). When the number of LASi indeterminates (defined by near-zero LASi values) was matched to the ASE/EACVI guidelines (n = 37), the accuracy of LASi improved to 87%. Importantly, among the 37 patients with ASE/EACVI-indeterminate diastolic function, LASi had an accuracy of 81%, compared with 76% for peak LAS. CONCLUSION: LASi allows the detection of elevated LVEDP using invasive measurements as a reference, at least as accurately as peak LAS and current diastolic function guideline algorithm, with the advantage of no indeterminate classifications in patients with measurable LAS.

Real-Time Artificial Intelligence-Based Guidance of Echocardiographic Imaging by Novices: Image Quality and Suitability for Diagnostic Interpretation and Quantitative Analysis.

BACKGROUND: We aimed to assess in a prospective multicenter study the quality of echocardiographic exams performed by inexperienced users guided by a new artificial intelligence software and evaluate their suitability for diagnostic interpretation of basic cardiac pathology and quantitative analysis of cardiac chamber and function. METHODS: The software (UltraSight, Ltd) was embedded into a handheld imaging device (Lumify; Philips). Six nurses and 3 medical residents, who underwent minimal training, scanned 240 patients (61±16 years; 63% with cardiac pathology) in 10 standard views. All patients were also scanned by expert sonographers using the same device without artificial intelligence guidance. Studies were reviewed by 5 certified echocardiographers blinded to the imager's identity, who evaluated the ability to assess left and right ventricular size and function, pericardial effusion, valve morphology, and left atrial and inferior vena cava sizes. Finally, apical 4-chamber images of adequate quality, acquired by novices and sonographers in 100 patients, were analyzed to measure left ventricular volumes, ejection fraction, and global longitudinal strain by an expert reader using conventional methodology. Measurements were compared between novices' and experts' images. RESULTS: Of the 240 studies acquired by novices, 99.2%, 99.6%, 92.9%, and 100% had sufficient quality to assess left ventricular size and function, right ventricular size, and pericardial effusion, respectively. Valve morphology, right ventricular function, and left atrial and inferior vena cava size were visualized in 67% to 98% exams. Images obtained by novices and sonographers yielded concordant diagnostic interpretation in 83% to 96% studies. Quantitative analysis was feasible in 83% images acquired by novices and resulted in high correlations (r≥0.74) and small biases, compared with those obtained by sonographers. CONCLUSIONS: After minimal training with the real-time guidance software, novice users can acquire images of diagnostic quality approaching that of expert sonographers in most patients. This technology may increase adoption and improve accuracy of point-of-care cardiac ultrasound.

Age-, Sex-, and Race-Based Normal Values for Left Ventricular Circumferential Strain from the World Alliance Societies of Echocardiography Study.

BACKGROUND: Left ventricular (LV) circumferential strain has received less attention than longitudinal deformation, which has recently become part of routine clinical practice. Among other reasons, this is because of the lack of established normal values. Accordingly, the aim of this study was to establish normative values for LV circumferential strain and determine sex-, age-, and race-related differences in a large cohort of healthy adults. METHODS: Complete two-dimensional transthoracic echocardiograms were obtained in 1,572 healthy subjects (51% men), enrolled in the World Alliance Societies of Echocardiography Normal Values Study. Subjects were divided into three age groups (<35, 35-55, and >55 years) and stratified by sex and by race. Vendor-independent semiautomated speckle-tracking software was used to determine LV regional circumferential strain and global circumferential strain (GCS) values. Limits of normal for each measurement were defined as 95% of the corresponding sex and age group falling between the 2.5th and 97.5th percentiles. Intergroup differences were analyzed using unpaired t tests. RESULTS: Circumferential strain showed a gradient, with lower magnitude at the mitral valve level, increasing progressively toward the apex. Compared with men, women had statistically higher magnitudes of regional and global strain. Older age was associated with a stepwise increase in GCS despite an unaffected ejection fraction, a decrease in LV volume, and relatively stable global longitudinal strain in men, with a small gradual decrease in women. Asian subjects demonstrated significantly higher GCS magnitudes than whites of both sexes and blacks among women only. In contrast, no significant differences in GCS were found between white and black subjects of either sex. Importantly, despite statistical significance of these differences across sex, age, and race, circumferential strain values were similar in all groups, with variations of the order of magnitude of 1% to 2%. Notably, no differences in GCS were found among brands of imaging equipment. CONCLUSION: This study established normal values of LV regional circumferential strain and GCS and identified sex-, age-, and race-related differences when present.

