Validation of a machine learning technique for segmentation and pose estimation in single plane fluoroscopy
Kinematics of total knee replacements (TKR) play an important role in assessing the success of a procedure and would be a valuable addition to clinical practice; however, measuring TKR kinematics is time consuming and labour intensive. Recently, an automatic single‐plane fluoroscopic method utilizin...
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| Veröffentlicht in: | Journal of orthopaedic research Jg. 41; H. 8; S. 1767 - 1773 |
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| Sprache: | Englisch |
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01.08.2023
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| ISSN: | 0736-0266, 1554-527X, 1554-527X |
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| Abstract | Kinematics of total knee replacements (TKR) play an important role in assessing the success of a procedure and would be a valuable addition to clinical practice; however, measuring TKR kinematics is time consuming and labour intensive. Recently, an automatic single‐plane fluoroscopic method utilizing machine learning has been developed to facilitate a quick and simple process for measuring TKR kinematics. This study aimed to validate the new automatic single‐plane technique using biplanar radiostereometric analysis (RSA) as the gold standard. Twenty‐four knees were imaged at various angles of flexion in a dedicated RSA lab and 113 image pairs were obtained. Only the lateral RSA images were used for the automatic single‐plane technique to simulate single‐plane fluoroscopy. Two networks helped automate the kinematics measurement process, one segmented implant components and the other generated an initial pose estimate for the optimization algorithm. Kinematics obtained via the automatic single plane and manual biplane techniques were compared using root‐mean‐square error and Bland–Altman plots. Two observers measured the kinematics using the automated technique and results were compared with assess reproducibility. Root‐mean‐square errors were 0.8 mm for anterior–posterior translation, 0.5 mm for superior–inferior translation, 2.6 mm for medial–lateral translation, 1.0° for flexion–extension, 1.2° for abduction–adduction, and 1.7° for internal–external rotation. Reproducibility, reported as root‐mean‐square errors between operator measurements, was submillimeter for in‐plane translations and below 2° for all rotations. Clinical Significance: The advantages of the automated single plane technique should aid in the kinematic measurement process and help researchers and clinicians perform TKR kinematic analyses. |
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| AbstractList | Kinematics of total knee replacements (TKR) play an important role in assessing the success of a procedure and would be a valuable addition to clinical practice; however, measuring TKR kinematics is time consuming and labour intensive. Recently, an automatic single-plane fluoroscopic method utilizing machine learning has been developed to facilitate a quick and simple process for measuring TKR kinematics. This study aimed to validate the new automatic single-plane technique using biplanar radiostereometric analysis (RSA) as the gold standard. Twenty-four knees were imaged at various angles of flexion in a dedicated RSA lab and 113 image pairs were obtained. Only the lateral RSA images were used for the automatic single-plane technique to simulate single-plane fluoroscopy. Two networks helped automate the kinematics measurement process, one segmented implant components and the other generated an initial pose estimate for the optimization algorithm. Kinematics obtained via the automatic single plane and manual biplane techniques were compared using root-mean-square error and Bland-Altman plots. Two observers measured the kinematics using the automated technique and results were compared with assess reproducibility. Root-mean-square errors were 0.8 mm for anterior-posterior translation, 0.5 mm for superior-inferior translation, 2.6 mm for medial-lateral translation, 1.0° for flexion-extension, 1.2° for abduction-adduction, and 1.7° for internal-external rotation. Reproducibility, reported as root-mean-square errors between operator measurements, was submillimeter for in-plane translations and below 2° for all rotations. Clinical Significance: The advantages of the automated single plane technique should aid in the kinematic measurement process and help researchers and clinicians perform TKR kinematic analyses. Kinematics of total knee replacements (TKR) play an important role in assessing the success of a procedure and would be a valuable addition to clinical practice; however, measuring TKR kinematics is time consuming and labour intensive. Recently, an automatic single-plane fluoroscopic method utilizing machine learning has been developed to facilitate a quick and simple process for measuring TKR kinematics. This study aimed to validate the new automatic single-plane technique using biplanar radiostereometric analysis (RSA) as the gold standard. Twenty-four knees were imaged at various angles of flexion in a dedicated RSA lab and 113 image pairs were obtained. Only the lateral RSA images were