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Automated Interpretation of Myocardial Perfusion Images with Multilayer Perceptron Network as a Decision Support System

机译:用多层的灌注图像自动解释与多层的Perceptron网络作为决策支持系统

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Aim: Bull's eye pattern recognition with artificial neural networks (ANNs) has the potential to assist interpretation of myocardial perfusion images (MPIs). We aimed to develop a model for interpretation of MPI based on the clinical variables and imaging data. Materials and Methods: The study included 208 patients referred to the department of nuclear medicine for 2-day stress-rest ECG-gated MPI. Several ANN models were designed with the following input variables: average count of 20 segments of the bull's eye images of stress and rest MPIs, gender, the constellation of coronary artery disease risk factors and scintigraphic cardiac ejection fraction. The procedure was repeated excluding the data of the rest phase scan. Data of 150 subjects were used for training, 21 subjects for cross-validation and 37 subjects for final operation testing. Several ANN models were examined with different hidden layers and processing elements and functions. The target output variable was the conclusion of the nuclear physician (i.e., normal vs. abnormal scan). Results: A multilayer perceptron (MLP) with two hidden layers trained with both stress and rest data demonstrated the best performance to classify the normal and abnormal MPIs. It showed an overall accuracy of 91.9%, sensitivity of 91.3% and specificity of 92.9%. The accuracy of the similar MLP trained using stress-only myocardial perfusion images reduced to 67.6%. Conclusion: The automated interpretation of MPIs with a 2 hidden layer MLP trained with stress and rest images could be an accurate support system either for the interpretation or quality assurance.
机译:目的:与人工神经网络(ANNS)的牛眼识别有可能协助解释心肌灌注图像(MPI)。我们旨在基于临床变量和成像数据来开发MPI解释模型。材料和方法:该研究包括208名患者核检部门的核医学部2天应力休息ECG门控MPI。几个ANN模型的设计有以下输入变量:牛眼眼睛的平均数量为应力和休息MPI,性别,冠状动脉疾病危险因素和闪烁的心脏射血部分的危险因素。重复该过程,不包括REST相位扫描的数据。 150个受试者的数据用于培训,21个受试者进行交叉验证和37个受试者进行最终操作测试。用不同的隐藏层和处理元件和功能检查了几个ANN模型。目标输出变量是核医生的结论(即,正常与异常扫描)。结果:具有两个隐藏图层的多层的Perceptron(MLP),带有应力和休息数据训练,表明了分类正常和异常MPI的最佳性能。它显示出91.9%的整体准确性,灵敏度为91.3%,特异性为92.9%。使用仅限应力的心肌灌注图像培训的类似MLP的准确性降低至67.6%。结论:具有带应力和休息图像训练的2个隐藏层MLP的MPI自动解释可以是用于解释或质量保证的准确支持系统。

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