Audiological Diagnosis of Valvular and Congenital Heart Diseases in the Era of Artificial Intelligence.

In recent years, electronic stethoscopes have been combined with artificial intelligence (AI) technology to digitally acquire heart sounds, intelligently identify valvular disease and congenital heart disease, and improve the accuracy of heart disease diagnosis. The research on AI-based intelligent stethoscopy technology mainly focuses on AI algorithms, and the commonly used methods are end-to-end deep learning algorithms and machine learning algorithms based on feature extraction, and the hot spot for future research is to establish a large standardized heart sound database and unify these algorithms for external validation; in addition, different electronic stethoscopes should also be extensively compared so that the algorithms can be compatible with different. In addition, there should be extensive comparison of different electronic stethoscopes so that the algorithms can be compatible with heart sounds collected by different stethoscopes; especially importantly, the deployment of algorithms in the cloud is a major trend in the future development of artificial intelligence. Finally, the research of artificial intelligence based on heart sounds is still in the preliminary stage, although there is great progress in identifying valve disease and congenital heart disease, they are all in the research of algorithm for disease diagnosis, and there is little research on disease severity, remote monitoring, prognosis, etc., which will be a hot spot for future research.

A Machine Learning Framework for Diagnosing and Predicting the Severity of Coronary Artery Disease.

Background: Although machine learning (ML)-based prediction of coronary artery disease (CAD) has gained increasing attention, assessment of the severity of suspected CAD in symptomatic patients remains challenging. Methods: The training set for this study consisted of 284 retrospective participants, while the test set included 116 prospectively enrolled participants from whom we collected 53 baseline variables and coronary angiography results. The data was pre-processed with outlier processing and One-Hot coding. In the first stage, we constructed a ML model that used baseline information to predict the presence of CAD with a dichotomous model. In the second stage, baseline information was used to construct ML regression models for predicting the severity of CAD. The non-CAD population was included, and two different scores were used as output variables. Finally, statistical analysis and SHAP plot visualization methods were employed to explore the relationship between baseline information and CAD. Results: The study included 269 CAD patients and 131 healthy controls. The eXtreme Gradient Boosting (XGBoost) model exhibited the best performance amongst the different models for predicting CAD, with an area under the receiver operating characteristic curve of 0.728 (95% CI 0.623-0.824). The main correlates were left ventricular ejection fraction, homocysteine, and hemoglobin (p < 0.001). The XGBoost model performed best for predicting the SYNTAX score, with the main correlates being brain natriuretic peptide (BNP), left ventricular ejection fraction, and glycated hemoglobin (p < 0.001). The main relevant features in the model predictive for the GENSINI score were BNP, high density lipoprotein, and homocysteine (p < 0.001). Conclusions: This data-driven approach provides a foundation for the risk stratification and severity assessment of CAD. Clinical Trial Registration: The study was registered in www.clinicaltrials.gov protocol registration system (number NCT05018715).

Maintaining ideal cardiovascular health is associated with higher serum anti-aging protein klotho in the middle-aged and older populations.

OBJECTIVES: Maintaining ideal cardiovascular health (CVH) is believed to have potential anti-aging benefits. The American Heart Association (AHA) recently updated the "Life's Essential 8 (LE8)" metrics to measure ideal CVH, but its connection with the anti-aging protein klotho is still unclear. We aimed to explore the relationship between ideal cardiovascular health and serum anti-aging protein klotho in a nationally representative US middle-aged and older population. DESIGN: A cross-sectional study. SETTING: The National Health and Nutrition Examination Survey (2007-2016). PARTICIPANTS: A total of 9457 middle-aged and older participants. MEASUREMENTS: Ideal CVH scores and their components were defined according to the guidelines set by the AHA. Serum klotho detected by enzyme-linked immunosorbent assay. Weighted multivariable linear regression and restricted cubic spline were employed to examine the association between CVH score and klotho. Subgroup analyses were conducted, stratified by age (40-59 and 60-79), sex (Male and Female), race (Mexican American, non-Hispanic White, non-Hispanic Black, and Others) and chronic kidney disease (Yes and No) in fully adjusted models. RESULTS: A total of 9457 middle-aged and older participants were included in this study, with a mean age of 55.27 ± 0.17 years. The mean serum klotho level in the population was 849.33 ± 5.39 pg/mL. After controlling for potential confounders, the LE8 score showed a positive correlation with serum klotho levels (ß: 1.32; 95% CI 0.73, 1.91), and a non-linear dose-response relationship was observed. Furthermore, we also discovered a positive relationship between health behaviors score and health factors score and serum klotho levels (ß: 0.48; 95% CI 0.07, 0.88 and ß: 1.05; 95% CI 0.54, 1.56, respectively), particularly a stronger correlation between health factors and serum klotho. In the subgroup analysis, we observed a significant interaction between LE8 score and sex and race. (P for interaction <0.05). CONCLUSIONS: LE8 and its subscale scores were positively associated with serum klotho levels in the middle-aged and older populations. Promoting the maintenance of ideal CVH can contribute to delaying the aging process.

Deep learning of heart-sound signals for efficient prediction of obstructive coronary artery disease.

Background: Due to the limitations of current methods for detecting obstructive coronary artery disease (CAD), many individuals are mistakenly or unnecessarily referred for coronary angiography (CAG). Objectives: Our goal is to create a comprehensive database of heart sounds in CAD and develop accurate deep learning algorithms to efficiently detect obstructive CAD based on heart sound signals. This will enable effective screening before undergoing CAG. Methods: We included 320 subjects suspected of CAD who underwent CAG. We employed advanced filtering techniques and state-of-the-art deep learning models (VGG-16, 1D CNN, and ResNet18) to analyze the heart sound signals and identify obstructive CAD (defined as at least one ≥50 % stenosis). To assess the performance of our models, we prospectively recruited an additional 80 subjects for testing. Results: In the test set, VGG-16 exhibited the highest performance with an area under the ROC curve (AUC) of 0.834 (95 % CI, 0.736-0.930), while ResNet-18 and CNN-7 achieved AUCs of only 0.755 (95 % CI, 0.614-0.819) and 0.652 (95 % CI, 0.554-0.770) respectively. VGG-16 demonstrated a sensitivity of 80.4 % and specificity of 86.2 % in the test set. The combined diagnostic model of VGG and DF scores achieved an AUC of 0.915 (95 % CI: 0.855-0.974), and the AUC for VGG combined with PTP scores was 0.908 (95 % CI: 0.845-0.971). The sensitivity and specificity of VGG-16 exceeded 0.85 in patients with coronary artery occlusion and those with 3 vascular lesions. Conclusions: Our deep learning model, based on heart sounds, offers a non-invasive and efficient screening method for obstructive CAD. It is expected to significantly reduce the number of unnecessary referrals for downstream screening.