Left ventricular ejection fraction (LVEF) measurement in echocardiography (Echo) using the recommended modified biplane Simpson (MBS) method is operator-dependent and exhibits variability. We aimed to assess the accuracy of a novel fully automated (Auto) artificial intelligence (AI) in view selection and biplane LVEF calculation compared to MBS-Echo, with cardiac magnetic resonance imaging (CMR) as reference. Each of the 301 consecutive patients underwent CMR and Echo on the same day. LVEF was measured independently by Auto-Echo, MBS-Echo and CMR. Interobserver (n = 40) and test-retest (n = 14) analysis followed. A total of 229 patients (76%) underwent complete analysis. Auto-Echo and MBS-Echo showed high correlations with CMR (R = 0.89 and 0.89) and with each other (R = 0.93). Auto underestimated LVEF (bias: 2.2%; limits of agreement [LOA]: -13.5 to 17.9%), while MBS overestimated it (bias: -2.2%; LOA: 18.6 to 14.1%). Despite comparable areas under the curves of Auto- and MBS-Echo (0.93 and 0.92), 46% (n = 70) of MBS-Echo misclassified LVEF by ≥5% units in patients with a reduced CMR-LVEF <51%. Although LVEF bias variability across different LV function ranges was significant (p < 0.001), Auto-Echo was closer to CMR for patients with reduced LVEF, wall motion abnormalities, and poor image quality than MBS-Echo. The interobserver correlation coefficient of Auto-Echo was excellent compared to MBS-Echo (1.00 vs. <0.91) for different readers. True test-retest variability was higher for MBS-Echo than for Auto-Echo (7.9% vs. 2.5%). The tested AI has the potential to improve the clinical utility of Echo by reducing user-related variability, providing more accurate and reliable results than MBS.

Left ventricular ejection fraction (LVEF) is a key parameter in evaluating left ventricular (LV) function using echocardiography (Echo), but its manual measurement by the modified biplane Simpson (MBS) method is time consuming and operator dependent. We investigated the feasibility of a server-based, commercially available and ready-to use-artificial intelligence (AI) application based on convolutional neural network methods that integrate fully automatic view selection and measurement of LVEF from an entire Echo exam into a single workflow. We prospectively enrolled 1083 consecutive patients who had been referred to Echo for diagnostic or therapeutic purposes. LVEF was measured independently using MBS and AI. Test–retest variability was assessed in 40 patients. The reliability, repeatability, and time efficiency of LVEF measurements were compared between the two methods. Overall, 889 Echos were analyzed by cardiologists with the MBS method and by the AI. Over the study period of 10 weeks, the feasibility of both automatic view classification and seamlessly measured LVEF rose to 81% without user involvement. LVEF, LV end-diastolic and end-systolic volumes correlated strongly between MBS and AI (R = 0.87, 0.89 and 0.93, p < 0.001 for all) with a mean bias of +4.5% EF, −12 mL and −11 mL, respectively, due to impaired image quality and the extent of LV function. Repeatability and reliability of LVEF measurement (n = 40, test–retest) by AI was excellent compared to MBS (coefficient of variation: 3.2% vs. 5.9%), although the median analysis time of the AI was longer than that of the operator-dependent MBS method (258 s vs. 171 s). This AI has succeeded in identifying apical LV views and measuring EF in one workflow with comparable results to the MBS method and shows excellent reproducibility. It offers realistic perspectives for fully automated AI-based measurement of LVEF in routine clinical settings.

