Artificial neural networks in mammography interpretation and diagnostic decision making.
Artificial neural networks in mammography interpretation and diagnostic decision making.
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DOI：
10.1155/2013/832509
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发表时间：
2013
影响因子：
--
通讯作者：
Burnside ES
中科院分区：
文献类型：
--
作者：
Ayer T;Chen Q;Burnside ES
乳腺癌早期诊断的最有效方法是筛查性乳房X线摄影。尽管存在用于区分恶性和良性病变的一般规则，但是放射科医师不能完美地检测所有病变并将其分类为恶性和良性，原因有很多，包括但不限于区分恶性的特征的重叠、估计疾病风险的困难以及推荐管理的可变性。当预测变量众多且相互作用时，基于经验和记忆的临时决策策略可能导致实践中的系统性错误和可变性。在过去的二十年里，计算机模型的集成，以帮助放射科医生提高乳腺X射线摄影检查的诊断决策的准确性，已获得越来越多的关注。在这项研究中，我们提供了一个最常用的模型之一，人工神经网络（ANN），在乳腺X射线摄影解释和诊断决策的概述，并讨论了乳腺X射线摄影解释的重要功能。最后，我们讨论了基于人工神经网络的检测和诊断模型的现有研究的几个常见的局限性，并提供了未来可能的研究方向。
Screening mammography is the most effective means for early detection of breast cancer. Although general rules for discriminating malignant and benign lesions exist, radiologists are unable to perfectly detect and classify all lesions as malignant and benign, for many reasons which include, but are not limited to, overlap of features that distinguish malignancy, difficulty in estimating disease risk, and variability in recommended management. When predictive variables are numerous and interact, ad hoc decision making strategies based on experience and memory may lead to systematic errors and variability in practice. The integration of computer models to help radiologists increase the accuracy of mammography examinations in diagnostic decision making has gained increasing attention in the last two decades. In this study, we provide an overview of one of the most commonly used models, artificial neural networks (ANNs), in mammography interpretation and diagnostic decision making and discuss important features in mammography interpretation. We conclude by discussing several common limitations of existing research on ANN-based detection and diagnostic models and provide possible future research directions.
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影响因子：
37.8
作者：
Cook, Nancy R.
通讯作者：
Cook, Nancy R.
DOI：
10.1016/j.jacr.2004.09.012
发表时间：
2005-04-01
期刊：
Journal of the American College of Radiology : JACR
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通讯作者：
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4.8
作者：
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通讯作者：
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