Department of Urology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10098 Berlin, Germany.
Nat Rev Urol. 2013 Mar;10(3):174-82. doi: 10.1038/nrurol.2013.9. Epub 2013 Feb 12.
Artificial neural networks (ANNs) are mathematical models that are based on biological neural networks and are composed of interconnected groups of artificial neurons. ANNs are used to map and predict outcomes in complex relationships between given 'inputs' and sought-after 'outputs' and can also be used find patterns in datasets. In medicine, ANN applications have been used in cancer diagnosis, staging and recurrence prediction since the mid-1990s, when an enormous effort was initiated, especially in prostate cancer detection. Modern ANNs can incorporate new biomarkers and imaging data to improve their predictive power and can offer a number of advantages as clinical decision making tools, such as easy handling of distribution-free input parameters. Most importantly, ANNs consider nonlinear relationships among input data that cannot always be recognized by conventional analyses. In the future, complex medical diagnostic and treatment decisions will be increasingly based on ANNs and other multivariate models.
人工神经网络（ANNs）是基于生物神经网络的数学模型，由相互连接的人工神经元群组成。ANNs 用于映射和预测给定“输入”和所需“输出”之间复杂关系中的结果，也可用于在数据集内查找模式。在医学领域，自 20 世纪 90 年代中期以来，ANN 应用已被用于癌症的诊断、分期和复发预测，当时特别在前列腺癌检测方面开展了大量工作。现代的人工神经网络可以结合新的生物标志物和成像数据来提高其预测能力，并可作为临床决策工具提供许多优势，例如易于处理无分布的输入参数。最重要的是，人工神经网络考虑了输入数据之间的非线性关系，这些关系通常无法通过常规分析识别。在未来，复杂的医疗诊断和治疗决策将越来越多地基于人工神经网络和其他多元模型。
