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

• Published in: The international journal of cardiovascular imaging 2023-05, Vol.39 (5), p.955-965
• Main Authors: Zhang, Xiaofeng, Liang, Tianyi, Su, Chunxiao, Qin, Shiyun, Li, Jingtao, Zeng, Decai, Cai, Yongzhi, Huang, Tongtong, Wu, Ji
• Format: Article
• Language: English
• Subjects: Amyloidosis; Cardiac Imaging; Cardiology; Cardiomyopathies - diagnostic imaging; Cardiomyopathy; Cardiomyopathy, Hypertrophic; Cardiovascular disease; Cardiovascular diseases; Computers; Decision making; Decision trees; Diagnosis; Diagnostic systems; Echocardiography; Efficiency; Feature extraction; Heart diseases; Heart Diseases - diagnostic imaging; Humans; Hypertension; Hypertrophy; Imaging; Learning algorithms; Machine learning; Medicine; Medicine & Public Health; Original Paper; Predictive Value of Tests; Radiology; Retrospective Studies; Sensitivity; Subgroups; Support vector machines; Texture; Ventricle; Visual observation
• ISSN: 1875-8312; 1569-5794; 1875-8312; 1573-0743
• DOI: 10.1007/s10554-023-02806-0

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.