Knee Implant Identification by Fine-Tuning Deep Learning Models
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Authors
Batta, Vineet
Kiruthika, M.
Issue Date
2021-09-28
Type
Scientific Paper
Language
Keywords
Knee implant , Revision arthroplasty , Implant identifcation , Deep learning , Image processing
Alternative Title
Abstract
Background Identification of implant model from primary knee arthroplasty in pre-op planning of revision surgery is a
challenging task with added delay. The direct impact of this inability to identify the implants in time leads to the increase
in complexity in surgery. Deep learning in the medical field for diagnosis has shown promising results in getting better with
every iteration. This study aims to find an optimal solution for the problem of identification of make and model of knee
arthroplasty prosthesis using automated deep learning models.
Methods Deep learning algorithms were used to classify knee arthroplasty implant models. The training, validation and
test comprised of 1078 radiographs with a total of 6 knee arthroplasty implant models with anterior–posterior (AP) and
lateral views. The performance of the model was calculated using accuracy, sensitivity, and area under the receiver-operating
characteristic curve (AUC), which were compared against multiple models trained for comparative in-depth analysis with
saliency maps for visualization.
Results After training for a total of 30 epochs on all 6 models, the model performing the best obtained an accuracy of 96.38%,
the sensitivity of 97.2% and AUC of 0.985 on an external testing dataset consisting of 162 radiographs. The best performing
model correctly and uniquely identified the implants which could be visualized using saliency maps.
Conclusion Deep learning models can be used to differentiate between 6 knee arthroplasty implant models. Saliency maps
give us a better understanding of which regions the model is focusing on while predicting the results.
Description
Citation
Sharma, S. et al. (2021) “Knee implant identification by fine-tuning deep learning models,” Indian Journal of Orthopaedics, 55(5), pp. 1295–1305. Available at: https://doi.org/10.1007/s43465-021-00529-9.