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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