Tibiofemoral cartilage strain and recovery following a 3-mile run measured using deep learning segmentation of bone and cartilage
Objective: We sought to measure the deformation of tibiofemoral cartilage immediately following a 3-mile treadmill run, as well as the recovery of cartilage thickness the following day. To enable these measurements, we developed and validated deep learning models to automate tibiofemoral cartilage a...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Osteoarthritis and Cartilage Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665913124001237 |
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| author | Patrick X. Bradley Sophia Y. Kim-Wang Brooke S. Blaisdell Alexie D. Riofrio Amber T. Collins Lauren N. Heckelman Eziamaka C. Obunadike Margaret R. Widmyer Chinmay S. Paranjape Bryan S. Crook Nimit K. Lad Edward G. Sutter Brian P. Mann Charles E. Spritzer Louis E. DeFrate |
| author_facet | Patrick X. Bradley Sophia Y. Kim-Wang Brooke S. Blaisdell Alexie D. Riofrio Amber T. Collins Lauren N. Heckelman Eziamaka C. Obunadike Margaret R. Widmyer Chinmay S. Paranjape Bryan S. Crook Nimit K. Lad Edward G. Sutter Brian P. Mann Charles E. Spritzer Louis E. DeFrate |
| author_sort | Patrick X. Bradley |
| collection | DOAJ |
| description | Objective: We sought to measure the deformation of tibiofemoral cartilage immediately following a 3-mile treadmill run, as well as the recovery of cartilage thickness the following day. To enable these measurements, we developed and validated deep learning models to automate tibiofemoral cartilage and bone segmentation from double-echo steady-state magnetic resonance imaging (MRI) scans. Design: Eight asymptomatic male participants arrived at 7 a.m., rested supine for 45 min, underwent pre-exercise MRI, ran 3 miles on a treadmill, and finally underwent post-exercise MRI. To assess whether cartilage recovered to its baseline thickness, participants returned the following morning at 7 a.m., rested supine for 45 min, and underwent a final MRI session. These images were used to generate 3D models of the tibia, femur, and cartilage surfaces at each time point. Site-specific tibial and femoral cartilage thicknesses were measured from each 3D model. To aid in these measurements, deep learning segmentation models were developed. Results: All trained deep learning models demonstrated repeatability within 0.03 mm or approximately 1 % of cartilage thickness. The 3-mile run induced mean compressive strains of 5.4 % (95 % CI = 4.1 to 6.7) and 2.3 % (95 % CI = 0.6 to 4.0) for the tibial and femoral cartilage, respectively. Furthermore, both tibial and femoral cartilage thicknesses returned to within 1 % of baseline thickness the following day. Conclusions: The 3-mile treadmill run induced a significant decrease in both tibial and femoral cartilage thickness; however, this was largely ameliorated the following morning. |
| format | Article |
| id | doaj-art-ce90503e63304686bcb71299edb10fb1 |
| institution | DOAJ |
| issn | 2665-9131 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Osteoarthritis and Cartilage Open |
| spelling | doaj-art-ce90503e63304686bcb71299edb10fb12025-08-20T02:55:29ZengElsevierOsteoarthritis and Cartilage Open2665-91312025-03-017110055610.1016/j.ocarto.2024.100556Tibiofemoral cartilage strain and recovery following a 3-mile run measured using deep learning segmentation of bone and cartilagePatrick X. Bradley0Sophia Y. Kim-Wang1Brooke S. Blaisdell2Alexie D. Riofrio3Amber T. Collins4Lauren N. Heckelman5Eziamaka C. Obunadike6Margaret R. Widmyer7Chinmay S. Paranjape8Bryan S. Crook9Nimit K. Lad10Edward G. Sutter11Brian P. Mann12Charles E. Spritzer13Louis E. DeFrate14Department of Mechanical Engineering and Materials Science, Duke University, United StatesDepartment of Orthopaedic Surgery, Duke University School of Medicine, United StatesDepartment of Physics, Duke University, United StatesDepartment of Radiology, Duke University School of Medicine, United StatesDepartment of Orthopaedic Surgery, Duke University School of Medicine, United StatesDepartment of Orthopaedic Surgery, Duke University School of Medicine, United StatesDepartment of Orthopaedic Surgery, Duke University School of Medicine, United StatesDepartment of Orthopaedic Surgery, Duke University School of Medicine, United StatesDepartment of