ArticleMagnetic resonance in medicine2015
Classification of sodium MRI data of cartilage using machine learning.
Article in Magnetic resonance in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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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
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Who cites it
13 citing papers in PubMed.
- Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach.Bioengineering (Basel, Switzerland) · 2023Article
- Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading.Journal of clinical medicine · 2022Article
- Review of Quantitative Knee Articular Cartilage MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Article
- Quantification of Sodium Relaxation Times and Concentrations as Surrogates of Proteoglycan Content of Patellar CARTILAGE at 3T MRI.Diagnostics (Basel, Switzerland) · 2021Article
- Imaging the transmembrane and transendothelial sodium gradients in gliomas.Scientific reports · 2021Article
- [Perspectives of X-nuclei magnetic resonance imaging in neuro-oncology].Der Radiologe · 2021Review
- Intervertebral Disc Degeneration Is Associated With Aberrant Endplate Remodeling and Reduced Small Molecule Transport.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2020Article
- Magnetic Resonance Imaging of the Musculoskeletal System at 7T: Morphological Imaging and Beyond.Topics in magnetic resonance imaging : TMRI · 2019Review
- Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures From MRI Data.Journal of magnetic resonance imaging : JMRI · 2019Article
- Imaging of osteoarthritis-recent research developments and future perspective.The British journal of radiology · 2018Review
- Machine Learning in Orthopedics: A Literature Review.Frontiers in bioengineering and biotechnology · 2018Review
- New Techniques in MR Imaging of the Ankle and Foot.Magnetic resonance imaging clinics of North America · 2017Review
- Quantitative sodium magnetic resonance imaging of cartilage, muscle, and tendon.Quantitative imaging in medicine and surgery · 2016Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
purposeTo assess the possible utility of machine learning for classifying subjects with and subjects without osteoarthritis using sodium magnetic resonance imaging data. Theory: Support vector machine, k-nearest neighbors, naïve Bayes, discriminant analysis, linear regression, logistic regression, neural networks, decision tree, and tree bagging were tested.
methodsSodium magnetic resonance imaging with and without fluid suppression by inversion recovery was acquired on the knee cartilage of 19 controls and 28 osteoarthritis patients. Sodium concentrations were measured in regions of interests in the knee for both acquisitions. Mean (MEAN) and standard deviation (STD) of these concentrations were measured in each regions of interest, and the minimum, maximum, and mean of these two measurements were calculated over all regions of interests for each subject. The resulting 12 variables per subject were used as predictors for classification.
resultsEither Min [STD] alone, or in combination with Mean [MEAN] or Min [MEAN], all from fluid suppressed data, were the best predictors with an accuracy >74%, mainly with linear logistic regression and linear support vector machine. Other good classifiers include discriminant analysis, linear regression, and naïve Bayes.
conclusionMachine learning is a promising technique for classifying osteoarthritis patients and controls from sodium magnetic resonance imaging data.
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Registered trials
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