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Machine Learning And MRI-Based Diagnostic Models For ADHD: Are We There Yet?
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2020-10-23
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Abstract
Machine learning (ML) has been applied to develop magnetic resonance imaging (MRI)-based
diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This systematic review
examines this literature to clarify its clinical significance and to assess the implications of the
various analytic methods applied. We found that, although most of studies reported the
classification accuracies, they varied in choice of MRI modalities, ML models, cross-validation
and testing methods, and sample sizes. We found that the accuracies of cross-validation
methods inflated the performance estimation compared with those of a held-out test,
compromising the model generalizability. Test accuracies have increased with publication year
but were not associated with training sample sizes. Improved test accuracy over time was likely
due to the use of better ML methods along with strategies to deal with data imbalances.
Ultimately, large multi-modal imaging datasets, and potentially the combination with other types
of data, like cognitive data and/or genetics, will be essential to achieve the goal of developing
clinically useful imaging classification tools for ADHD in the future.