Nizar Muhammad Hanif Ahmad, Chan Chow Khuen, Khalil Azira, Yusof Ahmad Khairuddin Mohamed, Lai Khin Wee
Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Jalan Universiti, Kuala Lumpur 50603, Malaysia.
Department of Applied Physics, Islamic Science University of Malaysia, Nilai, Negeri Sembilan 71800, Malaysia.
Curr Med Imaging. 2020;16(5):584-591. doi: 10.2174/1573405615666190114151255.
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.
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.
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.
Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.
心脏瓣膜病是一种严重疾病，可导致死亡并增加医疗成本。主动脉瓣是受该疾病影响最常见的瓣膜。医生依靠超声心动图来诊断和评估心脏瓣膜病。然而，与计算机断层扫描和磁共振成像扫描相比，超声心动图的图像质量较差。本研究提出开发卷积神经网络（CNN），其在实时超声心动图检查期间能够最佳地发挥功能以检测主动脉瓣。超声心动图中的自动检测系统将提高医学诊断的准确性，并可根据检测结果提供进一步的医学分析。
使用具有各种特征提取器的两种检测架构，即单阶段多框检测器（SSD）和基于区域的更快卷积神经网络（R-CNN），对33例患者的超声心动图图像进行训练。此后，在10个超声心动图视频上对模型进行测试。
更快的R-CNN Inception v2显示出最高的准确率（98.6%），紧随其后的是SSD Mobilenet v2。在速度方面，SSD Mobilenet v2在实时检测期间每秒帧数（fps）损失46.81%，但表现优于其他神经网络模型。此外，SSD Mobilenet v2使用的图形处理单元（GPU）最少，但所有模型的中央处理器（CPU）使用率相对相似。
我们的研究结果为将卷积检测系统应用于医学超声心动图提供了基础。
