ReviewOsteoarthritis and cartilage2019
Diagnosing osteoarthritis from T
Review in Osteoarthritis and cartilage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 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
53 citing papers in PubMed.
- Evaluation of Pre-Operative Cartilage and Meniscus Ultrashort Echo Time T2* as Biomarkers of Long-Term Clinical Outcomes Following Anterior Cruciate Ligament Reconstruction.Journal of magnetic resonance imaging : JMRI · 2026Article
- Deep learning-enhanced, accelerated cartilage TSkeletal radiology · 2026Article
- Multimodal model integrating ultrasound and demographic data for the diagnosis of knee osteoarthritis.BMC medical imaging · 2026Article
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Advancing deep learning based knee cartilage segmentation in MRI: Innovations, challenges and applications.Osteoarthritis and cartilage open · 2026Article
- Review of CNN-Based Approaches for Preprocessing, Segmentation and Classification of Knee Osteoarthritis.Diagnostics (Basel, Switzerland) · 2026Review
- Rapid and robust quantitative cartilage assessment for the clinical setting: deep learning-enhanced accelerated T2 mapping.Skeletal radiology · 2026Article
- Model-based deep learning with fully connected neural networks for accelerated magnetic resonance parameter mapping.International journal of computer assisted radiology and surgery · 2025Article
- Simplifying Knee OA Prognosis: A Deep Learning Approach Using Radiographs and Minimal Clinical Inputs.Diagnostics (Basel, Switzerland) · 2025Article
- Early diagnosis of knee osteoarthritis severity using vision transformer.BMC musculoskeletal disorders · 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
- Nanoparticle-enabled molecular imaging diagnosis of osteoarthritis.Materials today. Bio · 2025Review
- Deep learning classification models demonstrate high accuracy and clinical potential in radiograph interpretation in the arthroplasty clinical pathway: A systematic review and meta-analysis.Journal of experimental orthopaedics · 2025Review
- Biomarker-Guided Imaging and AI-Augmented Diagnosis of Degenerative Joint Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Improving Accuracy and Reproducibility of Cartilage TJournal of magnetic resonance imaging : JMRI · 2025Article
- Integration of longitudinal load-bearing tissue MRI radiomics and neural network to predict knee osteoarthritis incidence.Journal of orthopaedic translation · 2025Article
- Foundations of a knee joint digital twin from qMRI biomarkers for osteoarthritis and knee replacement.NPJ digital medicine · 2025Article
- Quantitative MRI methods for the assessment of structure, composition, and function of musculoskeletal tissues in basic research and preclinical applications.Magma (New York, N.Y.) · 2024Review
- Standardized maps - an emerging approach to leverage quantitative information in knee imaging.Osteoarthritis imaging · 2024Article
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
Abstract
objectiveWe aim to study to what extent conventional and deep-learning-based T
methodsT
resultsThe best shallow model was obtained when the first ten Principal Components, demographics and pain score were included as features (AUC = 77.77%, Sensitivity = 67.01%, Specificity = 71.79%). In comparison, DenseNet trained on raw T
conclusionIn this study, we presented a Magnetic Resonance Imaging (MRI)-based data-driven platform using T
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.