ArticleJournal of orthopaedic surgery and research2023
Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis.
Article in Journal of orthopaedic surgery and research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating potential for AI automation of quantitative and semi-quantitative MRI scoring in arthritis, especially at the knee: a systematic literature review.Skeletal radiology · 2025Pooled it
- Soft tissue degeneration 10+ years after ACLR and their association with radiographic PTOA and knee pain development: radiomic analysis using qMRI approach in MOON nested on-site cohort.Osteoarthritis imaging · 2026Article
- From undruggable to degradable: A deep learning-enabled framework for precision orthopaedic protein degradation.Journal of orthopaedic translation · 2026Review
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- CILP Inhibits hyaline cartilage fibrosis and chondrocyte ferroptosis via keap1-Nrf2 axis in early osteoarthritis exercise therapy.Cellular and molecular life sciences : CMLS · 2026Article
- Objective stratification of knee osteoarthritis stages using a semi-supervised learning approach on multimodal MRI-CT cartilage features.Frontiers in digital health · 2026Article
- MRI-based morphometric analysis of the patellofemoral joint: diagnostic modeling of knee pathologies in adolescents.Frontiers in artificial intelligence · 2026Article
- An MRI-based radiomics framework for early identification and progression stratification in knee osteoarthritis: data from the osteoarthritis initiative.BMC musculoskeletal disorders · 2025Article
- Ultrasound-based radiomics and machine learning for enhanced diagnosis of knee osteoarthritis: Evaluation of diagnostic accuracy, sensitivity, specificity, and predictive value.European journal of radiology open · 2025Article
- Integration of longitudinal load-bearing tissue MRI radiomics and neural network to predict knee osteoarthritis incidence.Journal of orthopaedic translation · 2025Article
- Progress in multi-omics studies of osteoarthritis.Biomarker research · 2025Review
- Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts.Frontiers in immunology · 2025Article
- Radiomics signature of osteoarthritis: Current status and perspective.Journal of orthopaedic translation · 2024Review
- Radiomics analysis using magnetic resonance imaging of bone marrow edema for diagnosing knee osteoarthritis.Frontiers in bioengineering and biotechnology · 2024Article
- Artificial intelligence and machine learning for joint disorder detection: Promising advances in diagnostics.International journal of health sciencesArticle
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Authors and funding
5 authors.
Funding
Abstract
backgroundTo develop and assess the performance of machine learning (ML) models based on magnetic resonance imaging (MRI) radiomics analysis for knee osteoarthritis (KOA) diagnosis.
methodsThis retrospective study analysed 148 consecutive patients (72 with KOA and 76 without) with available MRI image data, where radiomics features in cartilage portions were extracted and then filtered. Intraclass correlation coefficient (ICC) was calculated to quantify the reproducibility of features, and a threshold of 0.8 was set. The training and validation cohorts consisted of 117 and 31 cases, respectively. Least absolute shrinkage and selection operator (LASSO) regression method was employed for feature selection. The ML classifiers were logistic regression (LR), K-nearest neighbour (KNN) and support vector machine (SVM). In each algorithm, ten models derived from all available planes of three joint compartments and their various combinations were, respectively, constructed for comparative analysis. The performance of classifiers was mainly evaluated and compared by receiver operating characteristic (ROC) analysis.
resultsAll models achieved satisfying performances, especially the Final model, where accuracy and area under ROC curve (AUC) of LR classifier were 0.968, 0.983 (0.957-1.000, 95% CI) in the validation cohort, and 0.940, 0.984 (0.969-0.995, 95% CI) in the training cohort, respectively.
conclusionThe MRI radiomics analysis represented promising performance in noninvasive and preoperative KOA diagnosis, especially when considering all available planes of all three compartments of knee joints.
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