ArticleJournal of orthopaedic surgery and research2022
Utility of a novel integrated deep convolutional neural network for the segmentation of hip joint from computed tomography images in the preoperative planning of total hip arthroplasty.
Article in Journal of orthopaedic surgery and research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Segmentation methods for quantifying X-ray Computed Tomography based biomarkers to assess hip fracture risk: a systematic literature review.Frontiers in bioengineering and biotechnology · 2024Pooled it
- AI-driven 3D virtual surgical planning in total hip arthroplasty: a machine learning approach for precision implant positioning and improved clinical outcomes.Journal of orthopaedic surgery and research · 2026Article
- AI-assisted preoperative planning combined with robotic-assisted total knee arthroplasty vs. conventional surgery: a retrospective controlled study.BMC musculoskeletal disorders · 2026Article
- AI-assisted 3D preoperative planning in primary total hip arthroplasty for secondary hip osteoarthritis: etiology-specific perioperative burden and early recovery in DDH versus post-septic sequelae.Frontiers in medicine · 2026Article
- Severity-dependent benefits of AI-assisted 3D planning in total hip arthroplasty: a Crowe I-IV subgroup and trend analysis.BMC musculoskeletal disorders · 2025Article
- AI-assisted 3D versus conventional 2D preoperative planning in total hip arthroplasty for Crowe type II-IV high hip dislocation: a two-year retrospective study.Journal of orthopaedic surgery and research · 2025Article
- Strategies for the Patient-Specific Implant Angle of Bone Scaffolds Using Optimization.Tissue engineering and regenerative medicine · 2025Article
- Efficacy of an artificial intelligence preoperative planning system for assisting in revision surgery after artificial total hip arthroplasty.BMC surgery · 2025Article
- The Role of Artificial Intelligence and Emerging Technologies in Advancing Total Hip Arthroplasty.Journal of personalized medicine · 2025Review
- Article
- Fair AI-powered orthopedic image segmentation: addressing bias and promoting equitable healthcare.Scientific reports · 2024Article
- Evaluating the accuracy of a new robotically assisted system in cadaveric total knee arthroplasty procedures.Journal of orthopaedic surgery and research · 2024Article
- [Effectiveness analysis of revision surgery after total hip arthroplasty assisted by artificial intelligence preoperative planning system].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2024Article
- A novel image augmentation based on statistical shape and intensity models: application to the segmentation of hip bones from CT images.European radiology experimental · 2023Article
- Optimization of Revision Hip Arthroplasty Workflow by Means of Detailed Pre-Surgical Planning Using Computed Tomography Data, Open-Source Software and Three-Dimensional-Printed Models.Diagnostics (Basel, Switzerland) · 2023Article
- Extra-articular Blocking Technique to Resolve Severe Acetabular Bone Defect in Developmental Dysplasia of the Hip.Orthopaedic surgery · 2023Observational
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
purposePreoperative three-dimensional planning is important for total hip arthroplasty. To simulate the placement of joint implants on computed tomography (CT), pelvis and femur must be segmented. Accurate and rapid segmentation of the hip joint is challenging. This study aimed to develop a novel deep learning network, named Changmugu Net (CMG Net), which could achieve accurate segmentation of the femur and pelvis.
methodsThe overall deep neural network architecture of CMG Net employed three interrelated modules. CMG Net included the 2D U-net to separate the bony and soft tissues. The modular hierarchy method was used for the main femur segmentation to achieve better performance. A layer classifier was adopted to localise femur layers among a series of CT scan images. The first module was a modified 2D U-net, which separated bony and soft tissues; it provided intermediate supervision for the main femur segmentation. The second module was the main femur segmentation, which was used to distinguish the femur from the acetabulum. The third module was the layer classifier, which served as a post-processor for the second module.
resultsThere was a much greater overlap in accuracy results with the "gold standard" segmentation than with competing networks. The dice overlap coefficient was 93.55% ± 5.57%; the mean surface distance was 1.34 ± 0.24 mm, and the Hausdorff distance was 4.19 ± 1.04 mm in the normal and diseased hips, which indicated greater accuracy than the other four competing networks. Moreover, the mean segmentation time of CMG Net was 25.87 ± 2.73 s, which was shorter than the times of the other four networks.
conclusionsThe prominent segmentation accuracy and run-time of CMG Net suggest that it is a reliable method for clinicians to observe anatomical structures of the hip joints, even in severely diseased cases.
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