# Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip

arXiv: https://arxiv.org/abs/2209.03440 · HTML: https://mlsanity.com/paper/2209.03440/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 44 references that are in the map.
- First posted on arXiv 7 Sep 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [DeepPose: Human Pose Estimation via Deep Neural Networks](https://mlsanity.com/paper/1312.4659/) (2013)
- [HPatches: A benchmark and evaluation of handcrafted and learned local descriptors](https://mlsanity.com/paper/1704.05939/) (2017)
- [Efficient Object Localization Using Convolutional Networks](https://mlsanity.com/paper/1411.4280/) (2014)
- [Deep Learning Predicts Hip Fracture using Confounding Patient and Healthcare Variables](https://mlsanity.com/paper/1811.03695/) (2018)
- [Benchmarking and Error Diagnosis in Multi-Instance Pose Estimation](https://mlsanity.com/paper/1707.05388/) (2017)
- [Deep Learning-based End-to-end Diagnosis System for Avascular Necrosis of Femoral Head](https://mlsanity.com/paper/2002.05536/) (2020)

## Built on by (most cited first, 1 of 1)

- [A New Angle on Bones: Robust Pose Estimation in X-Ray and Ultrasound](https://mlsanity.com/paper/2606.04700/) (2026)

## Abstract (arXiv)

Objective: The clinical diagnosis of developmental dysplasia of the hip (DDH) typically involves manually measuring key radiological angles -- Center-Edge (CE), Tonnis, and Sharp angles -- from pelvic radiographs, a process that is time-consuming and susceptible to variability. This study aims to develop an automated system that integrates these measurements to enhance the accuracy and consistency of DDH diagnosis. Methods and procedures: We developed an end-to-end deep learning model for keypoint detection that accurately identifies eight anatomical keypoints from pelvic radiographs, enabling the automated calculation of CE, Tonnis, and Sharp angles. To support the diagnostic decision, we introduced a novel data-driven scoring system that combines the information from all three angles into a comprehensive and explainable diagnostic output. Results: The system demonstrated superior consistency in angle measurements compared to a cohort of eight moderately experienced orthopedists. The intraclass correlation coefficients for the CE, Tonnis, and Sharp angles were 0.957 (95% CI: 0.952--0.962), 0.942 (95% CI: 0.937--0.947), and 0.966 (95% CI: 0.964--0.968), respectively. The system achieved a diagnostic F1 score of 0.863 (95% CI: 0.851--0.876), significantly outperforming the orthopedist group (0.777, 95% CI: 0.737--0.817, p = 0.005), as well as using clinical diagnostic criteria for each angle individually (p<0.001). Conclusion: The proposed system provides reliable and consistent automated measurements of radiological angles and an explainable diagnostic output for DDH, outperforming moderately experienced clinicians. Clinical impact: This AI-powered solution reduces the variability and potential errors of manual measurements, offering clinicians a more consistent and interpretable tool for DDH diagnosis.