ArticleComputational and structural biotechnology journal2020
Classifying shoulder implants in X-ray images using deep learning.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in orthopedic implant model classification: a systematic review.Skeletal radiology · 2022Pooled it
- Artificial intelligence and machine learning: an important new set of tools for clinical shoulder arthroplasty research.JSES international · 2026Review
- Deep learning classification models demonstrate high accuracy and clinical potential in radiograph interpretation in the arthroplasty clinical pathway: A systematic review and meta-analysis.Journal of experimental orthopaedics · 2025Review
- Comparison of Machine Learning Algorithms and Hybrid Computational Intelligence Algorithms for Rehabilitation Classification and Prognosis in Reverse Total Shoulder Arthroplasty.Bioengineering (Basel, Switzerland) · 2025Article
- An accelerated deep learning model can accurately identify clinically important humeral and scapular landmarks on plain radiographs obtained before and after anatomic arthroplasty.International orthopaedics · 2025Article
- The Development of a Yolov8-Based Model for the Measurement of Critical Shoulder Angle (CSA), Lateral Acromion Angle (LAA), and Acromion Index (AI) from Shoulder X-ray Images.Diagnostics (Basel, Switzerland) · 2024Article
- AI applications in musculoskeletal imaging: a narrative review.European radiology experimental · 2024Review
- Fully automatic algorithm for detecting and tracking anatomical shoulder landmarks on fluoroscopy images with artificial intelligence.European radiology · 2024Article
- Improving the classification of veterinary thoracic radiographs through inter-species and inter-pathology self-supervised pre-training of deep learning models.Scientific reports · 2023Article
- Artificial intelligence-based applications in shoulder surgery leaves much to be desired: a systematic review.JSES reviews, reports, and techniques · 2023Review
- Development of a machine learning algorithm to identify total and reverse shoulder arthroplasty implants from X-ray images.Journal of orthopaedics · 2023Article
- Automated Detection of Surgical Implants on Plain Knee Radiographs Using a Deep Learning Algorithm.Medicina (Kaunas, Lithuania) · 2022Article
- Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
- A Novel Hybrid Machine Learning Based System to Classify Shoulder Implant Manufacturers.Healthcare (Basel, Switzerland) · 2022Article
- Deep learning for accurately recognizing common causes of shoulder pain on radiographs.Skeletal radiology · 2022Article
- Artificial Intelligence-Based Solution in Personalized Computer-Aided Arthroscopy of Shoulder Prostheses.Journal of personalized medicine · 2022Article
- Knee Implant Identification by Fine-Tuning Deep Learning Models.Indian journal of orthopaedics · 2021Article
- Artificial Intelligence-Based Recognition of Different Types of Shoulder Implants in X-ray Scans Based on Dense Residual Ensemble-Network for Personalized Medicine.Journal of personalized medicine · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Total Shoulder Arthroplasty (TSA) is a type of surgery in which the damaged ball of the shoulder is replaced with a prosthesis. Many years later, this prosthesis may be in need of servicing or replacement. In some situations, such as when the patient has changed his country of residence, the model and the manufacturer of the prosthesis may be unknown to the patient and primary doctor. Correct identification of the implant's model prior to surgery is required for selecting the correct equipment and procedure. We present a novel way to automatically classify shoulder implants in X-ray images. We employ deep learning models and compare their performance to alternative classifiers, such as random forests and gradient boosting. We find that deep convolutional neural networks outperform other classifiers significantly if and only if out-of-domain data such as ImageNet is used to pre-train the models. In a data set containing X-ray images of shoulder implants from 4 manufacturers and 16 different models, deep learning is able to identify the correct manufacturer with an accuracy of approximately 80% in 10-fold cross validation, while other classifiers achieve an accuracy of 56% or less. We believe that this approach will be a useful tool in clinical practice, and is likely applicable to other kinds of prostheses.
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Registered trials
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