ArticleFrontiers in medicine2022
Development and Validation of an Artificial Intelligence Preoperative Planning System for Total Hip Arthroplasty.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Comparison of Artificial Intelligence and Traditional Methods in Preoperative Planning for Primary Total Hip Arthroplasty: A Systematic Review and Meta-Analysis.Orthopaedic surgery · 2025Pooled it
- The role of artificial intelligence in preoperative planning for Total Hip Arthroplasty: a systematic review.Frontiers in artificial intelligence · 2024Pooled it
- Clinical application of artificial intelligence-assisted three-dimensional planning in direct anterior approach hip arthroplasty.International orthopaedics · 2024Trial
- Anatomy-driven automated subsegmentation of pelvic and proximal femoral CT into clinically relevant subregions and landmarks.BMC medical imaging · 2026Article
- Artificial Intelligence in Arthroplasty: A Comprehensive Structured Critical Review and Descriptive Evidence Map of Validation, Uncertainty, and Workflow Integration.Bioengineering (Basel, Switzerland) · 2026Review
- Automated Femoral, Acetabular, and Global Offset Measurements on Pelvis Radiograph Using Deep Learning.Hip & pelvis · 2026Article
- Three-dimensional and artificial intelligence-assisted preoperative templating in total hip arthroplasty: a systematic review and meta-analysis.Arthroplasty (London, England) · 2026Review
- Optimizing arthroplasty outcomes: the impact of artificial intelligence and robotic assistance.Annals of medicine and surgery (2012) · 2026Review
- Artificial Intelligence Applications in Implant Positioning, Dislocation Risk Prediction, and Surgical Indications in Orthopaedic Surgery.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Orthopaedics: Clinical Performance, Limitations, and Translational Readiness-A Review.Journal of clinical medicine · 2026Review
- 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
- The Application of artificial intelligence in periprosthetic joint infection.Journal of advanced research · 2026Review
- Optimized treatment of hip ankylosis in spondylitis with direct anterior approach arthroplasty using AI navigation: a case report.Annals of joint · 2026Article
- The accuracy of artificial intelligence in 3D preoperative planning for total hip arthroplasty: A systematic review and meta-analysis.Journal of experimental orthopaedics · 2026Review
- Semi-supervised learning for colposcopic image classification using generative adversarial networks.Frontiers in medicine · 2026Article
- Current Advances of Artificial Intelligence and Machine Learning in Orthopaedics: A Focus on Hip Surgery.Bioengineering (Basel, Switzerland) · 2025Review
- Development of a Deep Learning Model for Hip Arthroplasty Templating Using Anteroposterior Hip Radiograph.Journal of clinical medicine · 2025Article
- Review
- A practical guide to the implementation of artificial intelligence in orthopaedic research-Part 3: How orthopaedic research benefits from the implementation of artificial intelligence.Journal of experimental orthopaedics · 2025Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Accurate preoperative planning is essential for successful total hip arthroplasty (THA). However, the requirements of time, manpower, and complex workflow for accurate planning have limited its application. This study aims to develop a comprehensive artificial intelligent preoperative planning system for THA (AIHIP) and validate its accuracy in clinical performance. Methods: Over 1.2 million CT images from 3,000 patients were included to develop an artificial intelligence preoperative planning system (AIHIP). Deep learning algorithms were developed to facilitate automatic image segmentation, image correction, recognition of preoperative deformities and postoperative simulations. A prospective study including 120 patients was conducted to validate the accuracy, clinical outcome and radiographic outcome. Results: The comprehensive workflow was integrated into the AIHIP software. Deep learning algorithms achieved an optimal Dice similarity coefficient (DSC) of 0.973 and loss of 0.012 at an average time of 1.86 ± 0.12 min for each case, compared with 185.40 ± 21.76 min for the manual workflow. In clinical validation, AIHIP was significantly more accurate than X-ray-based planning in predicting the component size with more high offset stems used. Conclusion: The use of AIHIP significantly reduced the time and manpower required to conduct detailed preoperative plans while being more accurate than traditional planning method. It has potential in assisting surgeons, especially beginners facing the fast-growing need for total hip arthroplasty with easy accessibility.
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