ArticleDento maxillo facial radiology2022
Artificial intelligence and infrared thermography as auxiliary tools in the diagnosis of temporomandibular disorder.
Article in Dento maxillo facial radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- Comparing Traditional Versus AI-Assisted TMJ Disorder Management Approaches: A Systematic Review and Meta-Analysis.Clinical and experimental dental research · 2026Pooled it
- Machine learning-based medical imaging diagnosis in patients with temporomandibular disorders: a diagnostic test accuracy systematic review and meta-analysis.Clinical oral investigations · 2024Pooled it
- Temporomandibular Disorders Diagnosis: Current Challenges and the Promising Role of Artificial Intelligence.European journal of dentistry · 2026Article
- Artificial Intelligence in Orofacial Pain: Diagnostic and Predictive Performance Across Machine Learning and Deep Learning Models.Diagnostics (Basel, Switzerland) · 2026Review
- Science of heat mapping: Thermography in musculoskeletal disorders.World journal of orthopedics · 2026Review
- Thermographic-Infrared Changes During Surgical Osteotomy Influenced by the Motor Type, System, and Diameter.International journal of dentistry · 2026Article
- Health of peri-implant soft tissues adjacent to glazed or polished monolithic zirconia: a randomized clinical trial.The journal of advanced prosthodontics · 2025Article
- Article
- Clinical Applications, Legal Considerations and Implementation Challenges of Smartphone-Based Thermography: A Scoping Review.Journal of clinical medicine · 2024Article
- The influence of different cheek and lip retractors and emissivity on intraoral infrared thermography.Dento maxillo facial radiology · 2024Article
- Novel Diagnostic and Therapeutic Approaches to Temporomandibular Dysfunction: A Narrative Review.Life (Basel, Switzerland) · 2023Review
- A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- Temporomandibular Joint Osteoarthritis Diagnosis Employing Artificial Intelligence: Systematic Review and Meta-Analysis.Journal of clinical medicine · 2023Review
- Convolutional neural network-based automatic cervical vertebral maturation classification method.Dento maxillo facial radiology · 2022Article
- Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo assess three machine learning (ML) attribute extraction methods: radiomic, semantic and radiomic-semantic association on temporomandibular disorder (TMD) detection using infrared thermography (IT); and to determine which ML classifier, KNN, SVM and MLP, is the most efficient for this purpose. METHODS AND MATERIALS: 78 patients were selected by applying the Fonseca questionnaire and RDC/TMD to categorize control patients (37) and TMD patients (41). IT lateral projections of each patient were acquired. The masseter and temporal muscles were selected as regions of interest (ROI) for attribute extraction. Three methods of extracting attributes were assessed: radiomic, semantic and radiomic-semantic association. For radiomic attribute extraction, 20 texture attributes were assessed using co-occurrence matrix in a standardized angulation of 0°. The semantic features were the ROI mean temperature and pain intensity data. For radiomic-semantic association, a single dataset composed of 28 features was assessed. The classification algorithms assessed were KNN, SVM and MLP. Hopkins's statistic, Shapiro-Wilk, ANOVA and Tukey tests were used to assess data. The significance level was set at 5% (
resultsTraining and testing accuracy values differed statistically for the radiomic-semantic association (
conclusionSemantic and radiomic-semantic-associated ML feature extraction methods and MLP classifier should be chosen for TMD detection using IT images and pain scale data. IT associated with ML presents promising results for TMD detection.
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