Accuracy of biomarkers obtained from cone beam computed tomography in assessing the internal trabecular structure of the mandibular condyle

Ebrahim, Fouad H.; Ruellas, Antônio Carlos de Oliveira; Paniagua, Beatriz; Benavides, Erika; Jepsen, Karl J.; Wolford, Larry M.; Gonçalves, João Roberto; Cevidanes, Lucia

doi:10.1016/j.oooo.2017.08.013

Cited by 18 publications

(6 citation statements)

References 32 publications

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“…The difficulties of TMJOA diagnosis include the subjectivity of radiographic interpretation and the pain threshold of an individual. The diagnosis itself may be the biggest barrier in creating biomarker disease profiles for diseased and healthy groups (Ebrahim et al 2017). This study sample of biological and standardized DC/TMD clinical survey data consisted of only 17 TMJOA and 17 asymptomatic controls, while the NN TMJOA morphology disease staging used a larger training sample for which only imaging data and clinical diagnosis are available.…”

Section: Discussionmentioning

confidence: 99%

Minimally Invasive Approach for Diagnosing TMJ Osteoarthritis

et al. 2019

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This study’s objectives were to test correlations among groups of biomarkers that are associated with condylar morphology and to apply artificial intelligence to test shape analysis features in a neural network (NN) to stage condylar morphology in temporomandibular joint osteoarthritis (TMJOA). Seventeen TMJOA patients (39.9 ± 11.7 y) experiencing signs and symptoms of the disease for less than 10 y and 17 age- and sex-matched control subjects (39.4 ± 15.2 y) completed a questionnaire, had a temporomandibular joint clinical exam, had blood and saliva samples drawn, and had high-resolution cone beam computed tomography scans taken. Serum and salivary levels of 17 inflammatory biomarkers were quantified using protein microarrays. A NN was trained with 259 other condyles to detect and classify the stage of TMJOA and then compared to repeated clinical experts’ classifications. Levels of the salivary biomarkers MMP-3, VE-cadherin, 6Ckine, and PAI-1 were correlated to each other in TMJOA patients and were significantly correlated with condylar morphological variability on the posterior surface of the condyle. In serum, VE-cadherin and VEGF were correlated with one another and with significant morphological variability on the anterior surface of the condyle, while MMP-3 and CXCL16 presented statistically significant associations with variability on the anterior surface, lateral pole, and superior-posterior surface of the condyle. The range of mouth opening variables were the clinical markers with the most significant associations with morphological variability at the medial and lateral condylar poles. The repeated clinician consensus classification had 97.8% agreement on degree of degeneration within 1 group difference. Predictive analytics of the NN’s staging of TMJOA compared to the repeated clinicians’ consensus revealed 73.5% and 91.2% accuracy. This study demonstrated significant correlations among variations in protein expression levels, clinical symptoms, and condylar surface morphology. The results suggest that 3-dimensional variability in TMJOA condylar morphology can be comprehensively phenotyped by the NN.

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Section: Discussionmentioning

confidence: 99%

Minimally Invasive Approach for Diagnosing TMJ Osteoarthritis

et al. 2019

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“…This approach allows experts to obtain more diagnostic information from medical images, which are considered an important decision-making tool for personalized care [13,14]. Some studies have used CBCT images to implement a radiomics approach with the mandibular condylar head [15][16][17]. These efforts have demonstrated the feasibility of radiomics features as biomarkers for diagnosing bony changes of the mandibular condyle.…”

Section: Introductionmentioning

confidence: 99%

Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study

Jeon

Kim²,

Choi³

et al. 2023

PLoS ONE

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Legal age estimation of living individuals is a critically important issue, and radiomics is an emerging research field that extracts quantitative data from medical images. However, no reports have proposed age-related radiomics features of the condylar head or an age classification model using those features. This study aimed to introduce a radiomics approach for various classifications of legal age (18, 19, 20, and 21 years old) based on cone-beam computed tomography (CBCT) images of the mandibular condylar head, and to evaluate the usefulness of the radiomics features selected by machine learning models as imaging biomarkers. CBCT images from 85 subjects were divided into eight age groups for four legal age classifications: ≤17 and ≥18 years old groups (18-year age classification), ≤18 and ≥19 years old groups (19-year age classification), ≤19 and ≥20 years old groups (20-year age classification) and ≤20 and ≥21 years old groups (21-year age classification). The condylar heads were manually segmented by an expert. In total, 127 radiomics features were extracted from the segmented area of each condylar head. The random forest (RF) method was utilized to select features and develop the age classification model for four legal ages. After sorting features in descending order of importance, the top 10 extracted features were used. The 21-year age classification model showed the best performance, with an accuracy of 91.18%, sensitivity of 80%, and specificity of 95.83%. Radiomics features of the condylar head using CBCT showed the possibility of age estimation, and the selected features were useful as imaging biomarkers.

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“…Women, in relation to men, present complaints ranging from 2:1 to 8:1 [9][10][11][12][13]. The multifaceted specific picture of the manifestation of the disease leads to incorrect routing of patients, errors in diagnosis and treatment, and chronicity of the TMJ [3,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Temporomandibular Disorders of Iatrogenic Etiology

Slesarev¹

2023

Temporomandibular Joint - Surgical Reconstruction and Managements

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Temporomandibular disorder (TMD) is a heterogeneous chronic systemic disease based on genetic, immunological, anatomical, morphological, and functional disorders of the articulatory norm. The task of the diagnostic stage is to identify direct (inherent in only one nosological form) and indirect (occurring in two or more nosological forms) etiological risks that transform into pathogenetic factors and TMDs. The transformation of the pathogenetic horizon of TMDs does characterize by the implementation of a scenario leading to the formation of three nosological forms of the disease: articular and nonarticular lesions of TMJ, and TMDs of iatrogenic etiology. TMDs of iatrogenic etiology constitute the most severe group of patients. Failure to identify biological, technological, and communication iatrogenic risks at the diagnostic stage is the main reason for triggering TMDs of iatrogenic etiology. The transformation of iatrogenic risks into iatrogenic pathogenetic factors leads to the formation of iatrogenic disease. A specialist working with this group of patients must have the necessary competence to make clinical decisions in the diagnosis, treatment, and rehabilitation of patients of this profile, including maxillofacial surgery and psychological counseling. Timely diagnosis of iatrogenic risks is the only preventive measure that prevents the development of iatrogenic TMDs. The therapy of TMDs of iatrogenic etiology does base on an interdisciplinary approach’s principles.

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Accuracy of biomarkers obtained from cone beam computed tomography in assessing the internal trabecular structure of the mandibular condyle

Cited by 18 publications

References 32 publications

Minimally Invasive Approach for Diagnosing TMJ Osteoarthritis

Minimally Invasive Approach for Diagnosing TMJ Osteoarthritis

Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study

Temporomandibular Disorders of Iatrogenic Etiology

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