Comprehensive Summary
This study presents an innovative, no-code machine learning (ML) technique aimed at addressing the challenges of implant identification in total hip arthroplasty (THA revision surgeries. With the annual demand for THA projected to reach 572,000 and revisions 96,700 by 2030, accurate implant identification is critical for successfu surgical outcomes. Traditional identification methods are often hindered by incomplet medical records, patient history gaps, and the difficulty of distinguishing radiographicall similar implants. To overcome these limitations, the researchers generated a dataset of 27,020 images, split into 22,957 training, 4,063 validation, and 786 test images. The ML model was trained to classify nine radiographically similar femoral implants with a metaphyseal-fitting wedge taper design. The model achieved a weighted mea accuracy of 97.4%, sensitivity of 88.4%, and specificity of 98.5%. Implant-speci accuracy ranged from 95% to 99%, and the confusion matrix highlighted high performance across all implant types, with minor misclassifications occurring due t morphological deviations. This study also demonstrated scalability, enabling the inclusion of rare or historic implants, and validated the feasibility of using AI without specialized expertise
Outcomes and Implications
The findings of this study underscore the significant impact AI can have in improving efficiency and accuracy of THA revision surgeries. Accurate implant identificati demonstrated by a 97.4% mean accuracy and 98.5% specificity, directly addresse inefficiencies in current methods, which consume an average of 20 minutes per cas and lead to an annual burden of 41 hours per surgeon. By automating the identificatio process, the model reduces procedural delays, surgical complexity, and healthcare costs. Additionally, the ability to classify historic and obscure implants, which are underrepresented in existing datasets, ensures broader applicability and better patient outcomes in complex cases The graphical analysis of the model's performance revealed robust sensitivity and specificity across all nine implants, with the highest sensitivity (100%) observed in th AMIS implant and the highest specificity (99.85%) in the Actis implant. These metric indicate the model's reliability in distinguishing between similar implant types, minimizing errors that could complicate surgeries. For example, misclassifications in th Corail implant group (3 false positives and 3 false negatives out of 33 true positives) highlight areas for future improvement through dataset expansion and enhanced modeling The implications extend beyond implant identification. The scalable approach provides framework for addressing other challenges in orthopedic surgery, including acetabular component identification and integration into preoperative planning workflows. Fut efforts should focus on expanding datasets, particularly for underrepresented implants, and fostering collaborations with manufacturers to include a wider variety of CAD models. These advancements will solidify AI’s role in optimizing surgical workflows reducing healthcare costs, and improving clinical outcomes for THA patients.