Normal Values of Left Ventricular Mass by Two-Dimensional and Three-Dimensional Echocardiography: Results from the World Alliance Societies of Echocardiography Normal Values Study.

BACKGROUND: Although increased left ventricular (LV) mass is associated with adverse outcomes, measured values vary widely depending on the specific technique used. Moreover, the impact of sex, age, and race on LV mass remains controversial, further limiting the clinical use of this parameter. Accordingly, the authors studied LV mass using a variety of two-dimensional and three-dimensional echocardiographic techniques in a large population of normal subjects encompassing a wide range of ages. METHODS: Transthoracic echocardiograms obtained from 1,854 healthy adult subjects (52% men) enrolled in the World Alliance Societies of Echocardiography (WASE) Normal Values Study, were divided into three age groups (young, 18-35 years; middle aged, 36-55 years; and old, >55 years). LV mass was obtained using five conventional techniques, including linear and two-dimensional methods, as well as direct three-dimensional measurement. All LV mass values were indexed to body surface area, and differences according to sex, age, and race were analyzed for each technique. RESULTS: LV mass values differed significantly among the five techniques. Three-dimensional measurements were considerably smaller than those obtained using the other techniques and were closer to magnetic resonance imaging normal values reported in the literature. For all techniques, LV mass in men was significantly larger than in women, with and without body surface area indexing. These technique- and sex-related differences were larger than measurement variability. In women, age differences in LV mass were more pronounced and depicted significantly larger values in older age groups for all techniques, except three-dimensional echocardiography, which showed essentially no differences. LV mass was overall larger in black subjects than in white or Asian subjects. CONCLUSIONS: Significant differences in LV mass values exist across echocardiographic techniques, which are therefore not interchangeable. Sex-, race-, and age-related differences underscore the need for separate population specific normal values.

Normative values of the aortic valve area and Doppler measurements using two-dimensional transthoracic echocardiography: results from the Multicentre World Alliance of Societies of Echocardiography Study.

AIMS: Aortic valve area (AVA) used for echocardiographic assessment of aortic stenosis (AS) has been traditionally interpreted independently of sex, age and race. As differences in normal values might impact clinical decision-making, we aimed to establish sex-, age- and race-specific normative values for AVA and Doppler parameters using data from the World Alliance Societies of Echocardiography (WASE) Study. METHODS AND RESULTS: Two-dimensional transthoracic echocardiographic studies were obtained from 1903 healthy adult subjects (48% women). Measurements of the left ventricular outflow tract (LVOT) diameter and Doppler parameters, including AV and LVOT velocity time integrals (VTIs), AV mean pressure gradient, peak velocity, were obtained according to ASE/EACVI guidelines. AVA was calculated using the continuity equation. Compared with men, women had smaller LVOT diameters and AVA values, and higher AV peak velocities and mean gradients (all P < 0.05). LVOT and AV VTI were significantly higher in women (P < 0.05), and both parameters increased with age in both sexes. AVA differences persisted after indexing to body surface area. According to the current diagnostic criteria, 13.5% of women would have been considered to have mild AS and 1.4% moderate AS. LVOT diameter and AVA were lower in older subjects, both men and women, and were lower in Asians, compared with whites and blacks. CONCLUSION: WASE data provide clinically relevant information about significant differences in normal AVA and Doppler parameters according to sex, age, and race. The implementation of this information into clinical practice should involve development of specific normative values for each ethnic group using standardized methodology.

Abnormal Longitudinal Strain Reduction of Basal Left Ventricular Segments in Patients Recovered of COVID-19.

