ArticleDiagnostics (Basel, Switzerland)2023
Deep Learning for Fully Automated Radiographic Measurements of the Pelvis and Hip.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- An artificial intelligence framework for universal landmark matching and morphometry in musculoskeletal radiography.European radiology · 2026Article
- Evaluation of the reliability of novel pelvic X-ray assessment software: CalculOrther.Acta orthopaedica et traumatologica turcica · 2026Article
- A Hybrid Automatic Model for Circle Detection in X-Ray Imagery: A Case Study on Hip Prosthesis Wear.Bioengineering (Basel, Switzerland) · 2026Article
- Deep Learning-based Detection of Hip Anatomical Landmarks in X-ray Images.The archives of bone and joint surgery · 2026Article
- Exploring the role of artificial intelligence in orthopedic medical education: A narrative review.Journal of clinical orthopaedics and trauma · 2025Article
- Automated weight-bearing foot measurements using an artificial intelligence-based software.Skeletal radiology · 2025Article
- A Novel Model Based on CNN-ViT Fusion and Ensemble Learning for the Automatic Detection of Pes Planus.Journal of clinical medicine · 2024Article
- Attitude of aspiring orthopaedic surgeons towards artificial intelligence: a multinational cross-sectional survey study.Archives of orthopaedic and trauma surgery · 2024Article
- Orthopaedic surgeons display a positive outlook towards artificial intelligence: A survey among members of the AGA Society for Arthroscopy and Joint Surgery.Journal of experimental orthopaedics · 2024Article
- Automated radiographic hip morphology measurements: An open-access method.Osteoarthritis imaging · 2024Article
- Overview of Artificial Intelligence Research Within Hip and Knee Arthroplasty.Arthroplasty today · 2024Article
- Automated assessment of pelvic radiographs using deep learning: A reliable diagnostic tool for pelvic malalignment.Heliyon · 2024Article
- The promise and limitations of artificial intelligence in musculoskeletal imaging.Frontiers in radiology · 2023Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
The morphometry of the hip and pelvis can be evaluated in native radiographs. Artificial-intelligence-assisted analyses provide objective, accurate, and reproducible results. This study investigates the performance of an artificial intelligence (AI)-based software using deep learning algorithms to measure radiological parameters that identify femoroacetabular impingement and hip dysplasia. Sixty-two radiographs (124 hips) were manually evaluated by three observers and fully automated analyses were performed by an AI-driven software (HIPPO™, ImageBiopsy Lab, Vienna, Austria). We compared the performance of the three human readers with the HIPPO™ using a Bayesian mixed model. For this purpose, we used the absolute deviation from the median ratings of all readers and HIPPO™. Our results indicate a high probability that the AI-driven software ranks better than at least one manual reader for the majority of outcome measures. Hence, fully automated analyses could provide reproducible results and facilitate identifying radiographic signs of hip disorders.
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Registered trials
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