ArticleClinical image-based procedures, distributed and collaborative learning, artificial intelligence for combating COVID-19 and secure and privacy-preserving machine learning : 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Work...
TMJOAI: An Artificial Web-Based Intelligence Tool for Early Diagnosis of the Temporomandibular Joint Osteoarthritis.
Article in Clinical image-based procedures, distributed and collaborative learning, artificial intelligence for combating COVID-19 and secure and privacy-preserving machine learning : 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Work.... 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, 21 citations in OpenAlex.
- Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review.Frontiers in aging neuroscience · 2023Pooled it
- Progress in multi-omics studies of osteoarthritis.Biomarker research · 2025Review
- Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning.Journal of translational medicine · 2024Article
- Recent methodological advances in federated learning for healthcare.Patterns (New York, N.Y.) · 2024Review
- Anatomical attention can help to segment the dilated pancreatic duct in abdominal CT.International journal of computer assisted radiology and surgery · 2024Article
- Radiomics signature of osteoarthritis: Current status and perspective.Journal of orthopaedic translation · 2024Review
- Unobtrusive biometric data de-identification of fundus images using latent space disentanglement.Biomedical optics express · 2023Article
- A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning.Frontiers in neuroscience · 2023Article
- The application of machine learning in early diagnosis of osteoarthritis: a narrative review.Therapeutic advances in musculoskeletal disease · 2023Review
- Federated learning enables big data for rare cancer boundary detection.Nature communications · 2022Article
- The Forefront of Dentistry-Promising Tech-Innovations and New Treatments.JDR clinical and translational research · 2022Article
- Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic.Journal of imaging · 2022Review
- Quantitative bone imaging biomarkers and joint space analysis of the articular Fossa in temporomandibular joint osteoarthritis using artificial intelligence models.Frontiers in dental medicine · 2022Article
- Revisiting instrument segmentation: Learning from decentralized surgical sequences with various imperfect annotations.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
Osteoarthritis is a chronic disease that affects the temporomandibular joint (TMJ), causing chronic pain and disability. To diagnose patients suffering from this disease before advanced degradation of the bone, we developed a diagnostic tool called TMJOAI. This machine learning based algorithm is capable of classifying the health status TMJ in of patients using 52 clinical, biological and jaw condyle radiomic markers. The TMJOAI includes three parts. the feature preparation, selection and model evaluation. Feature generation includes the choice of radiomic features (condylar trabecular bone or mandibular fossa), the histogram matching of the images prior to the extraction of the radiomic markers, the generation of feature pairwise interaction, etc.; the feature selection are based on the p-values or AUCs of single features using the training data; the model evaluation compares multiple machine learning algorithms (e.g. regression-based, tree-based and boosting algorithms) from 10 times 5-fold cross validation. The best performance was achieved with averaging the predictions of XGBoost and LightGBM models; and the inclusion of 32 additional markers from the mandibular fossa of the joint improved the AUC prediction performance from 0.83 to 0.88. After cross-validation and testing, the tools presented here have been deployed on an open-source, web-based system, making it accessible to clinicians. TMJOAI allows users to add data and automatically train and update the machine learning models, and therefore improve their performance.
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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.