ReviewOsteoarthritis and cartilage open2020
Machine learning in knee osteoarthritis: A review.
Review in Osteoarthritis and cartilage open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled 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.
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
57 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Predicting Knee osteoarthritis progression using explainable machine learning and clinical imaging data.Osteoarthritis and cartilage open · 2026Article
- Identification of alkaline phosphatase as a putative biomarker of anti-NGF treatment-associated arthropathies: Machine learning-assisted analyses of clinical trial data.Osteoarthritis and cartilage open · 2026Article
- Review of CNN-Based Approaches for Preprocessing, Segmentation and Classification of Knee Osteoarthritis.Diagnostics (Basel, Switzerland) · 2026Review
- Development and validation of a generalisable survival model to predict osteoarthritis progression.Osteoarthritis and cartilage open · 2025Article
- Healthy knee asymmetry is a potential risk factor for knee osteoarthritis: Data from the osteoarthritis initiative.Osteoarthritis imaging · 2025Article
- Machine Learning Assisted Pre-operative planning for Total Hip Arthroplasty: Accuracy Validation and Post Operative Critical Analysis- A Retrospective Study.Journal of orthopaedic case reports · 2025Article
- Functional data analysis of ground reaction forces combined with clinical measures for early knee osteoarthritis detection.Scientific reports · 2025Article
- Augmenting a ResNet + BiLSTM Deep Learning Model with Clinical Mobility Data Helps Outperform a Heuristic Frequency-Based Model for Walking Bout Segmentation.Sensors (Basel, Switzerland) · 2025Article
- The Evolution of Machine Learning and Its Applications in Orthopaedics: A Bibliometric Analysis.Cureus · 2025Review
- Knee osteoarthritis rehabilitation: an integrated framework of exercise, nutrition, biomechanics, and physical therapist guidance-a narrative review.European journal of medical research · 2025Review
- Predicting Knee Osteoarthritis Severity from Radiographic Predictors: Data from the Osteoarthritis Initiative.Annals of biomedical engineering · 2025Article
- Interpretable Machine Learning for Osteopenia Detection: A Proof-of-Concept Study Using Bioelectrical Impedance in Perimenopausal Women.Journal of functional morphology and kinesiology · 2025Article
- Predictive validity of consensus-based MRI definition of osteoarthritis plus radiographic osteoarthritis for the progression of knee osteoarthritis: A longitudinal cohort study.Osteoarthritis and cartilage open · 2025Article
- Beyond symptomatic alignment: evaluating the integration of causal mechanisms in matching animal models with human pathotypes in osteoarthritis research.Arthritis research & therapy · 2025Review
- Constructing machine learning-based risk prediction model for osteoarthritis in population aged 45 and above: NHANES 2011-2018.Scientific reports · 2025Article
- Advancing osteoarthritis research: the role of AI in clinical, imaging and omics fields.Bone research · 2025Review
- Screening necroptosis genes influencing osteoarthritis development based on machine learning.Scientific reports · 2025Article
- A multidimensional approach reveals the function of lactylation related genes in osteoarthritis.Scientific reports · 2025Article
- ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs.IEEE transactions on medical imaging · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The purpose of present review paper is to introduce the reader to key directions of Machine Learning techniques on the diagnosis and predictions of knee osteoarthritis. Design: This survey was based on research articles published between 2006 and 2019. The articles were divided into four categories, namely (i) predictions/regression, (ii) classification, (iii) optimum post-treatment planning techniques and (iv) segmentation. The grouping was based on the application domain of each study. Results: The survey findings are reported outlining the main characteristics of the proposed learning algorithms, the application domains, the data sources investigated and the quality of the results. Conclusions: Knee osteoarthritis is a big data problem in terms of data complexity, heterogeneity and size as it has been commonly considered in the literature. Machine Learning has attracted significant interest from the scientific community to cope with the aforementioned challenges and thus lead to new automated pre- or post-treatment solutions that utilize data from the greatest possible variety of sources.
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.