دورية أكاديمية
Joint Track Machine Learning: An Autonomous Method of Measuring Total Knee Arthroplasty Kinematics From Single-Plane X-Ray Images.
| العنوان: | Joint Track Machine Learning: An Autonomous Method of Measuring Total Knee Arthroplasty Kinematics From Single-Plane X-Ray Images. |
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| المؤلفون: | Jensen AJ; Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida., Flood PDL; Department of Computer Science, University of Cambridge, Cambridge, UK., Palm-Vlasak LS; Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida., Burton WS; Center for Orthopaedic Biomechanics, University of Denver, Denver, Colorado., Chevalier A; Electromechanical, Systems and Metals Engineering, Ghent University, Ghent, Belgium; Department of Electromechanics, CoSysLab, University of Antwerp, Antwerp, Belgium; AnSyMo/Cosys, Flanders Make, The Strategic Research Centre for the Manufacturing Industry, Antwerp, Belgium., Rullkoetter PJ; Center for Orthopaedic Biomechanics, University of Denver, Denver, Colorado., Banks SA; Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida. |
| المصدر: | The Journal of arthroplasty [J Arthroplasty] 2023 Oct; Vol. 38 (10), pp. 2068-2074. Date of Electronic Publication: 2023 May 24. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Taylor and Francis Country of Publication: United States NLM ID: 8703515 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-8406 (Electronic) Linking ISSN: 08835403 NLM ISO Abbreviation: J Arthroplasty Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: New Brunswick, NJ : Taylor and Francis Original Publication: [New York, NY : Churchill Livingstone, c1986- |
| مواضيع طبية MeSH: | Arthroplasty, Replacement, Knee*, Humans ; Biomechanical Phenomena ; X-Rays ; Femur ; Machine Learning |
| مستخلص: | Background: Dynamic radiographic measurements of 3-dimensional (3-D) total knee arthroplasty (TKA) kinematics have provided important information for implant design and surgical technique for over 30 years. However, current methods of measuring TKA kinematics are too cumbersome, inaccurate, or time-consuming for practical clinical application. Even state-of-the-art techniques require human-supervision to obtain clinically reliable kinematics. Eliminating human supervision could potentially make this technology practical for clinical use. Methods: We demonstrate a fully autonomous pipeline for quantifying 3D-TKA kinematics from single-plane radiographic imaging. First, a convolutional neural network (CNN) segmented the femoral and tibial implants from the image. Second, those segmented images were compared to precomputed shape libraries for initial pose estimates. Lastly, a numerical optimization routine aligned 3D implant contours and fluoroscopic images to obtain the final implant poses. Results: The autonomous technique reliably produces kinematic measurements comparable to human-supervised measures, with root-mean-squared differences of less than 0.7 mm and 4° for our test data, and 0.8 mm and 1.7° for external validation studies. Conclusion: A fully autonomous method to measure 3D-TKA kinematics from single-plane radiographic images produces results equivalent to a human-supervised method, and may soon make it practical to perform these measurements in a clinical setting. (Copyright © 2023 Elsevier Inc. All rights reserved.) |
| فهرسة مساهمة: | Keywords: convolutional neural network; kinematics; machine learning; model-image registration; total knee arthroplasty |
| تواريخ الأحداث: | Date Created: 20230526 Date Completed: 20230925 Latest Revision: 20230927 |
| رمز التحديث: | 20231215 |
| DOI: | 10.1016/j.arth.2023.05.029 |
| PMID: | 37236287 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 1532-8406 |
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| DOI: | 10.1016/j.arth.2023.05.029 |