Congenital Heart Diseases in Pregnancy

Bernolian, Nuswil; Partan, Radiyati Umi; Nurmaini, Siti; Kesty, Cindy; Widodo, Benedictus Wicaksono

doi:10.32539/bsm.v5i4.376

Cited by 1 publication

(1 citation statement)

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“…Therefore, the development of a prognostic model for heart failure patients based on echocardiographic metrics is of great value to assist clinicians in the treatment of hospitalized patients and the daily management of patients after discharge. Comparative models were developed by applying machine learning algorithms with traditional statistical methods, that is, logistic linear regression, respectively, and, by comparing the values of the two models developed for the assessment of mortality and the risk of cardiovascular events in patients, it was concluded that comparative risk models with good comparative power and risk identification could be obtained by machine learning algorithms [ 7 ]. The purpose of this paper is to establish a comparative model based on the echocardiographic findings of the patients, by applying the BP neural network learning algorithm jointly by applying the 7 indicators reflecting the cardiac function of the patients obtained by echocardiography, and to conduct a comparative study on the comparative results of patients with reduced ejection fraction, that is, 1-year readmission and 3-year mortality, which has high clinical research and practical application significance.…”

Section: Introductionmentioning

confidence: 99%

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

Zhu

Fan

Liu

et al. 2021

Journal of Healthcare Engineering

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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.

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Section: Introductionmentioning

confidence: 99%