used for the automatic single-plane technique to simulate single-plane fluoroscopy. Two networks helped automate the kinematics measurement process, one segmented implant components and the other generated an initial pose estimate for the optimization algorithm. Kinematics obtained via the automatic single plane and manual biplane techniques were compared using root-mean-square error and Bland-Altman plots. Two observers measured the kinematics using the automated technique and results were compared with assess reproducibility. Root-mean-square errors were 0.8 mm for anterior-posterior translation, 0.5 mm for superior-inferior translation, 2.6 mm for medial-lateral translation, 1.0° for flexion-extension, 1.2° for abduction-adduction, and 1.7° for internal-external rotation. Reproducibility, reported as root-mean-square errors between operator measurements, was submillimeter for in-plane translations and below 2° for all rotations. Clinical Significance: The advantages of the automated single plane technique should aid in the kinematic measurement process and help researchers and clinicians perform TKR kinematic analyses.Kinematics of total knee replacements (TKR) play an important role in assessing the success of a procedure and would be a valuable addition to clinical practice; however, measuring TKR kinematics is time consuming and labour intensive. Recently, an automatic single-plane fluoroscopic method utilizing machine learning has been developed to facilitate a quick and simple process for measuring TKR kinematics. This study aimed to validate the new automatic single-plane technique using biplanar radiostereometric analysis (RSA) as the gold standard. Twenty-four knees were imaged at various angles of flexion in a dedicated RSA lab and 113 image pairs were obtained. Only the lateral RSA images were used for the automatic single-plane technique to simulate single-plane fluoroscopy. Two networks helped automate the kinematics measurement process, one segmented implant components and the other generated an initial pose estimate for the optimization algorithm. Kinematics obtained via the automatic single plane and manual biplane techniques were compared using root-mean-square error and Bland-Altman plots. Two observers measured the kinematics using the automated technique and results were compared with assess reproducibility. Root-mean-square errors were 0.8 mm for anterior-posterior translation, 0.5 mm for superior-inferior translation, 2.6 mm for medial-lateral translation, 1.0° for flexion-extension, 1.2° for abduction-adduction, and 1.7° for internal-external rotation. Reproducibility, reported as root-mean-square errors between operator measurements, was submillimeter for in-plane translations and below 2° for all rotations. Clinical Significance: The advantages of the automated single plane technique should aid in the kinematic measurement process and help researchers and clinicians perform TKR kinematic analyses. |
| Author | Broberg, Jordan S. Jensen, Andrew Teeter, Matthew G. Chen, Joanna Banks, Scott A. |
| Author_xml | – sequence: 1 givenname: Jordan S. orcidid: 0000-0001-8923-8338 surname: Broberg fullname: Broberg, Jordan S. email: jbroberg@uwo.ca organization: Lawson Health Research Institute – sequence: 2 givenname: Joanna surname: Chen fullname: Chen, Joanna organization: Western University – sequence: 3 givenname: Andrew surname: Jensen fullname: Jensen, Andrew organization: University of Florida – sequence: 4 givenname: Scott A. orcidid: 0000-0003-0404-6826 surname: Banks fullname: Banks, Scott A. organization: University of Florida – sequence: 5 givenname: Matthew G. orcidid: 0000-0002-3911-3171 surname: Teeter fullname: Teeter, Matthew G. organization: Western University and London Health Sciences Centre |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36691875$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1177_09544119241232271 crossref_primary_10_1016_j_knee_2025_02_014 crossref_primary_10_1016_j_jbiomech_2024_112172 crossref_primary_10_2106_JBJS_OA_24_00151 crossref_primary_10_3390_bioengineering11111108 |
| Cites_doi | 10.1016/j.arth.2017.09.064 10.1016/j.arth.2016.12.054 10.5435/JAAOS-D-16-00628 10.1016/j.arth.2021.10.024 10.1097/00003086-199610000-00015 10.1097/01.blo.0000092986.12414.b5 10.1007/s11999-008-0440-z 10.1007/s00167-018-4842-5 10.1109/TMI.2017.2773398 10.1016/j.knee.2019.11.020 10.1109/10.495283 10.1016/j.arth.2019.07.046 10.1016/j.arth.2019.05.037 10.1016/j.knee.2021.05.011 10.1016/j.arth.2017.09.035 10.1109/42.811310 10.1097/00003086-199112000-00036 10.1016/j.gaitpost.2016.03.006 10.1016/j.jbiomech.2010.10.033 10.1016/S0021-9290(01)00028-8 10.1016/j.knee.2020.07.092 10.1016/j.arth.2023.05.029 10.1109/34.385980 10.1109/ICCV.2019.01076 10.3109/17453678909149328 10.1097/01.blo.0000148777.98244.84 10.1109/TMI.2003.820027 10.1073/pnas.1806905115 10.1016/j.jbiomech.2014.02.031 |
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| Keywords | single plane fluoroscopy kinematics total knee replacement machine learning validation |
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| Title | Validation of a machine learning technique for segmentation and pose estimation in single plane fluoroscopy |
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