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Abstract #0251

Computer Aided Detection AI Reduces Inter-Reader Variability in Grading Hip Abnormalities from MRI

Radhika Tibrewala1, Eugene Ozhinsky1, Rutwik Shah1, Io Flament1, Kay Crossley2, Ramya Srinivasan1, Thomas M Link1, Valentina Pedoia1, and Sharmila Majumdar1
1Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, 2La Trobe Sport and Exercise Medicine Research Centre, Melbourne, Australia

MRI based hip degeneration grading is difficult, time-intensive and prone to inter-reader variability, aggravated by the lack of a standard hip grading scale. Recent research using deep learning based clinical classification tasks has shown efficiency in knee degenerative changes. In this study, we aim to develop a deep learning based hip degenerative changes classification model (for cartilage lesions, bone marrow edemas and cysts) and evaluate its performance. In addition to that, we develop an AI-assist tool based on model predictions to test on two radiologists to see if the inter-reader agreement increases by using the AI-assist.

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