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Abstract #1219

Single-subject level inference for volumetry features in mild traumatic brain injury using machine learning methods

Venkata Veerendranadh Chebrolu1, Tianhao Zhang2, Hariharan Ravishankar1, Sumit Niogi3, John A Tsiouris3, and Luca Marinelli4

1GE Global Research, Bangalore, India, 2GE Healthcare, Waukesha, WI, United States, 3Weill Cornell Medical Center, New York, NY, United States, 4GE Global Research, Niskayuna, NY, United States

The purpose of this work is to derive single-subject level inferences for volumetry features in mild traumatic brain injury (mTBI) at multiple time points after initial trauma using machine learning methods. 78 uncomplicated mTBI subjects were scanned three days (32 subjects), seven days (61 subjects), one month (56) and three months (42 subjects) post injury to derive volumetery features. 23 controls were also scanned. Logistic-regression models were used to identify important volumetry features that jointly describe the mTBI effects at single-subject level. Pallidus, supratentorial and whole-brain volumetry features together provide single-subject level signature for mTBI at multiple time-points after injury.

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