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Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks
Muhammad Hanif Ahmad Nizar1, Chow Khuen Chan1, Azira Khalil2
1Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Jalan Universiti, Kuala Lumpur 50603, Malaysia.
Insights
This study developed a Convolutional Neural Network (CNN) for automated aortic valve detection in echocardiograms. Faster R-CNN Inception v2 achieved 98.6% accuracy, improving cardiac diagnosis.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Healthcare
- Cardiovascular Diagnostics
Background:
- Valvular heart disease, particularly aortic valve disease, is a significant cause of mortality and healthcare costs.
- Echocardiography is crucial for diagnosis but suffers from image quality limitations compared to CT and MRI.
- Automated detection systems are needed to enhance diagnostic accuracy and enable further analysis.
Purpose of the Study:
- To develop and evaluate Convolutional Neural Networks (CNNs) for optimal live detection of the aortic valve during echocardiographic examinations.
- To improve the accuracy and efficiency of diagnosing valvular heart disease.
Main Methods:
- Trained two deep learning architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN), on echocardiography images from 33 patients.
- Utilized various feature extractors within these architectures.
- Tested the models on 10 echocardiography videos for real-time performance evaluation.
Main Results:
- Faster R-CNN Inception v2 demonstrated the highest accuracy at 98.6%.
- SSD Mobilenet v2 offered a competitive speed, with a minor frame rate loss during real-time detection.
- SSD Mobilenet v2 exhibited the lowest Graphic Processing Unit (GPU) usage among the tested models.
Conclusions:
- The study provides a foundation for integrating CNN-based detection systems into echocardiography for enhanced medical diagnosis.
- Automated aortic valve detection shows promise for improving cardiovascular diagnostics and patient care.
Background:
Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection.
Methods:
Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos.
Results:
Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models.
Conclusion:
Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.
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