Common Critiques and Recommendations for Studies in Neurology Using Machine Learning Methods

Abstract

® editorial board and have reviewed many studies using ML methods. In their review reports, several critiques often appear, which could be avoided if guidance were available. In this article, we detail common critiques found in ML research studies and make recommendations for how to avoid them. The first critique involves misalignment of the study goals and the analysis conducted. The second critique focuses on ML terminology being appropriately used. Critiques 3-6 are related to the study design: justifying sample sizes and the suitability of the data set for the study goals, describing the ML analysis pipeline sufficiently, quantifying the amount of missing data and providing information about missing data handling, and including uncertainty estimates for key metrics. The seventh critique focuses on fairly describing both strengths and limitations of the ML study, including the analysis methodology and results. We provide examples in neurology for each critique and guidance on how to avoid the critique. Overall, we recommend that authors use ML-specific checklists developed by research consortia for designing and reporting studies using ML. We also recommend that authors involve both a statistician and an ML expert in work that uses ML. Although our list of critiques is not exhaustive, our recommendations should help improve the quality and rigor of ML studies. ML has great potential to revolutionize neurology, but investigators need to conduct and report the results in a way that allows readers to fully evaluate the benefits and limitations of ML approaches.

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