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Background
Accurate interpretation of hip MRI is time‐intensive and difficult, prone to inter‐ and intrareviewer variability, and lacks a universally accepted grading scale to evaluate morphological abnormalities.
Purpose
To 1) develop and evaluate a deep‐learning‐based model for binary classification of hip osteoarthritis (OA) morphological abnormalities on MR images, and 2) develop an artificial...
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