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Artificial intelligence in total knee arthroplasty: clinical applications and implications
Kyeong Baek Kim1,2, Gi Beom Kim3, Jun-Ho Kim4
1Department of Orthopedic Surgery, Pusan National University Yangsan Hospital, Research Institute for Convergence of Biomedical Science and Technology, 20, Geumo-ro, Mulgeum-eup, Yangsan-si, Gyeongsangnam-do, Yangsan, Republic of Korea.
Artificial intelligence (AI) enhances total knee arthroplasty (TKA) by improving patient selection, surgical planning, and postoperative care. Overcoming challenges like algorithmic bias is key to realizing AI's full potential in orthopedic surgery.
Area of Science:
- Orthopedic Surgery
- Artificial Intelligence
- Machine Learning
- Deep Learning
Background:
- Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), is increasingly integrated into total knee arthroplasty (TKA).
- AI facilitates analysis of complex datasets for evidence-based clinical decision-making throughout the TKA process.
- These technologies aim to enhance accuracy, efficiency, and personalize patient care in TKA.
Purpose of the Study:
- To review the current applications and impact of AI in total knee arthroplasty.
- To highlight the utility of AI across various stages of the TKA procedure.
- To identify the challenges and future directions for AI implementation in TKA.
Main Methods:
- Review of AI applications in patient selection, preoperative planning, intraoperative assistance, and postoperative monitoring in TKA.
- Analysis of ML algorithms for predicting complications and DL techniques for anatomical reconstruction and implant sizing.
- Examination of AI-assisted robotic systems, sensor technologies, and wearable devices for TKA.
Main Results:
- AI algorithms show high accuracy in predicting postoperative complications (AUC up to 0.842).
- DL models achieve over 90% accuracy in component sizing, outperforming traditional methods.
- AI-integrated tools demonstrate potential for reducing hospital readmissions and improving rehabilitation.
Conclusions:
- AI holds significant potential to revolutionize TKA, enabling precise, data-driven, and patient-centered care.
- Overcoming challenges such as algorithmic bias, generalizability, explainability, and regulatory hurdles is crucial for widespread adoption.
- Multicenter validation and explainable AI are necessary to build clinical trust and ensure reliable implementation for enhanced patient outcomes.
