Artificial Intelligence-Based Echocardiographic Left Atrial Volume Measurement with Pulmonary Vein Comparison

Abstract

This paper combines echocardiographic signal processing and artificial intelligence technology to propose a deep neural network model adapted to echocardiographic signals to achieve left atrial volume measurement and automatic assessment of pulmonary veins efficiently and quickly. Based on the echocardiographic signal generation mechanism and detection method, an experimental scheme for the echocardiographic signal acquisition was designed. The echocardiographic signal data of healthy subjects were measured in four different experimental states, and a database of left atrial volume measurements and pulmonary veins was constructed. Combining the correspondence between ECG signals and echocardiographic signals in the time domain, a series of preprocessing such as denoising, feature point localization, and segmentation of the cardiac cycle was realized by wavelet transform and threshold method to complete the data collection. This paper proposes a comparative model based on artificial intelligence, adapts to the characteristics of one-dimensional time-series echocardiographic signals, automatically extracts the deep features of echocardiographic signals, effectively reduces the subjective influence of manual feature selection, and realizes the automatic classification and evaluation of human left atrial volume measurement and pulmonary veins under different states. The experimental results show that the proposed BP neural network model has good adaptability and classification performance in the tasks of LV volume measurement and pulmonary vein automatic classification evaluation and achieves an average test accuracy of over 96.58%. The average root-mean-square error percentage of signal compression is only 0.65% by extracting the coding features of the original echocardiographic signal through the convolutional autoencoder, which completes the signal compression with low loss. Comparing the training time and classification accuracy of the LSTM network with the original signal and encoded features, the experimental results show that the AI model can greatly reduce the model training time cost and achieve an average accuracy of 97.97% in the test set and increase the real-time performance of the left atrial volume measurement and pulmonary vein evaluation as well as the security of the data transmission process, which is very important for the comparison of left atrial volume measurement and pulmonary vein. It is of great practical importance to compare left atrial volume measurements with pulmonary veins.

Inferior vena cava distensibility from subcostal and trans-hepatic imaging using both M-mode or artificial intelligence: a prospective study on mechanically ventilated patients.

“…The use of AI has been applied to the whole echocardiography setting (i.e. left ventricular systolic 23,24 and diastolic [25][26][27] function, to right ventricular function 28 , assessment of heart valve diseases 29 , diagnosis of congenital heart diseases 30 ) and also to predict of FR, with encouraging results. For instance, Bataille et al 31 showed that machine learning models predicted FR with comparable accuracy to the hemodynamic response to passive leg raising, and evaluation of the IVC was among the key variables identified by the model, together with other Doppler derived parameters.…”

“…Among these, also echocardiography is experiencing a significant expansion of AI applications that might help daily practice. Indeed, AI has been used for the assessment of left ventricular systolic 23,24 and diastolic [25][26][27] function, right ventricular function 28 , but also for the evaluation of heart valve 29 and congenital heart diseases 30 . Moreover, machine learning has been developed for predicting FR at patient's bedside 31 with preliminary data on the implementation of AI for IVC assessment 32 .…”

Inferior vena cava distensibility from subcostal and trans-hepatic imaging using both M-mode or artificial intelligence: a prospective study on mechanically ventilated patients

“…Among these, also echocardiography is experiencing a significant expansion of AI applications that might help daily practice. Indeed, AI has been used for the assessment of left ventricular systolic [ 23 , 24 ] and diastolic [ 25 – 27 ] function, right ventricular function [ 28 ], but also for the evaluation of heart valve [ 29 ] and congenital heart diseases [ 30 ]. Moreover, machine learning has been developed for predicting FR at patient’s bedside [ 31 ] with preliminary data on the implementation of AI for IVC assessment [ 32 ].…”

Assessment of the inferior vena cava collapsibility from subcostal and trans-hepatic imaging using both M-mode or artificial intelligence: a prospective study on healthy volunteers

“…The use of artificial intelligence (AI) is in great expansion in several medical fields, including echocardiography. For instance, AI has been applied to the estimation of left ventricular systolic [ 24 , 25 ] and diastolic [ 26 – 28 ] function, to right ventricular function [ 29 ], but it has also been adopted for assessment of heart valve diseases [ 30 , 31 ] and for diagnosis of congenital heart diseases [ 32 ]. Furthermore, machine learning methods have been developed for the improvement of bedside prediction of FR [ 33 ], and preliminary experiences with AI in the assessment of IVCc have been reported [ 34 ].…”