Background: The Severe acute respiratory syndrome coronavirus 2, is the pathogen that causes COVID-19. Cardiovascular (CV) involvement during this infection, has been related to adverse outcomes. Interestingly, some patients may remain symptomatic beyond the first 14 days of disease diagnosis. Although a particular reduction on left ventricular (LV) global longitudinal strain (GLS) in basal segments has been recently described in patients hospitalized with diagnosis of COVID-19, the correlation of these findings with the persistence of symptoms has not been determined. Objectives: The objective of this study is to describe echocardiographic findings in patients recovered from COVID-19 and to determine its association with persistent dyspnea. Methods: Seventy-six patients were analyzed. Forty-six were asymptomatic (group N°1) and 30 referred persistent dyspnea at the time of examination (group N°2), and a cohort of 25 healthy individuals was included as a control group. Total LV GLS, average basal LV GLS, and average mid-apical LV GLS were assessed. Basal-mid-apical index (BMAI) was calculated. A difference in BMAI bigger than 15% between both groups was stablished as cutoff point. Results: Nonsignificative differences on standard echocardiographic measurements were found between the analyzed groups. When compared to the control group, there was nonsignificative reduction on basal LV GLS values in patients with persistent dyspnea. However, BMAI values were bigger than 15% in 70% of patients from group N°2 but in none of the patients from the control group ( = 0.0002). Conclusions: This new index allowed to identify an abnormal relation in LV GLS values between basal and medium-apical segments among patients recovered from COVID-19 and persistent dyspnea.

Extensive pulmonary venous stenoses as a complication of radiofrequency catheter ablation for atrial fibrillation.

Although pulmonary veins stenosis (PVS) is a well documented complication of radiofrequency-catheter ablation (RFCA) of atrial fibrillation (AF), simultaneous involvement of multiple PVs is extremely rare. We present the case of a 69 years-old male patient, with prior medical history of persistent AF, who had been treated with RFCA two years ago. After RFCA, he started with shortness of breath and needed hospitalization for bilateral pneumonia. One year after the procedure, he was on home oxygen, but still referred dyspnea, cough and hemoptysis. A transthoracic echocardiogram showed moderate right ventricular (RV) systolic dysfunction and elevated RV systolic pressure. Dedicated cardiac tomography for PV assessment revealed severe narrowing and pre-stenotic engorgement of all 5 PVs, with subtotal ostial occlusion of both the left lower and right middle PVs. PV angiography confirmed the diagnosis. Only the left and right upper PV were able to be wire-crossed and stented, with substantial reductions in stenosis from 90 % to 10 %. After 3 months of follow-up, the patient improved substantially, and home O2 was withdrawn.

Three-Dimensional Echocardiographic Deconstruction: Feasibility of Clinical Evaluation from Two-Dimensional Views Derived from a Three-Dimensional Data Set.

BACKGROUND: Three-dimensional echocardiography (3DE) makes it possible to capture the entire heart in a single data set that theoretically could be used to extract any two-dimensional (2D) views and potentially replace the standard practice of serial 2D acquisitions. The aim of this study was to test the hypothesis that the quality of 3DE-derived 2D images is sufficient to allow the visualization of the left ventricular (LV), right ventricular (RV), and left atrial (LA) endocardium, on par with images from conventional two-dimensional echocardiography (2DE), and potentially more accurate quantification of chamber size and function. METHODS: First, the investigators prospectively studied 36 patients who underwent 2DE in 14 standard views, and full-volume data sets from 3DE, from which the same views were extracted offline. The ability to visualize the LV endocardium, RV free wall, and LA endocardium was scored. LV linear dimensions, LV volumes, and LV ejection fraction (LVEF), LA volume, and RV basal dimension were measured and compared between both types of images. Thereafter, 40 patients who underwent 2DE, 3DE, and cardiac magnetic resonance (CMR) imaging on the same day were retrospectively studied. LV volumes and LVEF derived from 2DE and 3DE were compared side by side against the CMR reference. RESULTS: Intertechnique agreement in visualization scores was 87% for LV segments, 86% for the RV free wall, and 83% for the LA endocardium. The correlations between 2DE- and 3DE-derived measurements were 0.95, 0.97, and 0.97 for LV volumes and LVEF, respectively, and 0.88 for RV basal dimension. Three-dimensional echocardiography-derived measurements of LV volumes and LVEF were more similar to those on CMR than those obtained on conventional 2DE. CONCLUSIONS: The feasibility of segmental assessment of cardiac chambers using 3DE-derived 2D views is similar to that using conventional 2DE. This approach provides similar quantitative information, including more accurate LV volumes and LVEF measurements compared with CMR, and thus promises to significantly shorten the duration of the echocardiographic examination.

