Evidence map›Paper›PMID 39802079›Full record

ArticleOsteoarthritis and cartilage open2025

Tibiofemoral cartilage strain and recovery following a 3-mile run measured using deep learning segmentation of bone and cartilage.

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 and 5 more

Abstract read
In one paragraph

Article in Osteoarthritis and cartilage open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Patrick X BradleyDepartment of Mechanical Engineering and Materials Science, Duke University, United States.
Sophia Y Kim-WangDepartment of Orthopaedic Surgery, Duke University School of Medicine, United States.
Brooke S BlaisdellDepartment of Physics, Duke University, United States.
Alexie D RiofrioDepartment of Radiology, Duke University School of Medicine, United States.
Amber T CollinsDepartment of Orthopaedic Surgery, Duke University School of Medicine, United States.
Lauren N HeckelmanDepartment of Orthopaedic Surgery, Duke University School of Medicine, United States.
Eziamaka C ObunadikeDepartment of Orthopaedic Surgery, Duke University School of Medicine, United States.
Margaret R WidmyerDepartment of Orthopaedic Surgery, Duke University School of Medicine, United States.
Chinmay S ParanjapeDepartment of Orthopaedic Surgery, Duke University School of Medicine, United States.
Bryan S CrookDepartment of Orthopaedic Surgery, Duke University School of Medicine, United States.
Nimit K LadDepartment of Orthopaedic Surgery, Duke University School of Medicine, United States.
Edward G SutterDepartment of Orthopaedic Surgery, Duke University School of Medicine, United States.
Brian P MannDepartment of Mechanical Engineering and Materials Science, Duke University, United States.
Charles E SpritzerDepartment of Orthopaedic Surgery, Duke University School of Medicine, United States.
Louis E DeFrateDepartment of Mechanical Engineering and Materials Science, Duke University, United States.

Funding

In Vivo Knee Motion and ACL Biomechanics During Dynamic ActivitiesR01AR065527 · NIAMS · DUKE UNIVERSITY · PI DEFRATE, LOUIS E. · 2014 to 2024
$4.8M
Biomechanical and Biological Predictors of Cartilage Health Following Meniscus InjuryR01AR079184 · NIAMS · DUKE UNIVERSITY · PI DEFRATE, LOUIS E., GOODE, ADAM · 2021 to 2025
$3.5M
Weight loss, in vivo cartilage mechanics, and joint healthR01AR074800 · NIAMS · DUKE UNIVERSITY · PI DEFRATE, LOUIS E. · 2019 to 2023
$1.8M
In vivo ACL mechanics and risk factors for injuryR01AR082665 · NIAMS · DUKE UNIVERSITY · PI DEFRATE, LOUIS E. · 2024 to 2025
$1.3M
NIAMS NIH HHS R01 AR065527NIAMS NIH HHS R01 AR074800NIAMS NIH HHS R01 AR079184NIAMS NIH HHS R01 AR082665
6 · The paper itself

Abstract

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.

Indexed as

Auto-segmentationCartilage deformationCartilage thicknessMagnetic resonance imagingUNet

Identifiers

PMID39802079
PMCPMC11720442

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.