In The Journal of arthroplasty ; h5-index 65.0
BACKGROUND : Surgical management of complications following knee arthroplasty demands accurate and timely identification of implant manufacturer and model. Automated image processing using deep machine learning has been previously developed and internally validated; however, external validation is essential prior to scaling clinical implementation for generalizability.
METHODS : We trained, validated, and externally tested a deep learning system to classify knee arthroplasty systems as one of the 9 models from four manufacturers derived from 4,724 original, retrospectively collected antero-posterior (AP) plain knee radiographs across three academic referral centers. From these radiographs, 3,568 were used for training, 412 for validation, and 744 for external testing. Augmentation was applied to the training set (n = 3,568,000) to increase model robustness. Performance was determined by area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Implant identification processing speed was calculated. The training and testing sets were drawn from statistically different populations of implants (P < .001).
RESULTS : After 1,000 training epochs by the deep learning system, the system discriminated 9 implant models with a mean area under the receiver operating characteristic curve of 0.989, accuracy of 97.4%, sensitivity of 89.2%, and specificity of 99.0% in the external testing dataset of 744 AP radiographs. The software classified implants at a mean speed of 0.02 seconds per image.
CONCLUSION : An artificial intelligence-based software identifying knee arthroplasty implants demonstrated excellent internal and external validation. Although continued surveillance is necessary with implant library expansion, this software represents responsible and meaningful clinical application of AI with immediate potential to globally scale and assist in preoperative planning prior to revision knee arthroplasty.
Karnuta Jaret M, Shaikh Hashim J F, Murphy Michael P, Brown Nicholas M, Pearle Andrew D, Nawabi Danyal H, Chen Antonia F, Ramkumar Prem N
2023-Mar-18
artificial intelligence, implant identification, knee arthroplasty, machine learning, revision arthroplasty
