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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IEEE
2025-01-01
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| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
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| Online Access: | https://ieeexplore.ieee.org/document/10965781/ |
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| author | Yang Li Leo Yan Li-Han Hua Tian |
| author_facet | Yang Li Leo Yan Li-Han Hua Tian |
| author_sort | Yang Li |
| collection | DOAJ |
| description | 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 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, Tönnis, 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, Tönnis, 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, <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. |
| format | Article |
| id | doaj-art-a876cbcceefc47d4822214462f6504e0 |
| institution | DOAJ |
| issn | 2168-2372 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj-art-a876cbcceefc47d4822214462f6504e02025-08-20T03:11:06ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722025-01-011317418210.1109/JTEHM.2025.356087710965781Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the HipYang Li0https://orcid.org/0000-0002-4799-5397Leo Yan Li-Han1https://orcid.org/0000-0001-5059-0932Hua Tian2https://orcid.org/0000-0001-7139-3372Department of Orthopedics, Peking University Third Hospital, Beijing, ChinaThe Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, CanadaDepartment of Orthopedics, Peking University Third Hospital, Beijing, ChinaThe 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 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, Tönnis, 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, Tönnis, 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, <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.https://ieeexplore.ieee.org/document/10965781/Convolutional neural networkdevelopmental dysplasia of the hipkeypoint detectionradiographscoring system |
| spellingShingle | Yang Li Leo Yan Li-Han Hua Tian Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip IEEE Journal of Translational Engineering in Health and Medicine Convolutional neural network developmental dysplasia of the hip keypoint detection radiograph scoring system |
| title | Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip |
| title_full | Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip |
| title_fullStr | Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip |
| title_full_unstemmed | Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip |
| title_short | Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip |
| title_sort | deep learning based automatic diagnosis system for developmental dysplasia of the hip |
| topic | Convolutional neural network developmental dysplasia of the hip keypoint detection radiograph scoring system |
| url | https://ieeexplore.ieee.org/document/10965781/ |
| work_keys_str_mv | AT yangli deeplearningbasedautomaticdiagnosissystemfordevelopmentaldysplasiaofthehip AT leoyanlihan deeplearningbasedautomaticdiagnosissystemfordevelopmentaldysplasiaofthehip AT huatian deeplearningbasedautomaticdiagnosissystemfordevelopmentaldysplasiaofthehip |