The value of conventional echocardiographic and tissue doppler imaging in the diagnosis of cardiac amyloidosis

Research trends and hotspots evolution of cardiac amyloidosis: a bibliometric analysis from 2000 to 2022

In the new century, cardiac amyloidosis has received more attention from many countries and institutions, leading to innovations in the essence of the pathology, biological markers, noninvasive tests, and staging diagnoses and treatments for this disease. However, few reviews have summarized the research trends and hotspots in cardiac amyloidosis. Bibliometrics analysis is a statistically based approach to research that visualizes the contributions of academic institutions and changes in research hotspots. Therefore, in this paper, we used Citespace and VOSviewer software to conduct co-occurrence analysis and collaborative network analysis on the countries, institutions, and authors in the articles related to cardiac amyloidosis since the new century. And further find out burst keywords and references to obtain the research history, disciplinary development, and new hotspots and topics.

Deep learn-based computer-assisted transthoracic echocardiography: approach to the diagnosis of cardiac amyloidosis

Myocardial amyloidosis (CA) differs from other etiological pathologies of left ventricular hypertrophy in that transthoracic echocardiography is challenging to assess the texture features based on human visual observation. There are few studies on myocardial texture based on echocardiography. Therefore, this paper proposes an adaptive machine learning method based on ultrasonic image texture features to identify CA. In this retrospective study, a total of 289 participants (50 cases of myocardial amyloidosis; Hypertrophic cardiomyopathy: 70 cases; Uremic cardiomyopathy: 92 cases; Hypertensive heart disease: 77 cases). We extracted the myocardial ultrasonic imaging features of these patients and screened the features, and four models of random forest (RF), support vector machine (SVM), logistic regression (LR) and gradient decision-making lifting tree (GBDT) were established to distinguish myocardial amyloidosis from other diseases. Finally, the diagnostic efficiency of the model was evaluated and compared with the traditional ultrasonic diagnostic methods. In the overall population, the four machine learning models we established could effectively distinguish CA from nonCA diseases, AUC (RF 0.77, SVM 0.81, LR 0.81, GBDT 0.71). The LR model had the best diagnostic efficiency with recall, F1-score, sensitivity and specificity of 0.21, 0.34, 0.21 and 1.0, respectively. Slightly better than the traditional ultrasonic diagnosis model. In further subgroup analysis, the myocardial amyloidosis group was compared one-by-one with the patients with hypertrophic cardiomyopathy, uremic cardiomyopathy, and hypertensive heart disease groups, and the same method was used for feature extraction and data modeling. The diagnostic efficiency of the model was further improved. Notably, in identifying of the CA group and HHD group, AUC values reached more than 0.92, accuracy reached more than 0.87, sensitivity equal to or greater than 0.81, specificity 0.91, and F1 score higher than 0.84. This novel method based on echocardiography combined with machine learning may have the potential to be used in the diagnosis of CA.