Abstract
Background: Total elbow arthroplasty rates continue to rise year after year with up to 7.6% annual growth, yet complication and failure rates have been reported as high as 90%. Proper identification of implants prior to revision is crucial to optimize clinical resources and postoperative outcomes. This study aims to evaluate a machine learning algorithm for quick and accurate identification of elbow implants from radiographic images. Methods: A total of 142 elbow implant radiographs representing 5 unique prostheses were obtained from deidentified patients within the Mount Sinai Health System in New York. Images were split into training (n = 97) and test (n = 45) sets. A modified version of the convolutional neural network DenseNet-121 was used to train and evaluate the data. The model was then assessed for accuracy, sensitivity, positive predictive value, and f-1 scores. Results: Overall model accuracy was 97.8% and top-3 accuracy was 100% for each of the 5 prostheses. Sensitivity, positive predictive value, and f-1 scores for the model were 0.978, 0.980, and 0.96, respectively. The algorithm took 14.10 seconds to classify the 45 images in the test set, averaging 0.31 seconds per image. Conclusions: The model demonstrated strong performance in the identification of elbow implants among the sample of 142 images and 5 implant types. While these results suggest promise in providing future clinical utility within surgical workflows, the method requires external validation and further testing with a greater number and wider variety of implants.
Original language | English |
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Pages (from-to) | 255-260 |
Number of pages | 6 |
Journal | Seminars in Arthroplasty JSES |
Volume | 33 |
Issue number | 2 |
DOIs | |
State | Published - Jun 2023 |
Keywords
- Algorithm
- Basic Science Study
- Computer Modeling
- Elbow
- Implant
- Machine learning
- Orthopedic
- Radiographic