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- Title
Radiomics, machine learning, and artificial intelligence—what the neuroradiologist needs to know.
- Authors
Wagner, Matthias W.; Namdar, Khashayar; Biswas, Asthik; Monah, Suranna; Khalvati, Farzad; Ertl-Wagner, Birgit B.
- Abstract
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.
- Subjects
DEEP learning; NEUROLOGISTS; RESEARCH evaluation; STUDY skills; ARTIFICIAL intelligence; MACHINE learning; DIAGNOSTIC imaging; LEARNING; ARTIFICIAL neural networks; NEURORADIOLOGY
- Publication
Neuroradiology, 2021, Vol 63, Issue 12, p1957
- ISSN
0028-3940
- Publication type
Article
- DOI
10.1007/s00234-021-02813-9