Novel Technique for the Identification of Hip Implants Using Artificial Intelligence

• Published in: The Journal of arthroplasty Vol. 39; no. 5; pp. 1178 - 1183
• Main Authors: Antonson, Neil W., Buckner, Brandt C., Konigsberg, Beau S., Hartman, Curtis W., Garvin, Kevin L., Kildow, Beau J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.05.2024
• Subjects: artificial intelligence; implant identification; machine learning; revision total hip arthroplasty; total hip arthroplasty; total hip arthroplasty; machine learning; artificial intelligence; revision total hip arthroplasty; implant identification
• ISSN: 0883-5403; 1532-8406
• DOI: 10.1016/j.arth.2024.02.001

The anticipated growth of total hip arthroplasty will result in an increased need for revision total hip arthroplasty. Preoperative planning, including identifying current implants, is critical for successful revision surgery. Artificial intelligence (AI) is promising for aiding clinical decision-making, including hip implant identification. However, previous studies have limitations such as small datasets, dissimilar stem designs, limited scalability, and the need for AI expertise. To address these limitations, we developed a novel technique to generate large datasets, tested radiographically similar stems, and demonstrated scalability utilizing a no-code machine learning solution. We trained, validated, and tested an automated machine learning-implemented convolutional neural network to classify 9 radiographically similar femoral implants with a metaphyseal-fitting wedge taper design. Our novel technique uses computed tomography-derived projections of a 3-dimensional scanned implant model superimposed within a computed tomography pelvis volume. We employed computer-aided design modeling and MATLAB to process and manipulate the images. This generated 27,020 images for training (22,957) and validation (4,063) sets. We obtained 786 test images from various sources. The performance of the model was evaluated by calculating sensitivity, specificity, and accuracy. Our machine learning model discriminated the 9 implant models with a mean accuracy of 97.4%, sensitivity of 88.4%, and specificity of 98.5%. Our novel hip implant detection technique accurately identified 9 radiographically similar implants. The method generates large datasets, is scalable, and can include historic or obscure implants. The no-code machine learning model demonstrates the feasibility of obtaining meaningful results without AI expertise, encouraging further research in this area.