Related Experiment Video
Updated: Jul 5, 2025
07:45
The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report
Published on: August 4, 2022
3.4K
Artificial intelligence-based three-dimensional templating for total joint arthroplasty planning: a scoping review.
Ausberto Velasquez Garcia1,2, Lainey G Bukowiec1, Linjun Yang1
1Mayo Clinic Department of Orthopedic Surgery, Rochester, MN, 55905, USA.
International Orthopaedics
|January 15, 2024
Summary
Artificial intelligence (AI)-based 3D templating shows promise for improving total joint arthroplasty planning by reducing surgical time and enhancing implant accuracy. Further research is needed to address data and model development limitations for reliable clinical application.
Area of Science:
- Orthopedic Surgery
- Medical Imaging
- Artificial Intelligence
Background:
- Total joint arthroplasty (TJA) relies heavily on precise preoperative planning.
- Conventional templating methods using radiography have limitations in accuracy and efficiency.
- Advancements in artificial intelligence (AI) offer potential for enhanced surgical planning.
Purpose of the Study:
- To review the current research on AI-based 3D templating for TJA preoperative planning.
- To evaluate the effectiveness and limitations of AI in improving surgical planning accuracy and efficiency.
- To identify areas requiring further investigation for clinical implementation.
Main Methods:
- A scoping review adhering to PRISMA and PRISMA-ScR guidelines.
- Inclusion of studies utilizing AI-based 3D templating for primary or revision joint arthroplasty.
- Analysis of dataset/model characteristics, AI performance, time efficiency, and accuracy of component placement and size.
Main Results:
- Nine studies met inclusion criteria, focusing on CT/MRI-based AI templating for hip/knee arthroplasty.
- AI-based 3D templating demonstrated reduced planning time and improved implant size/position estimation compared to conventional methods.
- Significant gaps were identified in reporting data processing, model development, and testing methodologies.
Conclusions:
- AI-based 3D templating holds significant potential for improving preoperative planning in joint arthroplasty.
- This technology can lead to more accurate, personalized planning, potentially enhancing patient functional outcomes.
- Addressing deficiencies in data handling, model development, and testing is crucial for ensuring the reliability and reproducibility of AI templating systems.
