Related Experiment Video
Updated: Jul 6, 2025
Semi-automated Optical Heartbeat Analysis of Small Hearts
Published on: September 16, 2009
Deep learning of heart-sound signals for efficient prediction of obstructive coronary artery disease
Aikeliyaer Ainiwaer1, Wen Qing Hou2, Quan Qi3
1Department of Cardiology, First Afliated Hospital of Xinjiang Medical University, State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, Xinjiang, 830000, China.
Insights
A novel deep learning model using heart sounds can effectively screen for obstructive coronary artery disease (CAD), reducing unnecessary invasive procedures. This non-invasive method shows high accuracy in identifying significant blockages before coronary angiography (CAG).
Area of Science:
- Cardiology
- Medical Diagnostics
- Artificial Intelligence in Healthcare
Background:
- Current methods for detecting obstructive coronary artery disease (CAD) have limitations, leading to unnecessary referrals for coronary angiography (CAG).
- Accurate and efficient screening for obstructive CAD is crucial to optimize patient management and resource allocation.
Purpose of the Study:
- To develop a comprehensive database of heart sounds in patients with CAD.
- To create and validate deep learning algorithms for the efficient detection of obstructive CAD using heart sound signals.
- To establish a non-invasive screening tool to reduce unnecessary CAG referrals.
Main Methods:
- Analysis of heart sound signals from 320 subjects suspected of CAD using advanced filtering and deep learning models (VGG-16, 1D CNN, ResNet18).
- Obstructive CAD defined as at least one stenosis ≥50%.
- Prospective validation on an additional 80 subjects.
Main Results:
- The VGG-16 model achieved the highest performance in the test set with an Area Under the ROC Curve (AUC) of 0.834.
- Combined models (VGG with DF or PTP scores) demonstrated superior performance with AUCs of 0.915 and 0.908, respectively.
- VGG-16 showed high sensitivity and specificity (>0.85) for detecting coronary artery occlusion and multi-vessel disease.
Conclusions:
- A deep learning model utilizing heart sounds provides a non-invasive and effective screening method for obstructive CAD.
- This approach is anticipated to significantly decrease the number of unwarranted referrals for invasive diagnostic procedures like CAG.
- Heart sound analysis offers a promising avenue for early and accurate detection of 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.
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