Artificial Intelligence-Based Assessment of Indices of Right Ventricular Function

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.

Structural design and mechanical performance of composite vascular grafts

This study reviews the state of the art in structural design and the corresponding mechanical behaviours of composite vascular grafts. We critically analyse surface and matrix designs composed of layered, embedded, and hybrid structures along the radial and longitudinal directions; materials and manufacturing techniques, such as tissue engineering and the use of textiles or their combinations; and the corresponding mechanical behaviours of composite vascular grafts in terms of their physical–mechanical properties, especially their stress–strain relationships and elastic recovery. The role of computational studies is discussed with respect to optimizing the geometrics designs and the corresponding mechanical behaviours to satisfy specialized applications, such as those for the aorta and its subparts. Natural and synthetic endothelial materials yield improvements in the mechanical and biological compliance of composite graft surfaces with host arteries. Moreover, the diameter, wall thickness, stiffness, compliance, tensile strength, elasticity, and burst strength of the graft matrix are determined depending on the application and the patient. For composite vascular grafts, hybrid architectures are recommended featuring multiple layers, dimensions, and materials to achieve the desired optimal flexibility and function for complying with user-specific requirements. Rapidly emerging artificial intelligence and big data techniques for diagnostics and the three-dimensional (3D) manufacturing of vascular grafts will likely yield highly compliant, subject-specific, long-lasting, and economical vascular grafts in the near-future.

Graphic abstract

Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review

Background

Machine learning (ML) is developing fast with promising prospects within medicine and already has several applications in perioperative care. We conducted a scoping review to examine the extent and potential limitations of ML implementation in perioperative anesthetic care, specifically in cardiac surgery patients.

Methods

We mapped the current literature by searching three databases: MEDLINE (Ovid), EMBASE (Ovid), and Cochrane Library. Articles were eligible if they reported on perioperative ML use in the field of cardiac surgery with relevance to anesthetic practices. Data on the applicability of ML and comparability to conventional statistical methods were extracted.

Results

Forty-six articles on ML relevant to the work of the anesthesiologist in cardiac surgery were identified. Three main categories emerged: (I) event and risk prediction, (II) hemodynamic monitoring, and (III) automation of echocardiography. Prediction models based on ML tend to behave similarly to conventional statistical methods. Using dynamic hemodynamic or ultrasound data in ML models, however, shifts the potential to promising results.

Conclusions

ML in cardiac surgery is increasingly used in perioperative anesthetic management. The majority is used for prediction purposes similar to conventional clinical scores. Remarkable ML model performances are achieved when using real-time dynamic parameters. However, beneficial clinical outcomes of ML integration have yet to be determined. Nonetheless, the first steps introducing ML in perioperative anesthetic care for cardiac surgery have been taken.

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.

The Year in Perioperative Echocardiography: Selected Highlights from 2020

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

Inferior vena cava dilation predicts global cardiac dysfunction in acute respiratory distress syndrome: A strain echocardiographic study

PURPOSE

Limited data exist on the utility of ultrasonographic evaluation of inferior vena cava (IVC) in acute respiratory distress syndrome (ARDS). We studied the value of IVC diameter in assessing cardio-circulatory performance in ARDS using strain echocardiography.

MATERIALS AND METHODS

Retrospective cross-sectional analysis of Doppler echocardiograms of patients with moderate-severe ARDS was performed. Right ventricle (RV) parameters, IVC diameter, and left ventricle (LV) systolic and diastolic parameters were collected. RV free wall strain (RVFWS) and LV global longitudinal strain (LVGLS) were calculated.

RESULTS

Fifty-one patients were dichotomized into two groups: with IVC > 2.1 cm (dilated) and with IVC ≤ 2.1 cm (nondilated). The dilated IVC group presented worse hypoxemic profile, hypotension, and poor perfusion markers. No significant associations with positive end-expiratory pressure or lung mechanics were observed. Dilated IVC was associated with impaired RV function, high central venous pressure, elevated pulmonary artery pressure, and LV systolic and diastolic dysfunctions. Strongest predictors of a dilated IVC were RVFWS, LVGLS, and tissue Doppler mitral annular early diastolic velocity. Dilated IVC predicted a global cardiac dysfunction defined by strain echocardiography (GCDS) with high sensitivity and specificity.

CONCLUSIONS

In ARDS, strain echocardiography analyses demonstrated that a dilated IVC is associated with GCDS and impaired hemodynamics independent of lung mechanics. A dilated IVC should be considered a marker of circulatory distress, signaling the potential necessity for improved hemodynamic optimization.

Artificial Intelligence in Echocardiography for Anesthesiologists

Echocardiography is a unique diagnostic tool for intraoperative monitoring and assessment of patients with cardiovascular diseases. However, there are high levels of interoperator variations in echocardiography interpretations that could lead to inaccurate diagnosis and incorrect treatment. Furthermore, anesthesiologists are faced with the additional challenge to interpret echocardiography and make decisions in a limited timeframe from these complex data. The need for an automated, less operator-dependent process that enhances speed and accuracy of echocardiography analysis is crucial for anesthesiologists. Artificial intelligence is playing an increasingly important role in the medical field and could help anesthesiologists analyze complex echocardiographic data while adding increased accuracy and consistency to interpretation. This review aims to summarize practical use of artificial intelligence in echocardiography and discusses potential limitations and challenges in the future for anesthesiologists.