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Computer-aided Diagnosis in Interstitial Lung Disease : Application of an Artificial Neural Network to Differential Diagnosis

间质性肺疾病的计算机辅助诊断：人工神经网络在鉴别诊断中的应用

基本信息

批准号：
12670886
负责人：
ASHIZAWA Kazuto
金额：
$ 2.05万
依托单位：
NAGASAKI UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2000
资助国家：
日本
起止时间：
2000 至 2002
项目状态：
已结题

项目摘要

Purpose : We applied an artificial neural network (ANN) to differential diagnosis among certain diffuse lung diseases using high-resolution CT (HRCT) to evaluate the effect of the ANN output on radiologists' diagnostic performance.Materials and Methods : We selected 130 clinical cases of diffuse lung disease. We used a single three-layer, feed-forward ANN with a back-propagation algorithm. The ANN was designed to differentiate among 11 diffuse lung diseases using 10 clinical parameters and 23 HRCT features. Therefore, the ANN consisted of 33 input units and 11 output units. Subjective ratings for 23 HRCT features were provided independently by eight radiologists. All clinical cases were used for training and testing of the ANN by using a round-robin technique. In the observer test, HRCT images were viewed by eight radiologists first without and then with ANN output. The radiologists' performance was evaluated with receiver operating characteristic (ROC) analysis with a continuous rating scale.Results : The average A_z value for ANN performance obtained with all clinical parameters and HRCT features was 0.956. The diagnostic performance of all radiologists was significantly improved from 0.972 to 0.981 (p<0.005), when they used the ANN output based on their own feature ratings.Conclusion : The ANN can provide a useful output as a "second opinion" to improve radiologists' diagnostic performance in the differential diagnosis of certain diffuse lung diseases using HRCT.

目的：我们应用人工神经网络（ANN）的某些弥漫性肺疾病之间的鉴别诊断，使用高分辨率CT（HRCT）的放射科医师的诊断performance.Materials和方法：我们选择了130例弥漫性肺疾病的临床病例，以评估人工神经网络的输出效果。我们使用了一个单一的三层，前馈人工神经网络与反向传播算法。该人工神经网络旨在使用10个临床参数和23个HRCT特征来区分11种弥漫性肺部疾病。因此，人工神经网络由33个输入单元和11个输出单元组成。23个HRCT特征的主观评分由8名放射科医生独立提供。所有的临床病例被用于训练和测试的人工神经网络通过使用循环技术。在观察者测试中，HRCT图像由八位放射科医生首先查看，然后使用ANN输出。结果：所有临床参数和HRCT特征的人工神经网络性能的平均Az值为0.956。所有放射科医师的诊断性能显着提高，从0.972到0.981（p<0.005），当他们使用的ANN输出基于自己的功能ratings.Conclusion：ANN可以提供一个有用的输出作为“第二意见”，以提高放射科医师的诊断性能在某些弥漫性肺部疾病的鉴别诊断HRCT。