Abstract #1298

Discriminating between normal and cam positive hips using MRI texture and gradient-boosted decision trees

Rebecca E Thornhill^1,2, Taryn Hodgdon^1,2, Gerd Melkus^1,2, Nick D James³, Paul E Beaulé⁴, and Kawan S Rakhra^1,2

¹Medical Imaging, The Ottawa Hospital, Ottawa, ON, Canada, ²Radiology, University of Ottawa, Ottawa, ON, Canada, ³Information Services, The Ottawa Hospital, Ottawa, ON, Canada, ⁴Division of Orthopaedic Surgery, The Ottawa Hospital, Ottawa, ON, Canada

Cam-type femoroacetabular impingement (FAI) results in altered biomechanics and acetabular pathology that has been associated with osteoarthritis of the hip. These early changes can be difficult to detect with routine clinical imaging. Texture analysis offers a more quantitative approach for characterizing gray-level patterns. The purpose of this study was to determine the MRI texture profile of acetabular subchondral bone in normal, asymptomatic cam positive and symptomatic cam-FAI hips with the assistance of gradient-boosted decision trees. This work demonstrates that MRI textural features can be used to generate machine learning models that can identify cam positive hips, regardless of symptom status.

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