Orthopaedic Surgery, Duke University School of Medicine, United StatesDepartment of Orthopaedic Surgery, Duke University School of Medicine, United StatesDepartment of Orthopaedic Surgery, Duke University School of Medicine, United StatesDepartment of Orthopaedic Surgery, Duke University School of Medicine, United StatesDepartment of Mechanical Engineering and Materials Science, Duke University, United StatesDepartment of Orthopaedic Surgery, Duke University School of Medicine, United States; Department of Radiology, Duke University School of Medicine, United StatesDepartment of Mechanical Engineering and Materials Science, Duke University, United States; Department of Orthopaedic Surgery, Duke University School of Medicine, United States; Department of Biomedical Engineering, Duke University, United States; Corresponding author. Duke University, Box 3093, Durham, NC, 27710, United States.Objective: We sought to measure the deformation of tibiofemoral cartilage immediately following a 3-mile treadmill run, as well as the recovery of cartilage thickness the following day. To enable these measurements, we developed and validated deep learning models to automate tibiofemoral cartilage and bone segmentation from double-echo steady-state magnetic resonance imaging (MRI) scans. Design: Eight asymptomatic male participants arrived at 7 a.m., rested supine for 45 min, underwent pre-exercise MRI, ran 3 miles on a treadmill, and finally underwent post-exercise MRI. To assess whether cartilage recovered to its baseline thickness, participants returned the following morning at 7 a.m., rested supine for 45 min, and underwent a final MRI session. These images were used to generate 3D models of the tibia, femur, and cartilage surfaces at each time point. Site-specific tibial and femoral cartilage thicknesses were measured from each 3D model. To aid in these measurements, deep learning segmentation models were developed. Results: All trained deep learning models demonstrated repeatability within 0.03 mm or approximately 1 % of cartilage thickness. The 3-mile run induced mean compressive strains of 5.4 % (95 % CI = 4.1 to 6.7) and 2.3 % (95 % CI = 0.6 to 4.0) for the tibial and femoral cartilage, respectively. Furthermore, both tibial and femoral cartilage thicknesses returned to within 1 % of baseline thickness the following day. Conclusions: The 3-mile treadmill run induced a significant decrease in both tibial and femoral cartilage thickness; however, this was largely ameliorated the following morning.http://www.sciencedirect.com/science/article/pii/S2665913124001237Auto-segmentationCartilage deformationCartilage thicknessMagnetic resonance imagingUNet |
| spellingShingle | Patrick X. Bradley Sophia Y. Kim-Wang Brooke S. Blaisdell Alexie D. Riofrio Amber T. Collins Lauren N. Heckelman Eziamaka C. Obunadike Margaret R. Widmyer Chinmay S. Paranjape Bryan S. Crook Nimit K. Lad Edward G. Sutter Brian P. Mann Charles E. Spritzer Louis E. DeFrate Tibiofemoral cartilage strain and recovery following a 3-mile run measured using deep learning segmentation of bone and cartilage Osteoarthritis and Cartilage Open Auto-segmentation Cartilage deformation Cartilage thickness Magnetic resonance imaging UNet |
| title | Tibiofemoral cartilage strain and recovery following a 3-mile run measured using deep learning segmentation of bone and cartilage |
| title_full | Tibiofemoral cartilage strain and recovery following a 3-mile run measured using deep learning segmentation of bone and cartilage |
| title_fullStr | Tibiofemoral cartilage strain and recovery following a 3-mile run measured using deep learning segmentation of bone and cartilage |
| title_full_unstemmed | Tibiofemoral cartilage strain and recovery following a 3-mile run measured using deep learning segmentation of bone and cartilage |
| title_short | Tibiofemoral cartilage strain and recovery following a 3-mile run measured using deep learning segmentation of bone and cartilage |
| title_sort | tibiofemoral cartilage strain and recovery following a 3 mile run measured using deep learning segmentation of bone and cartilage |
| topic | Auto-segmentation Cartilage deformation Cartilage thickness Magnetic resonance imaging UNet |
| url | http://www.sciencedirect.com/science/article/pii/S2665913124001237 |
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