ArticleOsteoarthritis and cartilage2015
Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging.
Article in Osteoarthritis and cartilage, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 36 citations in OpenAlex.
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Artificial Intelligence Has Varied Diagnostic and Predictive Performance in Diagnosing Patellofemoral Osteoarthritis, Trochlear Dysplasia, and Patellofemoral Tracking Abnormalities: A Systematic Review.Arthroscopy, sports medicine, and rehabilitation · 2025Review
- Computational and Imaging Approaches for Precision Characterization of Bone, Cartilage, and Synovial Biomolecules.Journal of personalized medicine · 2025Review
- Articular Cartilage-From Basic Science Structural Imaging to Non-Invasive Clinical Quantitative Molecular Functional Information for AI Classification and Prediction.International journal of molecular sciences · 2023Review
- Spatial Gradients of Quantitative MRI as Biomarkers for Early Detection of Osteoarthritis: Data From Human Explants and the Osteoarthritis Initiative.Journal of magnetic resonance imaging : JMRI · 2023Article
- A Comprehensive Survey on Bone Segmentation Techniques in Knee Osteoarthritis Research: From Conventional Methods to Deep Learning.Diagnostics (Basel, Switzerland) · 2022Review
- Article
- Machine learning algorithms trained with pre-hospital acquired history-taking data can accurately differentiate diagnoses in patients with hip complaints.Acta orthopaedica · 2021Article
- Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach.Diagnostics (Basel, Switzerland) · 2021Article
- Radiographic interpretation using high-resolution Cbct to diagnose degenerative temporomandibular joint disease.PloS one · 2021Observational
- Imaging Manifestations and Evaluation of Postoperative Complications of Bone and Joint Infections under Deep Learning.Journal of healthcare engineering · 2021Article
- Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-rays and CT Scans Using Deep Learning and Machine Learning Methodologies.Diagnostics (Basel, Switzerland) · 2020Review
- Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning.Scientific reports · 2020Article
- Functional MRI for evaluation of hyaline cartilage extracelullar matrix, a physiopathological-based approach.The British journal of radiology · 2019Review
- Cell type discrimination based on image features of molecular component distribution.Scientific reports · 2018Article
- Machine Learning in Orthopedics: A Literature Review.Frontiers in bioengineering and biotechnology · 2018Review
- Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2017Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveThe purpose of this study is to evaluate the ability of machine learning to discriminate between magnetic resonance images (MRI) of normal and pathological human articular cartilage obtained under standard clinical conditions.
methodAn approach to MRI classification of cartilage degradation is proposed using pattern recognition and multivariable regression in which image features from MRIs of histologically scored human articular cartilage plugs were computed using weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM). The WND-CHRM method was first applied to several clinically available MRI scan types to perform binary classification of normal and osteoarthritic osteochondral plugs based on the Osteoarthritis Research Society International (OARSI) histological system. In addition, the image features computed from WND-CHRM were used to develop a multiple linear least-squares regression model for classification and prediction of an OARSI score for each cartilage plug.
resultsThe binary classification of normal and osteoarthritic plugs yielded results of limited quality with accuracies between 36% and 70%. However, multiple linear least-squares regression successfully predicted OARSI scores and classified plugs with accuracies as high as 86%. The present results improve upon the previously-reported accuracy of classification using average MRI signal intensities and parameter values.
conclusionMRI features detected by WND-CHRM reflect cartilage degradation status as assessed by OARSI histologic grading. WND-CHRM is therefore of potential use in the clinical detection and grading of osteoarthritis.
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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.