The clinical effect of copper accumulation on the heart of patients suffering from Wilson’s disease (WD) is not completely understood. We aimed to determine if patients with WD show signs of cardiac involvement, structural heart disease or autonomic dysfunction. In this prospective trial, we studied 61 patients (mean age 44.3 ± 15.2 years, 51% males) with WD and compared them to 61 age- and gender-matched healthy controls. All subjects underwent clinical examination, blood tests, echocardiography and 24 h electrocardiographic (ECG) recording. Left- and right ventricular systolic function did not differ significantly between WD patients and controls. However, 5 of the 61 patients had a reduced left ventricular ejection fraction (LVEF). Furthermore, diastolic dysfunction was more prevalent in WD patients (9 of 61 vs. 0 of 61, p = 0.001). The severity of WD based on the Unified Wilson’s Disease Rating Scale was significantly correlated to NT-pro BNP (r = 0.34, P = 0.013). Patients with an exacerbation of WD in medical history had higher troponin levels compared to those without (11.3 ± 4.7 vs 4.6 ± 1.2). The autonomic function assessed by triangular index (TI) and SDNN-index was significantly reduced in WD patients compared to controls in most in almost every age category (p-value TI and SDNN: age 20–29, p < 0.001 and 0.05; age 30–39, p < 0.01 and not significant (ns); age 40–49, p < 0,01 and 0.001; age 50–59, p = ns and < 0.001, age 60–70, p < 0.05 and ns). Our data demonstrate that cardiac involvement and autonomic dysfunction in WD is possible, however the underlying cause is still not known. We suggest that patients with signs and symptoms of structural heart disease should be examined by a cardiologist in addition to the interdisciplinary treatment team of WD.

Background:LV twist has a key role in maintaining left ventricular (LV) contractility during exercise. The purpose of this study was to investigate LV torsion instead of twist as a surrogate marker of peak oxygen uptake (peak V̇O₂) assessed by cardiopulmonary exercise testing (CPET) in patients with non-ischemic dilated cardiomyopathy (DCM). Methods and Results:We evaluated 45 outpatients with DCM (50±12 years, 24% females) with 3D speckle-tracking electrocardiography prior to CPET. LV torsion, LV ejection fraction (EF), LV diastolic function, LV global longitudinal (GLS) and circumferential (GCS) strain were quantified. A reduced functional capacity (FC) was defined as a peak V̇O₂<20 mL/kg/min. LV torsion correlated most strongly with peak V̇O₂(r=0.76, P<0.001). LV torsion instead of twist was an independent predictor of peak V̇O₂(B: 0.59 to 0.71, P<0.001) in multivariable analyses. Impaired LV torsion <0.61 degrees/cm was able to predict a reduced FC with higher sensitivity and specificity (0.91 and 0.81; area under the curve (AUC): 0.88, P<0.001) than LV EF, GLS or GCS (AUC 0.64, 0.63 and 0.66; P<0.05 for differences in AUC). Conclusions:Peak V̇O₂correlated more strongly with LV torsion than with LV diastolic function, LV EF, GLS or GCS. LV torsion had high accuracy in identifying patients with a reduced FC.

Aims: To evaluate the accuracy and feasibility of artificial intelligence (AI) in left ventricular global longitudinal strain (GLS) analysis as compared to conventional (Manual) and semi-automated (SemiAuto) method in echocardiography (Echo). Methods and results: GLS validation was performed on 550 standard Echo exams by expert cardiologists. The performance of a beginner cardiologist without experience of GLS analysis was assessed on a subset of 90 exams. The AI employs fully automated view selection, classification, endocardial border tracing, and calculation of GLS from an entire Echo exam, while SemiAuto requires manual chamber view selection, and Manual involves full user input. Interobserver agreement was assessed using the intraclass correlation coefficient (ICC) for all three methods. Agreement of measures included Pearson’s correlation (R) and Bland-Altman analysis [median bias; limits of agreement (LOA)]. With an 89% feasibility the AI showed good agreement with Manual (R = 0.92, bias = 0.7% and LOA: −3.5 to 4.8%) and with SemiAuto (r = 0.90, bias = 0.10% and LOA: −4.5 to 4%). ICCs for GLS were 1.0 for AI, 0.93 for SemiAuto, and 0.80 for Manual. After the 55th analysis, the beginner showed stable time performance with Manual (171 s), contrasting with the consistent performance of SemiAuto (85-69 s) from the beginning. The highest agreement between beginner and expert readers was achieved with AI (R = 1.00), followed by SemiAuto (R = 0.85) and Manual (R = 0.74). Conclusion: Automated GLS analysis enhances efficiency and accuracy in cardiac diagnostics, particularly for novice users. Integration of automated solutions into routine clinical practice could yield more standardized results.