  As the first-line diagnostic imaging modality, radiography plays an essential
role in the early detection of developmental dysplasia of the hip (DDH).
Clinically, the diagnosis of DDH relies on manual measurements and subjective
evaluation of different anatomical features from pelvic radiographs. This
process is inefficient and error-prone and requires years of clinical
experience. In this study, we propose a deep learning-based system that
automatically detects 14 keypoints from a radiograph, measures three anatomical
angles (center-edge, T\"onnis, and Sharp angles), and classifies DDH hips as
grades I-IV based on the Crowe criteria. Moreover, a novel data-driven scoring
system is proposed to quantitatively integrate the information from the three
angles for DDH diagnosis. The proposed keypoint detection model achieved a mean
(95% confidence interval [CI]) average precision of 0.807 (0.804-0.810). The
mean (95% CI) intraclass correlation coefficients between the center-edge,
Tonnis, and Sharp angles measured by the proposed model and the ground-truth
were 0.957 (0.952-0.962), 0.947 (0.941-0.953), and 0.953 (0.947-0.960),
respectively, which were significantly higher than those of experienced
orthopedic surgeons (p<0.0001). In addition, the mean (95% CI) test diagnostic
agreement (Cohen's kappa) obtained using the proposed scoring system was 0.84
(0.83-0.85), which was significantly higher than those obtained from diagnostic
criteria for individual angle (0.76 [0.75-0.77]) and orthopedists (0.71
[0.63-0.79]). To the best of our knowledge, this is the first study for
objective DDH diagnosis by leveraging deep learning keypoint detection and
integrating different anatomical measurements, which can provide reliable and
explainable support for clinical decision-making.

Image and Video Processing;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning