ArticleThe Journal of arthroplasty2019
Variation in the Thickness of Knee Cartilage. The Use of a Novel Machine Learning Algorithm for Cartilage Segmentation of Magnetic Resonance Images.
Article in The Journal of arthroplasty, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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.
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.
Who cites it
29 citing papers in PubMed.
- Are magnetic resonance imaging features associated with intermittent and constant pain in knee osteoarthritis? A cross-sectional study.Osteoarthritis and cartilage open · 2026Article
- KneeXNet-2.5D: a clinically-oriented and explainable deep learning framework for MRI-based knee cartilage and meniscus segmentation.npj health systems · 2026Article
- Cartilage thickness at the apex of femoral resections in kinematically aligned total knee arthroplasty is close to 2.5 millimeters.Journal of experimental orthopaedics · 2026Article
- Appropriately planned resection depth can impact outcomes after robotic total knee arthroplasty.Journal of orthopaedics · 2025Article
- Validation of Articular Cartilage Depth in Total Knee Arthroplasty.Arthroplasty today · 2025Article
- Machine learning models for clinical and structural knee osteoarthritis prediction: Recent advancements and future directions.Osteoarthritis and cartilage open · 2025Article
- Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives.Military Medical Research · 2025Review
- Advances and Challenges in the Pursuit of Disease-Modifying Osteoarthritis Drugs: A Review of 2010-2024 Clinical Trials.Biomedicines · 2025Review
- Integration of Artificial Intelligence for Diagnostic Methods in Musculoskeletal Conditions: A Systematic Review.Cureus · 2025Review
- Is Native Joint Line More Accurately Restored with Robotic Assisted Total Knee Arthroplasty than with Conventional Instruments?Journal of orthopaedic case reports · 2025Article
- Article
- Unbiased Method to Determine Articular Cartilage Thickness Using a Three-Dimensional Model Derived from Laser Scanning: Demonstration on the Distal Femur.Bioengineering (Basel, Switzerland) · 2024Article
- An MRI-Derived Formula for Estimating the Native Joint Line Position in the Presence of Distal Femoral Bone Loss.Cureus · 2024Article
- Machine Learning Models for Prediction of Progression of Knee Osteoarthritis: A Comprehensive Analysis.Journal of pharmacy & bioallied sciences · 2024Article
- Does robotic-assisted unicompartmental knee arthroplasty restore native joint line more accurately than with conventional instruments?Journal of robotic surgery · 2024Article
- The knee connectome: A novel tool for studying spatiotemporal change in cartilage thickness.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2024Article
- Automated measurement and grading of knee cartilage thickness: a deep learning-based approach.Frontiers in medicine · 2024Article
- Unrestricted kinematic alignment corrects fixed flexion contracture in robotically aligned total knees without raising the joint line in extension.Journal of experimental orthopaedics · 2023Article
- Associations between weight change, knee subcutaneous fat and cartilage thickness in overweight and obese individuals: 4-Year data from the osteoarthritis initiative.Osteoarthritis and cartilage · 2023Article
- Injectable and biodegradable piezoelectric hydrogel for osteoarthritis treatment.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundThe variation in articular cartilage thickness (ACT) in healthy knees is difficult to quantify and therefore poorly documented. Our aims are to (1) define how machine learning (ML) algorithms can automate the segmentation and measurement of ACT on magnetic resonance imaging (MRI) (2) use ML to provide reference data on ACT in healthy knees, and (3) identify whether demographic variables impact these results.
methodsPatients recruited into the Osteoarthritis Initiative with a radiographic Kellgren-Lawrence grade of 0 or 1 with 3D double-echo steady-state MRIs were included and their gender, age, and body mass index were collected. Using a validated ML algorithm, 2 orthogonal points on each femoral condyle were identified (distal and posterior) and ACT was measured on each MRI. Site-specific ACT was compared using paired t-tests, and multivariate regression was used to investigate the risk-adjusted effect of each demographic variable on ACT.
resultsA total of 3910 MRI were included. The average femoral ACT was 2.34 mm (standard deviation, 0.71; 95% confidence interval, 0.95-3.73). In multivariate analysis, distal-medial (-0.17 mm) and distal-lateral cartilage (-0.32 mm) were found to be thinner than posterior-lateral cartilage, while posterior-medial cartilage was found to be thicker (0.21 mm). In addition, female sex was found to negatively impact cartilage thickness (OR, -0.36; all values: P < .001).
conclusionML was effectively used to automate the segmentation and measurement of cartilage thickness on a large number of MRIs of healthy knees to provide normative data on the variation in ACT in this population. We further report patient variables that can influence ACT. Further validation will determine whether this technique represents a powerful new tool for tracking the impact of medical intervention on the progression of articular cartilage degeneration.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.