Three-Dimensional Transthoracic Static and Dynamic Normative Values of the Mitral Valve Apparatus: Results from the Multicenter World Alliance Societies of Echocardiography Study.

BACKGROUND: Recent advances in mitral valve (MV) percutaneous interventions have escalated the need for a more quantitative and comprehensive assessment of the MV, which can be best achieved using three-dimensional echocardiography. Understanding normal valve size, structure, and function is essential for differentiation of healthy from disease states. The aims of this study were to establish normative values for MV apparatus size and morphology and to determine how they vary across age, sex, and race groups using data from the World Alliance Societies of Echocardiography Normal Values Study. METHODS: Three-dimensional volumetric data sets obtained on transthoracic echocardiography in 748 normal subjects (51% men) were analyzed using commercial MV analysis software (TomTec Imaging Systems) to determine annular and leaflet dimensions and areas. The subjects were divided into groups by sex (378 men and 370 women) and age (18 to 40 years [n = 266], 41 to 65 years [n = 249], and >65 years [n = 233]) to identify sex- and age-related differences. In addition, differences among black, white, and Asian populations were studied. Inter- and intraobserver variability was assessed in a subset of 30 subjects and expressed as mean absolute difference between pairs of repeated measurements. RESULTS: Compared with women, men had larger annular size measurements, larger tenting size parameters, and larger leaflet length and area. Compared with the black and white populations, the Asian population showed significantly smaller mitral annular size. Although many of the age, sex, and race differences in MV parameters were statistically significant, they were comparable with or smaller than the corresponding measurement variability. Indexing to body surface area and height did not eliminate these differences consistently, suggesting that parameters may need to be indexed according to their dimensionality. CONCLUSIONS: This analysis of the World Alliance Societies of Echocardiography data provides normative values of mitral apparatus size and morphology. Although sex- and age-related differences were noted, they need to be interpreted with caution in view of the associated measurement variability.

Toxoplasmosis and the Heart.

Toxoplasmosis is a common disease caused by Toxoplasma gondii, a parasite with high prevalence in tropical regions. Most infections show minimal symptoms, but immunocompromised patients tend to have a poor prognosis. Cardiovascular manifestations in toxoplasmosis are rare and reported in a limited number of patients. As part of the "Neglected Tropical Diseases and Other Infectious Diseases Affecting the Heart" (NET-Heart) project, this paper aims to systematically review all available information regarding the cardiovascular implications of toxoplasmosis. Relevant studies were identified in the MEDLINE and/or PubMed database, and 48 articles were ultimately included. This was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Cardiac compromise in toxoplasmosis mainly involves myocarditis, and complications vary widely in severity. Toxoplasmic myocarditis is challenging to diagnose, as endomyocardial biopsy is usually required. This article provides a summary of cardiac toxoplasmosis, including an original algorithm facilitating diagnosis and treatment.

Aortic and Mitral Disease due to an Unusual Etiology.

Relapsing polychondritis (RP) is a rare multisystem disease characterized by inflammation in cartilaginous structures and other connective tissues throughout the body, affecting the ears, nose, eyes, joints, respiratory tract, heart, and blood vessels. Cardiovascular involvement is the second most common cause of mortality after laryngotracheal involvement.[1] Here, we report a successful surgical case of RP in which the patient underwent aortic and mitral valve replacement and concomitant coronary artery bypass grafting.

