ArticleJournal of personalized medicine2021
Automatic Hip Detection in Anteroposterior Pelvic Radiographs-A Labelless Practical Framework.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- An automated hip fracture detection, classification system on pelvic radiographs and comparison with 35 clinicians.Scientific reports · 2025Article
- Deep learning-based automated measurement of hip key angles and auxiliary diagnosis of developmental dysplasia of the hip.BMC musculoskeletal disorders · 2024Article
- A Review of Medical Image Registration for Different Modalities.Bioengineering (Basel, Switzerland) · 2024Review
- Deep learning approach to femoral AVN detection in digital radiography: differentiating patients and pre-collapse stages.BMC musculoskeletal disorders · 2024Article
- Predicting the Risk of Total Hip Replacement by Using A Deep Learning Algorithm on Plain Pelvic Radiographs: Diagnostic Study.JMIR formative research · 2023Article
- The Feasibility and Performance of Total Hip Replacement Prediction Deep Learning Algorithm with Real World Data.Bioengineering (Basel, Switzerland) · 2023Article
- Evaluation of ensemble strategy on the development of multiple view ankle fracture detection algorithm.The British journal of radiology · 2023Article
- Taking the Next Step in Personalised Orthopaedic Implantation.Journal of personalized medicine · 2022Article
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Authors and funding
9 authors.
Funding
Abstract
Automated detection of the region of interest (ROI) is a critical step in the two-step classification system in several medical image applications. However, key information such as model parameter selection, image annotation rules, and ROI confidence score are essential but usually not reported. In this study, we proposed a practical framework of ROI detection by analyzing hip joints seen on 7399 anteroposterior pelvic radiographs (PXR) from three diverse sources. We presented a deep learning-based ROI detection framework utilizing a single-shot multi-box detector with a customized head structure based on the characteristics of the obtained datasets. Our method achieved average intersection over union (IoU) = 0.8115, average confidence = 0.9812, and average precision with threshold IoU = 0.5 (AP50) = 0.9901 in the independent testing set, suggesting that the detected hip regions appropriately covered the main features of the hip joints. The proposed approach featured flexible loose-fitting labeling, customized model design, and heterogeneous data testing. We demonstrated the feasibility of training a robust hip region detector for PXRs. This practical framework has a promising potential for a wide range of medical image applications.
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Registered trials
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