ArticleDiagnostics (Basel, Switzerland)2022
A Deep Learning Algorithm for Radiographic Measurements of the Hip in Adults-A Reliability and Agreement Study.
Article in Diagnostics (Basel, Switzerland), 2022. 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, 25 citations in OpenAlex.
- An artificial intelligence framework for universal landmark matching and morphometry in musculoskeletal radiography.European radiology · 2026Article
- Deep Learning-based Detection of Hip Anatomical Landmarks in X-ray Images.The archives of bone and joint surgery · 2026Article
- Evaluation of AI Performance in Spinal Radiographic Measurements Compared to Radiologists: A Study of Accuracy and Efficiency.Journal of imaging · 2025Article
- A deep learning algorithm for radiographic measurements of the hip versus human CT measurements: An intermodality agreement study.Acta radiologica open · 2025Article
- Automated weight-bearing foot measurements using an artificial intelligence-based software.Skeletal radiology · 2025Article
- Evaluation of a deep learning software for automated measurements on full-leg standing radiographs.Knee surgery & related research · 2024Article
- Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.Skeletal radiology · 2024Review
- Radiographic Findings Associated With Mild Hip Dysplasia in 3869 Patients Using a Deep Learning Measurement Tool.Arthroplasty today · 2024Article
- Automated radiographic hip morphology measurements: An open-access method.Osteoarthritis imaging · 2024Article
- Application of deep learning for automated diagnosis and classification of hip dysplasia on plain radiographs.BMC musculoskeletal disorders · 2024Article
- Plain radiographic indices are reliable indicators for quantitative bone mineral density in male and female patients before total hip arthroplasty.Scientific reports · 2023Article
- Deep Learning for Fully Automated Radiographic Measurements of the Pelvis and Hip.Diagnostics (Basel, Switzerland) · 2023Article
- Patient positioning during the radiographic procedure affects the radiological signs of acetabular retroversion - A systematic review.Journal of clinical imaging science · 2023Review
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
Hip dysplasia (HD) is a frequent cause of hip pain in skeletally mature patients and may lead to osteoarthritis (OA). An accurate and early diagnosis may postpone, reduce or even prevent the onset of OA and ultimately hip arthroplasty at a young age. The overall aim of this study was to assess the reliability of an algorithm, designed to read pelvic anterior-posterior (AP) radiographs and to estimate the agreement between the algorithm and human readers for measuring (i) lateral center edge angle of Wiberg (LCEA) and (ii) Acetabular index angle (AIA). The algorithm was based on deep-learning models developed using a modified U-net architecture and ResNet 34. The newly developed algorithm was found to be highly reliable when identifying the anatomical landmarks used for measuring LCEA and AIA in pelvic radiographs, thus offering highly consistent measurement outputs. The study showed that manual identification of the same landmarks made by five specialist readers were subject to variance and the level of agreement between the algorithm and human readers was consequently poor with mean measured differences from 0.37 to 9.56° for right LCEA measurements. The algorithm displayed the highest agreement with the senior orthopedic surgeon. With further development, the algorithm may be a good alternative to humans when screening for HD.
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Registered trials
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