Clinical Utility of Machine-Learning Approaches in Schizophrenia: Improving Diagnostic Confidence for Translational Neuroimaging - Iwabuchi, Sarina; Liddle, Peter F.; Palaniyappan, Lena (Frontiers in Psychiatry, 2013)
Machine-learning approaches are becoming commonplace in the neuroimaging literature as potential diagnostic and prognostic tools for the study of clinical populations. However, very few studies provide clinically informative measures to aid in decision-making and resource allocation. Head-to-head comparison of neuroimaging-based multivariate classifiers is an essential first step to promote translation of these tools to clinical practice. We systematically evaluated the classifier performance using back-to-back structural MRI in two field strengths (3-Tesla and 7-Tesla) to discriminate patients with schizophrenia (n=19) from healthy controls (n=20). Grey (GM) and white matter (WM) images were used as inputs into a support vector machine (SVM) to classify patients and control subjects. 7T classifiers outperformed the 3T classifiers with accuracy reaching as high as 77% for the 7T GM classifier compared to 66.6% for the 3T GM classifier. Furthermore, diagnostic odds ratio (a measure that is not affected by variations in sample characteristics) and number needed to predict (a measure based on Bayesian certainty of a test result) indicated superior performance of the 7T classifiers, whereby for each correct diagnosis made, the number of patients that need to be examined using the 7T GM classifier was one less than the number that need to be examined if a different classifier was used. Using a hypothetical example, we highlight how these findings could have significant implications for clinical decision-making. We encourage the reporting of measures proposed here in future studies utilizing machine-learning approaches. This will not only promote the search for an optimum diagnostic tool but also aid in the translation of neuroimaging to clinical use.