Three main mechanisms contribute to global right ventricular (RV) function: longitudinal shortening, radial displacement of the RV free wall (bellows effect), and anteroposterior shortening (as a consequence of left ventricular contraction). Since the importance of these mechanisms may vary in different cardiac conditions, a technology being able to assess their relative influence on the global RV pump function could help to clarify the pathophysiology and the mechanical adaptation of the chamber. Previously, we have introduced our 3D echocardiography (3DE)-based solution—the Right VentrIcular Separate wall motIon quantificatiON (ReVISION) method—for the quantification of the relative contribution of the three aforementioned mechanisms to global RV ejection fraction (EF). Since then, our approach has been applied in several clinical scenarios, and its strengths have been demonstrated in the in-depth characterization of RV mechanical pattern and the prognostication of patients even in the face of maintained RV EF. Recently, various new features have been implemented in our software solution to enable the convenient, standardized, and more comprehensive analysis of RV function. Accordingly, in our current technical paper, we aim to provide a detailed description of the latest version of the ReVISION method with special regards to the volumetric partitioning of the RV and the calculation of longitudinal, circumferential, and area strains using 3DE datasets. We also report the results of the comparison between 3DE- and cardiac magnetic resonance imaging-derived RV parameters, where we found a robust agreement in our advanced 3D metrics between the two modalities. In conclusion, the ReVISION method may provide novel insights into global and also segmental RV function by defining parameters that are potentially more sensitive and predictive compared to conventional echocardiographic measurements in the context of different cardiac diseases.

Background: The relative importance of variables explaining sex-related differences in outcomes is scarcely explored in patients undergoing cardiac resynchronization therapy (CRT). We sought to implement and evaluate machine learning (ML) algorithms for the prediction of 1- and 3-year all-cause mortality in CRT patients. We also aimed to assess the sex-specific differences in predictors of mortality utilizing ML.Methods: Using a retrospective registry of 2,191 CRT patients, ML models were implemented in 6 partially overlapping patient subsets (all patients, females, or males with 1- or 3-year follow-up). Each cohort was randomly split into training (80%) and test sets (20%). After hyperparameter tuning in the training sets, the best performing algorithm was evaluated in the test sets. Model discrimination was quantified using the area under the receiver-operating characteristic curves (AUC). The most important predictors were identified using the permutation feature importances method.Results: Conditional inference random forest exhibited the best performance with AUCs of 0.728 (0.645–0.802) and 0.732 (0.681–0.784) for the prediction of 1- and 3-year mortality, respectively. Etiology of heart failure, NYHA class, left ventricular ejection fraction, and QRS morphology had higher predictive power, whereas hemoglobin was less important in females compared to males. The importance of atrial fibrillation and age increased, while the importance of serum creatinine decreased from 1- to 3-year follow-up in both sexes.Conclusions: Using ML techniques in combination with easily obtainable clinical features, our models effectively predicted 1- and 3-year all-cause mortality in CRT patients. Sex-specific patterns of predictors were identified, showing a dynamic variation over time.

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Background The relative importance of variables explaining sex differences in outcomes is scarcely explored in patients undergoing cardiac resynchronization therapy (CRT). Purpose We sought to implement and evaluate machine learning (ML) algorithms for the prediction of 1- and 3-year all-cause mortality in patients undergoing CRT implantation. We also aimed to assess the sex-specific differences and similarities in the predictors of mortality using ML approaches. Methods A retrospective registry of 2191 CRT patients (75% males) was used in the current analysis. ML models were implemented in 6 partially overlapping patient subsets (all patients, females or males with 1- or 3-year follow-up data available). Each cohort was randomly split into a training (80%) and a test set (20%). After hyperparameter tuning with 10-fold cross-validation in the training set, the best performing algorithm was also evaluated in the test set. Model discrimination was quantified using the area under the receiver-operating characteristic curves (AUC) and the associated 95% confidence intervals. The most important predictors were identified using the permutation feature importances method. Results Conditional inference random forest exhibited the best performance with AUCs of 0.728 [0.645–0.802] and 0.732 [0.681–0.784] for the prediction of 1- and 3-year mortality, respectively. Etiology of heart failure, NYHA class, left ventricular ejection fraction and QRS morphology had higher predictive power in females, whereas hemoglobin was less important than in males. The importance of atrial fibrillation and age increased, whereas the relevance of serum creatinine decreased from 1- to 3-year follow-up in both sexes. Conclusions Using advanced ML techniques in combination with easily obtainable clinical features, our models effectively predicted 1- and 3-year all-cause mortality in patients undergoing CRT implantation. The in-depth analysis of features has revealed marked sex differences in mortality predictors. These results support the use of ML-based approaches for the risk stratification of patients undergoing CRT implantation. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): National Research, Development and Innovation Office of Hungary

