Kentala E, Pyykkö I, Auramo Y, Juhola M
Department of Otorhinolaryngology, Helsinki University Hospital, Finland.
Acta Otolaryngol Suppl. 1997;529:127-9. doi: 10.3109/00016489709124102.
Artificial intelligence donates new possibilities to neurotologic research. Neural networks are a computer-based reasoning method which can be applied in expert systems created for clinical decision support. Neural networks have been used in medical imaging, in medical signal processing and to analyze both clinical and laboratory data. Principally, neural networks simulate the function of the brain. They have to be taught to make correct decisions from the input data. This learning process can be either supervised or unsupervised. The decision making is based on mathematical transformations and it occurs on a hidden level. Calculations are made on parallel manner and the decision making simulates pattern recognition method. Neural networks suit well in medical problems which cannot be defined in simple rules. A drawback of neural networks is that the decisions are irrational and cannot be motivated to the user. Another problem is neural networks' difficulty to handle incomplete input data, i.e., how to define some default or expected values for unknown input parameters. In a complex medical area, which would require multilayered neural networks, the neural networks require a large amount of solved cases for the learning process. In our experience neural networks seem not suitable for diagnosing vertigo and a better choice would be either case-based reasoning or possibly genetic algorithms or a combination of these.
人工智能为神经耳科学研究带来了新的可能性。神经网络是一种基于计算机的推理方法，可应用于为临床决策支持而创建的专家系统。神经网络已被用于医学成像、医学信号处理以及分析临床和实验室数据。从本质上讲，神经网络模拟大脑的功能。它们必须通过输入数据来学习做出正确的决策。这种学习过程可以是有监督的，也可以是无监督的。决策基于数学变换，并且发生在隐藏层面。计算以并行方式进行，决策模拟模式识别方法。神经网络非常适合那些无法用简单规则定义的医学问题。神经网络的一个缺点是决策是不合理的，并且无法向用户解释其依据。另一个问题是神经网络难以处理不完整的输入数据，即如何为未知的输入参数定义一些默认值或期望值。在需要多层神经网络的复杂医学领域，神经网络在学习过程中需要大量已解决的案例。根据我们的经验，神经网络似乎不适合诊断眩晕，更好的选择可能是基于案例的推理，或者可能是遗传算法，或者是这些方法的组合。
