Lisboa Paulo J, Taktak Azzam F G
School of Computing and Mathematical Science, Liverpool John Moores University, Liverpool, UK.
Neural Netw. 2006 May;19(4):408-15. doi: 10.1016/j.neunet.2005.10.007. Epub 2006 Feb 14.
Artificial neural networks have featured in a wide range of medical journals, often with promising results. This paper reports on a systematic review that was conducted to assess the benefit of artificial neural networks (ANNs) as decision making tools in the field of cancer. The number of clinical trials (CTs) and randomised controlled trials (RCTs) involving the use of ANNs in diagnosis and prognosis increased from 1 to 38 in the last decade. However, out of 396 studies involving the use of ANNs in cancer, only 27 were either CTs or RCTs. Out of these trials, 21 showed an increase in benefit to healthcare provision and 6 did not. None of these studies however showed a decrease in benefit. This paper reviews the clinical fields where neural network methods figure most prominently, the main algorithms featured, methodologies for model selection and the need for rigorous evaluation of results.
人工神经网络已在众多医学期刊中出现，常常取得了令人鼓舞的成果。本文报告了一项系统性综述，该综述旨在评估人工神经网络（ANNs）作为癌症领域决策工具的益处。在过去十年中，涉及将人工神经网络用于诊断和预后的临床试验（CTs）和随机对照试验（RCTs）数量从1项增加到了38项。然而，在396项涉及将人工神经网络用于癌症研究的项目中，只有27项是临床试验或随机对照试验。在这些试验中，21项显示对医疗保健服务有改善，6项则没有。不过，这些研究中没有一项显示出益处减少。本文回顾了神经网络方法最为突出的临床领域、主要的特色算法、模型选择方法以及对结果进行严格评估的必要性。