Optimización de la interpretación de ecocardiogramas con la utilización de Machine Learning en el estudio WASE / Optimizing Echocardiograms Interpretation with the Application of Machine Learning in the WASE Study

RESUMEN Introducción: El número creciente de estudios ecocardiográficos y la necesidad de cumplir rigurosamente con las recomendaciones de guías internacionales de cuantificación, ha llevado a que los cardiólogos deban realizar tareas sumamente extensas y repetitivas, como parte de la interpretación y análisis de cantidades de información cada vez más abrumadoras. Novedosas técnicas de machine learning (ML), diseñadas para reconocer imágenes y realizar mediciones en las vistas adecuadas, están siendo cada vez más utilizadas para responder a esta necesidad evidente de automatización de procesos. Objetivos: Nuestro objetivo fue evaluar un modelo alternativo de interpretación y análisis de estudios ecocardiográficos, basado fundamentalmente en la utilización de software de ML, capaz de identificar y clasificar vistas y realizar mediciones estandarizadas de forma automática. Material y métodos: Se utilizaron imágenes obtenidas en 2000 sujetos normales, libres de enfermedad, de los cuales 1800 fueron utilizados para desarrollar los algoritmos de ML y 200 para su validación posterior. Primero, una red neuronal convolucional fue desarrollada para reconocer 18 vistas ecocardiográficas estándar y clasificarlas de acuerdo con 8 grupos (stacks) temáticos. Los resultados de la identificación automática fueron comparados con la clasificación realizada por expertos. Luego, algoritmos de ML fueron desarrollados para medir automáticamente 16 parámetros de eco Doppler de evaluación clínica habitual, los cuales fueron comparados con las mediciones realizadas por un lector experto. Finalmente, comparamos el tiempo necesario para completar el análisis de un estudio ecocardiográfico con la utilización de métodos manuales convencionales, con el tiempo necesario con el empleo del modelo que incorpora ML en la clasificación de imágenes y mediciones ecocardiográficas iniciales. La variabilidad inter e intraobservador también fue analizada. Resultados: La clasificación automática de vistas fue posible en menos de 1 segundo por estudio, con una precisión de 90 % en imágenes 2D y de 94 % en imágenes Doppler. La agrupación de imágenes en stacks tuvo una precisión de 91 %, y fue posible completar dichos grupos con las imágenes necesarias en 99% de los casos. La concordancia con expertos fue excelente, con diferencias similares a las observadas entre dos lectores humanos. La incorporación de ML en la clasificación y medición de imágenes ecocardiográficas redujo un 41 % el tiempo de análisis y demostró menor variabilidad que la metodología de interpretación convencional. Conclusión: La incorporación de técnicas de ML puede mejorar significativamente la reproducibilidad y eficiencia de las interpretaciones y mediciones ecocardiográficas. La implementación de este tipo de tecnologías en la práctica clínica podría resultar en reducción de costos y aumento en la satisfacción del personal médico.

ABSTRACT Background: The growing number of echocardiographic tests and the need for strict adherence to international quantification guidelines have forced cardiologists to perform highly extended and repetitive tasks when interpreting and analyzing increasingly overwhelming amounts of data. Novel machine learning (ML) techniques, designed to identify images and perform measurements at relevant visits, are becoming more common to meet this obvious need for process automation. Objectives: Our objective was to evaluate an alternative model for the interpretation and analysis of echocardiographic tests mostly based on the use of ML software in order to identify and classify views and perform standardized measurements automatically. Methods: Images came from 2000 healthy subjects, 1800 of whom were used to develop ML algorithms and 200 for subsequent validation. First, a convolutional neural network was developed in order to identify 18 standard echocardiographic views and classify them based on 8 thematic groups (stacks). The results of automatic identification were compared to classification by experts. Later, ML algorithms were developed to automatically measure 16 Doppler scan parameters for regular clinical evaluation, which were compared to measurements by an expert reader. Finally, we compared the time required to complete the analysis of an echocardiographic test using conventional manual methods with the time needed when using the ML model to classify images and perform initial echocardiographic measurements. Inter- and intra-observer variability was also analyzed. Results: Automatic view classification was possible in less than 1 second per test, with a 90% accuracy for 2D images and a 94% accuracy for Doppler scan images. Stacking images had a 91% accuracy, and it was possible to complete the groups with any necessary images in 99% of cases. Expert agreement was outstanding, with discrepancies similar to those found between two human readers. Applying ML to echocardiographic imaging classification and measurement reduced time of analysis by 41% and showed lower variability than conventional reading methods. Conclusion: Application of ML techniques may significantly improve reproducibility and efficiency of echocardiographic interpretations and measurements. Using this type of technologies in clinical practice may lead to reduced costs and increased medical staff satisfaction.