How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key meth
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IMAGING INFORMATICS AND ARTIFICIAL INTELLIGENCE
How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts Burak Kocak 1
&
Ece Ates Kus 2 & Ozgur Kilickesmez 1
Received: 31 May 2020 / Revised: 25 August 2020 / Accepted: 18 September 2020 # European Society of Radiology 2020
Abstract In recent years, there has been a dramatic increase in research papers about machine learning (ML) and artificial intelligence in radiology. With so many papers around, it is of paramount importance to make a proper scientific quality assessment as to their validity, reliability, effectiveness, and clinical applicability. Due to methodological complexity, the papers on ML in radiology are often hard to evaluate, requiring a good understanding of key methodological issues. In this review, we aimed to guide the radiology community about key methodological aspects of ML to improve their academic reading and peer-review experience. Key aspects of ML pipeline were presented within four broad categories: study design, data handling, modelling, and reporting. Sixteen key methodological items and related common pitfalls were reviewed with a fresh perspective: database size, robustness of reference standard, information leakage, feature scaling, reliability of features, high dimensionality, perturbations in feature selection, class balance, bias-variance trade-off, hyperparameter tuning, performance metrics, generalisability, clinical utility, comparison with traditional tools, data sharing, and transparent reporting. Key Points • Machine learning is new and rather complex for the radiology community. • Validity, reliability, effectiveness, and clinical applicability of studies on machine learning can be evaluated with a proper understanding of key methodological concepts about study design, data handling, modelling, and reporting. • Understanding key methodological concepts will provide a better academic reading and peer-review experience for the radiology community. Keywords Machine learning . Artificial intelligence . Deep learning . Radiology . Peer-review
Abbreviations ML Machine learning
Introduction As a subfield of artificial intelligence, machine learning (ML) is the study of computer algorithms that learn from the input * Burak Kocak [email protected] 1
Department of Radiology, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480 Istanbul, Turkey
2
Department of Radiology, Istanbul Training and Research Hospital, Samatya, 34098 Istanbul, Turkey
data and make predictions on unseen instances [1, 2]. ML algorithms are designed to operate without specific rulebased instructions, improving themselves by learning and correcting through experience [1–4]. ML is broadly grouped into two categories with a key difference in the use of labels for model development. While supervised learning needs labels in training, unsupervised learning requires no labels in discovering patterns in data sets. Several ML algorithms exist with a wide range of comp
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