Bibliometric analysis of 100 top cited articles of heart failure-associated diseases in combination with machine learning

Objective

The aim of this paper is to analyze the application of machine learning in heart failure-associated diseases using bibliometric methods and to provide a dynamic and longitudinal bibliometric analysis of heart failure-related machine learning publications.

Materials and methods

Web of Science was screened to gather the articles for the study. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility. Intuitive data analysis was employed to analyze the top-100 cited articles and VOSViewer was used to analyze the relevance and impact of all articles. The two analysis methods were then compared to get conclusions.

Results

The search identified 3,312 articles. In the end, 2,392 papers were included in the study, which were published between 1985 and 2023. All articles were analyzed using VOSViewer. Key points of the analysis included the co-authorship map of authors, countries and organizations, the citation map of journal and documents and a visualization of keyword co-occurrence analysis. Among these 100 top-cited papers, with a mean of 122.9 citations, the most-cited article had 1,189, and the least cited article had 47. Harvard University and the University of California topped the list among all institutes with 10 papers each. More than one-ninth of the authors of these 100 top-cited papers wrote three or more articles. The 100 articles came from 49 journals. The articles were divided into seven areas according to the type of machine learning approach employed: Support Vector Machines, Convolutional Neural Networks, Logistic Regression, Recurrent Neural Networks, Random Forest, Naive Bayes, and Decision Tree. Support Vector Machines were the most popular method.

Conclusions

This analysis provides a comprehensive overview of the artificial intelligence (AI)-related research conducted in the field of heart failure, which helps healthcare institutions and researchers better understand the prospects of AI in heart failure and formulate more scientific and effective research plans. In addition, our bibliometric evaluation can assist healthcare institutions and researchers in determining the advantages, sustainability, risks, and potential impacts of AI technology in heart failure.

Fetal Heart and Descending Aorta Detection in Four-Chamber View of Fetal Echocardiography

Automatic analysis of fetal heart and related components in fetal echocardiography can help cardiologists to reach a diagnosis for Congenital Heart Disease (CHD). Previous studies mainly focused on cardiac chamber segmentation, while few researches deal with the cardiac component detection. In this paper, we tackle the task of simultaneous detection of the fetal heart and descending aorta in four-chamber view of fetal echocardiography, which is useful to analyze some kinds of CHD, such as left/right atrial isomerism, dextroversion of heart, etc. Several CNN-based object detection methods with different backbones are thoroughly evaluated, and finally, the Hybrid Task Cascade method with HRNet is selected as the detection method. Experiments on a fetal echocardiography dataset show that the method can achieve superior performance according to common-used evaluation metrics.Clinical relevance-This can be used to help the cardiologists to estimate the position of the fetal heart and the descending aorta, which is also useful to estimate the direction of the cardiac axis and apex and analyze some kinds of CHD, such as left/right atrial isomerism, dextroversion of heart, etc.

Texture Spectrum Coupled with Entropy and Homogeneity Image Features for Myocardium Muscle Characterization

Background:

People in middle/later age often suffer from heart muscle damage due to coronary artery disease associated to myocardial infarction. In young people, the genetic forms of cardiomyopathies (heart muscle disease) are the utmost protuberant cause of myocardial disease.

Objective:

Accurate early detected information regarding the myocardial tissue structure is a key answer for tracking the progress of several myocardial diseases. associations while known disease-lncRNA associations are required only.

Method:

The present work proposes a new method for myocardium muscle texture classification based on entropy, homogeneity and on the texture unit-based texture spectrum approaches. Entropy and homogeneity are generated in moving windows of size 3x3 and 5x5 to enhance the texture features and to create the premise of differentiation of the myocardium structures. The texture is then statistically analyzed using the texture spectrum approach. Texture classification is achieved based on a fuzzy c–means descriptive classifier. The proposed method has been tested on a dataset of 80 echocardiographic ultrasound images in both short-axis and long-axis in apical two chamber view representations, for normal and infarct pathologies.

