Evidence map›Paper›PMID 40139644›Full record

ArticleOsteoarthritis and cartilage2025

Using deep-learning based segmentation to enable spatial evaluation of knee osteoarthritis (SEKO) in rodent models.

Jacob L Griffith, Justin Joseph, Andrew Jensen, Scott Banks, Kyle D Allen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Osteoarthritis and cartilage, 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

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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

5 authors.

Jacob L GriffithJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA; Pain Research & Intervention Center of Excellence (PRICE), University of Florida, Gainesville, FL, USA.
Justin JosephJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Andrew JensenDepartment of Mechanical and Aerospace Engineering at the University of Florida, Gainesville, FL, USA.
Scott BanksDepartment of Mechanical and Aerospace Engineering at the University of Florida, Gainesville, FL, USA; Department of Orthopaedic Surgery and Sports Medicine, University of Florida, Gainesville, FL, USA.
Kyle D AllenJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA; Pain Research & Intervention Center of Excellence (PRICE), University of Florida, Gainesville, FL, USA; Department of Mechanical and Aerospace Engineering at the University of Florida, Gainesville, FL, USA; Department of Orthopaedic Surgery and Sports Medicine, University of Florida, Gainesville, FL, USA. Electronic address: kyle.allen@bme.ufl.edu.

Funding

Innervation of the knee and TMJUC2AR082196 · NIAMS · UNIVERSITY OF FLORIDA · PI Kyle D Allen, Alejandro Jose Almarza · 2022 to 2026
$11.2M
Treatment of Knee Osteoarthritis via Intra-articular Delivery of an Immunosuppressive EnzymeR01AR079874 · NIAMS · UNIVERSITY OF FLORIDA · PI Kyle D Allen · 2022 to 2026
$3.1M
OA Pathogenesis beyond Cartilage: A preclinical study of the sources of OA painR01AR071431 · NIAMS · UNIVERSITY OF FLORIDA · PI ALLEN, KYLE D · 2018 to 2022
$2.3M
Magnetic Capture of Osteoarthritis BiomarkersR01AR068424 · NIAMS · UNIVERSITY OF FLORIDA · PI ALLEN, KYLE D · 2015 to 2019
$1.6M
Function and Behavior Phenotype of Inflammatory Arthritis in the Rat Knee and TMJR00AR057426 · NIAMS · UNIVERSITY OF FLORIDA · PI ALLEN, KYLE D · 2011 to 2013
$735k
NIAMS NIH HHS R00 AR057426NIAMS NIH HHS R01 AR068424NIAMS NIH HHS R01 AR071431NIAMS NIH HHS R01AR071431NIAMS NIH HHS R01 AR079874NIAMS NIH HHS UC2 AR082196NIAMS NIH HHS UC2AR082196
6 · The paper itself

Abstract

objectiveIn preclinical models of osteoarthritis (OA), histology is commonly used to evaluate joint remodeling. The current study introduces a deep learning driven histological analysis pipeline for the spatial evaluation of knee osteoarthritis (SEKO) focused on quantifying and visualizing joint remodeling in the medial compartment of rodent knees.

methodsThe SEKO pipeline contains both segmentation and visualization tools. For segmentation, two separate convolutional neural network architectures, HRNet and U-Net, were considered for identifying multiple regions of interest. Following segmentation, SEKO calculates multiple morphometric and location dependent measures to summarize joint-level changes. Additionally, SEKO generates probabilistic heat maps for visualization of the spatial aspects of joint remodeling.

resultsSEKO incorporated the U-NET architecture - due to its higher prediction accuracy - and identified similar cartilage loss changes that were reported using by-hand segmentation in prior work. Additionally, SEKO enabled the detection of changes in subchondral bone area and location dependent bone remodeling. SEKO also enabled visualization of spatial changes in cartilage thinning and bone remodeling using probabilistic heat maps.

conclusionThe SEKO pipeline offers the potential for objective comparison of OA progression and therapeutic interventions through visualization of spatial and morphometric changes. SEKO is provided as an open-source tool for the OA research community, facilitating collaborative research efforts and comprehensive analysis of knee joint histology.

Indexed as

Arthritis, ExperimentalCartilage, ArticularDeep LearningOsteoarthritis, KneeAnimalsBone RemodelingDisease Models, AnimalKnee JointMiceRatsBone remodelingCartilage damageConvolutional neural networksKnee osteoarthritisPreclinical modelsQuantitative histology

Identifiers

PMID40139644
PMCPMC13632774

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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.