Department of Nuclear Medicine, Gazi University School of Medicine, Besevler, Ankara, Turkey.
J Nucl Cardiol. 2010 Jun;17(3):405-13. doi: 10.1007/s12350-010-9207-5. Epub 2010 Mar 4.
The purpose of this study is to develop and analyze an open-source artificial intelligence program built on artificial neural networks that can participate in and support the decision making of nuclear medicine physicians in detecting coronary artery disease from myocardial perfusion SPECT (MPS).
Two hundred and forty-three patients, who had MPS and coronary angiography within three months, were selected to train neural networks. Six nuclear medicine residents, one experienced nuclear medicine physician, and neural networks evaluated images of 65 patients for presence of coronary artery stenosis. Area under the curve (AUC) of receiver operating characteristics analysis for networks and expert was .74 and .84, respectively. The AUC of the other physicians ranged from .67 to .80. There were no significant differences between expert, neural networks, and standard quantitative values, summed stress score and total stress defect extent.
The open-source neural networks developed in this study may provide a framework for further testing, development, and integration of artificial intelligence into nuclear cardiology environment.
本研究旨在开发和分析一个基于人工神经网络的开源人工智能程序，该程序可以参与并支持核医学医师在检测心肌灌注 SPECT（MPS）中的冠状动脉疾病时做出决策。
选择了 243 名在三个月内进行了 MPS 和冠状动脉造影的患者来训练神经网络。6 名核医学住院医师、1 名经验丰富的核医学医师和神经网络对 65 名患者的图像进行了冠状动脉狭窄存在的评估。接受者操作特征分析的曲线下面积（AUC）对于网络和专家分别为 0.74 和 0.84。其他医师的 AUC 范围在 0.67 至 0.80 之间。专家、神经网络和标准定量值（总和应激评分和总应激缺陷程度）之间没有显著差异。
本研究中开发的开源神经网络可为进一步测试、开发和将人工智能集成到核心脏病学环境中提供框架。
