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

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

The clinical diagnosis of developmental dysplasia of the hip (DDH) typically involves manually measuring key radiological angles—Center-Edge (CE), Tönnis, and Sharp angles—from pelvic radiographs, a process that is time-consuming and susceptible to variability. This...

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Bibliographic Details
Main Authors: Yang Li, Leo Yan Li-Han, Hua Tian
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Translational Engineering in Health and Medicine
Subjects:
Convolutional neural network
developmental dysplasia of the hip
keypoint detection
radiograph
scoring system
Online Access:https://ieeexplore.ieee.org/document/10965781/
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Summary:The clinical diagnosis of developmental dysplasia of the hip (DDH) typically involves manually measuring key radiological angles&#x2014;Center-Edge (CE), T&#x00F6;nnis, and Sharp angles&#x2014;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. 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, T&#x00F6;nnis, 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. The system demonstrated superior consistency in angle measurements compared to a cohort of eight moderately experienced orthopedists. The intraclass correlation coefficients for the CE, T&#x00F6;nnis, and Sharp angles were 0.957 (95% CI: 0.952&#x2013;0.962), 0.942 (95% CI: 0.937&#x2013;0.947), and 0.966 (95% CI: 0.964&#x2013;0.968), respectively. The system achieved a diagnostic F1 score of 0.863 (95% CI: 0.851&#x2013;0.876), significantly outperforming the orthopedist group (0.777, 95% CI: 0.737&#x2013;0.817, <inline-formula> <tex-math notation="LaTeX">$p = 0.005$ </tex-math></inline-formula>), as well as using clinical diagnostic criteria for each angle individually (<inline-formula> <tex-math notation="LaTeX">$p\lt 0.001$ </tex-math></inline-formula>). 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.
ISSN:2168-2372

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