Funding Acknowledgements Type of funding sources: None. On top of global ventricular function, segmental metrics may bear clinically relevant information. Concerning the left ventricle (LV), standardized segmentation is widely performed in different cardiovascular imaging modalities mainly to correlate regional dysfunction with coronary perfusion territories, or to appreciate and quantify distinct patterns in LV myocardial function. The same applies to the right ventricle (RV); as pulmonary hypertension, or arrhythmogenic cardiomyopathy are just two clinical examples among several others, where established regional dysfunction exists. Nevertheless, only a few options are available for the comprehensive and quantitative assessment of the segmental RV function due to its complex three-dimensional (3D) shape. Therefore, our aim was to develop a 3D echocardiographic software solution for volumetric partitioning of the RV using a 15-segment model and to investigate a large number of healthy volunteers to describe the normal segmental pattern. One hundred and fifty healthy adults with a balanced age range and an equal sex distribution were investigated (15-15 women and men in each age groups: 20-29, 30-39, 40-49, 50-59, 60+). Beyond standard two-dimensional echocardiographic protocol, full volume 3D datasets were acquired. Using commercially available software, we reconstructed the 3D mesh model of the RV and measured end-diastolic (EDV), end-systolic volumes and ejection fraction (EF). The 3D model was post-processed using the ReVISION method to calculate regional and segmental volumes and EFs. Fifteen standard segments were separated and quantified (Figure). Increasing age resulted in significantly lower RV stroke volume (r=-0.17; p < 0.05) and tended towards lower RV EDV (r=-0.15, p = 0.06). EDVs of inflow tract and outflow tract segments decreased during aging (r=-0.21, p < 0.05 and r=-0.26, p < 0.01, respectively). Between the pre-specified age groups, there was no difference concerning global RVEF (ANOVA p = NS). In the 50-59 age group, regional EF of septal segments and also free wall segments were significantly lower compared to subjects in the 30-39 and 40-49 age categories (both p < 0.05). Global RV EDV was significantly lower in women (women vs. men: 95 ± 20 vs. 125 ± 28 ml; p < 0.05) along with a higher RV EF compared to men (62 ± 4 vs. 59 ± 4; p < 0.05). However, segmental EFs of apical, septal mid anterior, free wall mid posterior, free wall mid lateral, septal basal anterior and inflow tract segments were comparable between genders. The ReVISION method allows a volumetric partitioning of the RV 3D models to investigate segmental geometry and function in a 15-segment model. We have explored segmental differences between different ages and genders. Further studies are warranted to justify the importance of segmental assessment of the RV in different cardiac diseases. Abstract Figure. Separation of 15 standard RV segments

Automatic situation understanding in videos has improved remarkably in recent years. However, state-of-the-art methods still have considerable shortcomings: they usually require training data for each object class present and may have high false positive or false negative rates, making them impractical for general applications. We study a case that has a limited goal in a narrow context and argue about the complexity of the general problem. We suggest to solve this problem by including common sense rules and by exploiting various state-of-the art deep neural networks (DNNs) as the detectors of the conditions of those rules. We want to deal with the manipulation of unknown objects at a remote table. We have two action types to be detected: `picking up an object from the table' and `putting an object onto the table' and due to remote monitoring, we consider monocular observation. We quantitatively evaluate the performance of the system on manually annotated video segments, present precision and recall scores. We also discuss issues on machine reasoning. We conclude that the proposed neural-symbolic approach a) diminishes the required size of training data and b) enables new applications where labeled data are difficult or expensive to get.

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