Application of deep learning for automated diagnosis and classification of hip dysplasia on plain radiographs.

Background: Hip dysplasia is a condition where the acetabulum is too shallow to support the femoral head and is commonly considered a risk factor for hip osteoarthritis. The objective of this study was to develop a deep learning model to diagnose hip dysplasia from plain radiographs and classify dysplastic hips based on their severity.

Methods: We collected pelvic radiographs of 571 patients from two single-center cohorts and one multicenter cohort.