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Traumatic brain injury (TBI) often results in traumatic axonal injury (TAI). This can be difficult to identify using conventional imaging. Diffusion tensor imaging (DTI) offers a method of assessing axonal damage in vivo, but has previously mainly been used to investigate groups of patients. Machine learning techniques are increasingly used to improve diagnosis based on complex imaging measures. We investigated whether machine learning applied to DTI data can be used to diagnose white matter damage after TBI and to predict neuropsychological outcome in individual patients.

We trained pattern classifiers to predict the presence of white matter damage in 25 TBI patients with microbleed evidence of TAI compared to neurologically healthy age‐matched controls. We then applied these classifiers to 35 additional patients with no conventional imaging evidence of TAI. Finally, we used regression …