ArticleThe Journal of arthroplasty2021
A Deep Learning Tool for Automated Radiographic Measurement of Acetabular Component Inclination and Version After Total Hip Arthroplasty.
Article in The Journal of arthroplasty, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- 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
- Arthrofibrosis After Total Knee Arthroplasty: Biological Mechanisms, Diagnosis, and Treatment: An International Consensus Symposium.HSS journal : the musculoskeletal journal of Hospital for Special Surgery · 2026Article
- Robotic-referenced automated measurement of acetabular cup orientation on postoperative CT: development and internal validation.Journal of robotic surgery · 2026Article
- Using an App based algorithm to determine axial rotation and pelvic tilt at the time of an AP radiograph: a sawbone study.Archives of orthopaedic and trauma surgery · 2026Article
- AI in Musculoskeletal Imaging: An End-to-End Perspective.Journal of clinical medicine · 2026Review
- A Hybrid Automatic Model for Circle Detection in X-Ray Imagery: A Case Study on Hip Prosthesis Wear.Bioengineering (Basel, Switzerland) · 2026Article
- From data to precision: The transformative role of AI and machine learning in modern orthopaedic practice.Journal of clinical orthopaedics and trauma · 2025Article
- Artificial intelligence to automatically measure on radiographs the postoperative positions of the glenosphere and pivot point after reverse total shoulder arthroplasty.JSES international · 2025Article
- Automated Web Tool Measurement of Total Hip Arthroplasty Acetabular Component Inclination and Anteversion Angles.Arthroplasty today · 2025Article
- Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip.IEEE journal of translational engineering in health and medicine · 2025Article
- Automated Assessment of Pelvic Longitudinal Rotation Using Computer Vision in Canine Hip Dysplasia Screening.Veterinary sciences · 2024Article
- CORR Insights®: Is Quantitative Radiographic Measurement of Acetabular Version Reliable in Anteverted and Retroverted Hips?Clinical orthopaedics and related research · 2024Article
- Analyzing Racial Differences in Imaging Joint Replacement Registries Using Generative Artificial Intelligence: Advancing Orthopaedic Data Equity.Arthroplasty today · 2024Article
- Leveraging digital twins for improved orthopaedic evaluation and treatment.Journal of experimental orthopaedics · 2024Article
- Radiographic Findings Associated With Mild Hip Dysplasia in 3869 Patients Using a Deep Learning Measurement Tool.Arthroplasty today · 2024Article
- Overview of Artificial Intelligence Research Within Hip and Knee Arthroplasty.Arthroplasty today · 2024Article
- AI applications in musculoskeletal imaging: a narrative review.European radiology experimental · 2024Review
- Automatic Segmentation and Radiologic Measurement of Distal Radius Fractures Using Deep Learning.Clinics in orthopedic surgery · 2024Article
- Predicting acetabular version in native hip joints through plain x-ray radiographs: a comparative analysis of convolutional neural network model and the current gold standard, with insights and implications for hip arthroplasty.Frontiers in surgery · 2024Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundInappropriate acetabular component angular position is believed to increase the risk of hip dislocation after total hip arthroplasty. However, manual measurement of these angles is time consuming and prone to interobserver variability. The purpose of this study was to develop a deep learning tool to automate the measurement of acetabular component angles on postoperative radiographs.
methodsTwo cohorts of 600 anteroposterior (AP) pelvis and 600 cross-table lateral hip postoperative radiographs were used to develop deep learning models to segment the acetabular component and the ischial tuberosities. Cohorts were manually annotated, augmented, and randomly split to train-validation-test data sets on an 8:1:1 basis. Two U-Net convolutional neural network models (one for AP and one for cross-table lateral radiographs) were trained for 50 epochs. Image processing was then deployed to measure the acetabular component angles on the predicted masks for anatomical landmarks. Performance of the tool was tested on 80 AP and 80 cross-table lateral radiographs.
resultsThe convolutional neural network models achieved a mean Dice similarity coefficient of 0.878 and 0.903 on AP and cross-table lateral test data sets, respectively. The mean difference between human-level and machine-level measurements was 1.35° (σ = 1.07°) and 1.39° (σ = 1.27°) for the inclination and anteversion angles, respectively. Differences of 5⁰ or more between human-level and machine-level measurements were observed in less than 2.5% of cases.
conclusionWe developed a highly accurate deep learning tool to automate the measurement of angular position of acetabular components for use in both clinical and research settings. LEVEL OF EVIDENCE: III.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.