ArticleJournal of digital imaging2022
Recognition and Segmentation of Individual Bone Fragments with a Deep Learning Approach in CT Scans of Complex Intertrochanteric Fractures: A Retrospective Study.
Article in Journal of digital imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 19 citations in OpenAlex.
- A Benchmark X-ray Dataset for Pediatric Supracondylar Humerus Fractures with Improved YOLOv11-Based Detection.Scientific data · 2026Article
- Artificial intelligence and robotic technologies redefining precision and personalization in orthopedic surgery: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Transformer-based and CNN-based models for clinically effective 2D and 3D pelvic bone segmentation in CT imaging.BMC musculoskeletal disorders · 2025Article
- Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions.MedComm · 2025Review
- Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review.Journal of experimental orthopaedics · 2025Review
- Automatic pelvic fracture segmentation: a deep learning approach and benchmark dataset.Frontiers in medicine · 2025Article
- Development and reporting of artificial intelligence in osteoporosis management.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024Review
- [Research progress on weak state of lateral wall in intertrochanteric fractures].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2024Review
- Diagnosis and detection of bone fracture in radiographic images using deep learning approaches.Frontiers in medicine · 2024Article
- Hybrid SFNet Model for Bone Fracture Detection and Classification Using ML/DL.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
5 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The characteristics of bone fragments are the main influencing factors for the choice of treatment in intertrochanteric fractures. This study aimed to develop a deep learning algorithm for recognizing and segmenting individual fragments in CT images of complex intertrochanteric fractures for orthopedic surgeons. This study was based on 160 hip CT scans (43,510 images) of complex fractures of three types based on the Evans-Jensen classification (40 cases of type 3 (IIA) fractures, 80 cases of type 4 (IIB)fractures, and 40 cases of type 5 (III)fractures) retrospectively. The images were randomly split into two groups to construct a training set of 120 CT scans (32,045 images) and a testing set of 40 CT scans (11,465 images). A deep learning model was built into a cascaded architecture composed by a convolutional neural network (CNN) for location of the fracture ROI and another CNN for recognition and segmentation of individual fragments within the ROI. The accuracy of object detection and dice coefficient of segmentation of individual fragments were used to evaluate model performance. The model yielded an average accuracy of 89.4% for individual fragment recognition and an average dice coefficient of 90.5% for segmentation in CT images. The results demonstrated the feasibility of recognition and segmentation of individual fragments in complex intertrochanteric fractures with a deep learning approach. Altogether, these promising results suggest the potential of our model to be applied to many clinical scenarios.
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