ArticleWorld wide web2023
Multi-task hourglass network for online automatic diagnosis of developmental dysplasia of the hip.
Article in World wide web, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Scaling Early Detection for Developmental Dysplasia of the Hip With Artificial Intelligence-Assisted Imaging.Cureus · 2026Review
- Assessing deep learning accuracy in the measurement of radiographic parameters in pediatric hip X-rays.BMC medical imaging · 2025Article
- Enhancing YOLO5 for the Assessment of Irregular Pelvic Radiographs with Multimodal Information.Journal of imaging informatics in medicine · 2024Article
- Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach.Journal of imaging · 2023Article
- Clinical thought-based software for diagnosing developmental dysplasia of the hip on pediatric pelvic radiographs.Frontiers in pediatrics · 2023Article
- Detection of developmental dysplasia of the hip in X-ray images using deep transfer learning.BMC medical informatics and decision making · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developmental dysplasia of the hip (DDH) is one of the most common diseases in children. Due to the experience-requiring medical image analysis work, online automatic diagnosis of DDH has intrigued the researchers. Traditional implementation of online diagnosis faces challenges with reliability and interpretability. In this paper, we establish an online diagnosis tool based on a multi-task hourglass network, which can accurately extract landmarks to detect the extent of hip dislocation and predict the age of the femoral head. Our method utilizes a multi-task hourglass network, which trains an encoder-decoder network to regress the landmarks and predict the developmental age for online DDH diagnosis. With the support of precise image analysis and fast GPU computing, our method can help overcome the shortage of medical resources and enable telehealth for DDH diagnosis. Applying this approach to a dataset of DDH X-ray images, we demonstrate 4.64 mean pixel error of landmark detection compared to the results of human experts. Moreover, we can improve the accuracy of the age prediction of femoral heads to 89
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