Results:

The noise sensitivity of the fuzzy c–means classifier was overcome by using the image features. The results established that the entropy-based features provided superior clustering results compared to homogeneity.

Conclusion:

Entropy image feature has a lower spread of the data in the clusters of healthy subjects and myocardial infarction. Also, the Euclidean distance function between the cluster centroids has higher values for both LAX and SAX views for entropy images.</P>

Efficient feature selection for neural network based detection of flaws in steel welded joints using ultrasound testing

This work studies methods for efficient extraction and selection of features in the context of a decision support system based on neural networks. The data comes from ultrasonic testing of steel welded joints, in which are found three types of flaws. The discrete Fourier, wavelet and cosine transforms are applied for feature extraction. Statistical techniques such as principal component analysis and the Wilcoxon-Mann-Whitney test are used for optimal feature selection. Two different artificial neural network architectures are used for automatic classification. Through the proposed approach, it is achieved a high discrimination efficiency by using only 20 features to feed the classifier, instead of the original 2500 A-scan sample points.

Boosting backpropagation algorithm by stimulus-sampling: Application in computer-aided medical diagnosis

Neural networks (NNs), in general, and multi-layer perceptron (MLP), in particular, represent one of the most efficient classifiers among the machine learning (ML) algorithms. Inspired by the stimulus-sampling paradigm, it is plausible to assume that the association of stimuli with the neurons in the output layer of a MLP can increase its performance. The stimulus-sampling process is assumed memoryless (Markovian), in the sense that the choice of a particular stimulus at a certain step, conditioned by the whole prior evolution of the learning process, depends only on the network's answer at the previous step. This paper proposes a novel learning technique, by enhancing the standard backpropagation algorithm performance with the aid of a stimulus-sampling procedure applied to the output neurons. The network uses the observable behavior that varies throughout the training process by stimulating the correct answers through corresponding rewards/penalties assigned to the output neurons. The proposed model has been applied in computer-aided medical diagnosis using five real-life breast cancer, colon cancer, diabetes, thyroid, and fetal heartbeat databases. The statistical comparison to well-established ML algorithms proved beyond doubt its efficiency and robustness.

Echocardiography without electrocardiogram using nonlinear dimensionality reduction methods

PURPOSE

The aim of this study is to evaluate the efficiency of a new automatic image processing technique, based on nonlinear dimensionality reduction (NLDR) to separate a cardiac cycle and also detect end-diastole (ED) (cardiac cycle start) and end-systole (ES) frames on an echocardiography system without using ECG.

METHODS

Isometric feature mapping (Isomap) and locally linear embeddings (LLE) are the most popular NLDR algorithms. First, Isomap algorithm is applied on recorded echocardiography images. By this approach, the nonlinear embedded information in sequential images is represented in a two-dimensional manifold and each image is characterized by a symbol on the constructed manifold. Cyclicity analysis of the resultant manifold, which is derived from the cyclic nature of the heart motion, is used to perform cardiac cycle length estimation. Then, LLE algorithm is applied on extracted left ventricle (LV) echocardiography images of one cardiac cycle. Finally, the relationship between consecutive symbols of the resultant manifold by the LLE algorithm, which is based on LV volume changes, is used to estimate ED (cycle start) and ES frames. The proposed algorithms are quantitatively compared to those obtained by a highly experienced echocardiographer from ECG as a reference in 20 healthy volunteers and 12 subjects with pathology.

RESULTS

Mean difference in cardiac cycle length, ED, and ES frame estimation between our method and ECG detection by the experienced echocardiographer is approximately 7, 17, and 17 ms (0.4, 1, and 1 frame), respectively.

CONCLUSION

The proposed image-based method, based on NLDR, can be used as a useful tool for estimation of cardiac cycle length, ED and ES frames in echocardiography systems, with good agreement to ECG assessment by an experienced echocardiographer in routine clinical evaluation.