ArticleScientific reports2021
Uncovering associations between data-driven learned qMRI biomarkers and chronic pain.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Beyond the algorithms: Challenges in predicting knee osteoarthritis pain from a machine learning point of view.Osteoarthritis imaging · 2026Article
- Machine learning models for clinical and structural knee osteoarthritis prediction: Recent advancements and future directions.Osteoarthritis and cartilage open · 2025Article
- Artificial intelligence in musculoskeletal applications: a primer for radiologists.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Foundations of a knee joint digital twin from qMRI biomarkers for osteoarthritis and knee replacement.NPJ digital medicine · 2025Article
- Standardized maps - an emerging approach to leverage quantitative information in knee imaging.Osteoarthritis imaging · 2024Article
- Effects of TJournal of magnetic resonance imaging : JMRI · 2024Article
- Identifying significant structural factors associated with knee pain severity in patients with osteoarthritis using machine learning.Scientific reports · 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
- LigaNET: A multi-modal deep learning approach to predict the risk of subsequent anterior cruciate ligament injury after surgery.medRxiv : the preprint server for health sciences · 2023Article
- Patterns of variation among baseline femoral and tibial cartilage thickness and clinical features: Data from the osteoarthritis initiative.Osteoarthritis and cartilage open · 2023Article
- K2S Challenge: From Undersampled K-Space to Automatic Segmentation.Bioengineering (Basel, Switzerland) · 2023Article
- FDA/Arthritis Foundation osteoarthritis drug development workshop recap: Assessment of long-term benefit.Seminars in arthritis and rheumatism · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Knee pain is the most common and debilitating symptom of knee osteoarthritis (OA). While there is a perceived association between OA imaging biomarkers and pain, there are weak or conflicting findings for this relationship. This study uses Deep Learning (DL) models to elucidate associations between bone shape, cartilage thickness and T
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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.