Objective To quantitatively assess left ventricular global systolic and diastolic function using volume-time curves (VTC) by real-time three-dimensional echoeardiography(RT-3DE).Methods Ninty-eight subjects were divided into four groups.Group A consisted of twenty-eight normal subject,group B included twenty-four patients with hypertensive(HTN), group C consisted of twenty-four patients with coronary artery disease(CAD) and group D of twenty-two patients having dilated cordiomyopathy (DCM).Participants were selected undergoing full volume RT-3DE.The global and 17-segmental VTCs were obtained by the off-line Qlab software.The end-diastolic volume(EDV), end-systolic volume(ESV) and ejection fraction(EF) were derived from VTCs.The peak ejection rate(PER),peak early filling rate (PFR),PER/EDV and PFR/EDV were calculted.Results EDV and ESV of group B,C and D was significantly larger than that of group A(all P < 0.05), EF and PER/EDV of group C and D significantly lower than those of group A.There were close correlation between PER/EDV and EF ( r=0.694, P＜0.05).Comparison of VTC pattern of HTN,CAD and DCM with that of healthy participants revealed the loss of symmetry of systolic and diastolic pattern.Conclusions Generation of VTCs by RT3DE is a promising approach to evaluate left ventricular global systolic and diastolic function.PER/EDV and PFR/EDV may be potential parameters for assessing left ventricular global systolic and diastolic function.

Objective:To evaluate the detection performance of left ventricular ejection fraction (LVEF) in patients with chronic renal failure (CRF) by an artificial intelligence (AI) model based on yolox framework integrating left ventricular segmentation and critical point detection.Methods:From January 2019 to June 2023, a total of 4 284 echocardiographic images of 2 000 adults aged 18-80 years without segmental wall motion abnormalities, structural heart disease, cardiac surgery or cardiomyopathy were collected in Henan Provincial People′s Hospital to delineate the endocardial membrane, as a training set, an AI model based on yolox framework integrating left ventricular segmentation and critical point detection was established. The images were divided into the training set( n=1 675) and the test set( n=325) in a ratio of about 5∶1. All 228 echocardiographic images of 100 normal adult volunteers who were treated in Henan Provincial Chest Hospital from May 2020 to May 2021 were collected as external test set validation. All 792 echocardiographic images of 204 patients treated in Henan Provincial People′s Hospital from April 2019 to June 2023 were continuously enrolled to evaluate the measurement efficiency of AI model. Spearman correlation statistical method was used to analyze the consistency of AI model measurement with manual measurement and TomTec software measurement methods of 3 senior echocardiographic professionals. Subjects were divided into clear image group, unclear image group, normal LVEF group and reduced LVEF group, the differences of general data between the two groups were compared. The correlation coefficient(ICC) within the group was calculated to analyze the consistency, so as to evaluate the model performance. Results:LVEF measured by AI model was significantly correlated with both manual measurement and TomTec model measurement ( rs=0.834, 0.826; all P<0.01). ICC values of the clear image group and the unclear image group were 0.96 and 0.97, respectively. ICC values for all subjects, normal LVEF group and reduced LVEF group were 0.96, 0.90 and 0.96, respectively. Conclusions:The AI model based on yolox framework integrating left ventricular segmentation and critical point detection has good diagnostic performance in the automatic measurement of LVEF in patients with CRF.