Purpose

Artificial intelligence (AI) is playing an ever-increasing role in Neuroradiology.

Methods

When designing AI-based research in neuroradiology and appreciating the literature, it is important to understand the fundamental principles of AI. Training, validation, and test datasets must be defined and set apart as priorities. External validation and testing datasets are preferable, when feasible. The specific type of learning process (supervised vs. unsupervised) and the machine learning model also require definition. Deep learning (DL) is an AI-based approach that is modelled on the structure of neurons of the brain; convolutional neural networks (CNN) are a commonly used example in neuroradiology.

Results

Radiomics is a frequently used approach in which a multitude of imaging features are extracted from a region of interest and subsequently reduced and selected to convey diagnostic or prognostic information. Deep radiomics uses CNNs to directly extract features and obviate the need for predefined features.

Conclusion

Common limitations and pitfalls in AI-based research in neuroradiology are limited sample sizes (“small-n-large-p problem”), selection bias, as well as overfitting and underfitting.

中文翻译：

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放射组学、机器学习和人工智能——神经放射科医生需要知道的

目的

人工智能 (AI) 在神经放射学中发挥着越来越重要的作用。

方法

在设计基于 AI 的神经放射学研究并欣赏文献时，了解 AI 的基本原理非常重要。必须定义训练、验证和测试数据集并将其作为优先事项分开。如果可行，最好使用外部验证和测试数据集。特定类型的学习过程（监督与无监督）和机器学习模型也需要定义。深度学习 (DL) 是一种基于人工智能的方法，它以大脑神经元的结构为模型；卷积神经网络 (CNN) 是神经放射学中常用的示例。

结果

放射组学是一种常用的方法，其中从感兴趣区域提取大量成像特征，然后对其进行缩减和选择以传达诊断或预后信息。深度放射组学使用 CNN 直接提取特征并消除对预定义特征的需求。

结论

基于 AI 的神经放射学研究的常见局限性和缺陷是样本量有限（“small-n-large-p 问题”）、选择偏差以及过拟合和欠拟合。