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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Main Authors: 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
Format: Article
Language:English
Published: Elsevier 2025-03-01
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.
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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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