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

Abstract

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.

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

“…When it comes to cardiovascular diseases, AI also plays an important role in their diagnosis. From the acute diagnosis of left ventricular hypertrophy using imaging methods such as echocardiography [ 56 ], to the diagnosis of amyloidosis also based on TTE [ 66 ] or idiopathic pulmonary hypertension [ 157 ], artificial intelligence has demonstrated its power to help clinicians.…”

“…Zhang X. et al showed in a retrospective analysis of 289 patients that ultrasonic imaging omics and a machine learning model can provide an excellent and non-invasive diagnostic tool for clinical practice for distinguishing CA from non-CA. For left ventricular strain, the machine learning model was slightly better than conventional echocardiography [ 66 ]. Another study, which analyzed 128 patients with ATTR-CA using AI, concluded that the ANN model estimated the risk of death or transplantation in patients with ATTR cm with better accuracy compared to traditional risk models [ 67 ].…”

Identifying Methamphetamine Users through EEG Analysis: Harnessing hctsa and Machine Learning Approaches

“…The application of ML for automated analysis for medical purposes is gaining signi cant interest, particularly in clinical diagnostics that rely on EEG data (Gemein et al, 2020). These techniques has been widely used in various elds of medicine such as seizure detection (Jiang et al, 2022;Thakare et al, 2023) and emotion recognition (Fang et al, 2021;Yalamanchili et al, 2022) using EEG data, diagnosis of cardiac disease using electrocardiograph (Sharma et al, 2023;Zhang et al, 2023), and also image classi cation (Menaka and Vaidyanathan, 2022;Samraj et al, 2023). In this research, we examined three ML methods with the top 40 features extracted from hctsa to assess the differences between healthy individuals and those with MA dependence.…”