Evaluation of TMJ Disc Displacement with MRI Based Radiomics Analysis

Duyan Yüksel H., Orhan K., Evlice B., Kaya Ö.

CED-IADR/NOF Oral Health Research Congress, Geneve, İsviçre, 12 - 14 Eylül 2024, ss.1

  • Yayın Türü: Bildiri / Özet Bildiri
  • Basıldığı Şehir: Geneve
  • Basıldığı Ülke: İsviçre
  • Sayfa Sayıları: ss.1
  • Çukurova Üniversitesi Adresli: Evet

Özet

Objectives The purpose of this study was to propose a machine-learning model and

assess its ability to classify temporomandibular joint (TMJ) disk displacements on

magnetic resonance (MR) T1-W and PD-W images.

Methods This retrospective cohort study included 180 TMJs from 90 patients with TMJ

signs and symptoms. A Radiomics platform was used to extract (Huiying Medical

Technology Co., Ltd, China) imaging features of TMJ pathologies, condylar bone

changes and disc displacements. Thereafter, different machine learning (ML) algorithms

and logistic regression were implemented on radiomic features for feature selection,

classification, and prediction. The following radiomic features included first-order

statistic, shape, texture, gray level co-occurrence matrix (GLCM), gray level run length

matrix (GLRLM) and gray level size zone matrix (GLSZM). Six classifiers, including logistic

regression (LR), random forest (RF), decision tree (DT), k-nearest neighbors (KNN),

XGBoost and support vector machine (SVM) were used for a model building which could

predict the TMJ pathologies. The performance of models was evaluated by sensitivity,

specificity and ROC curve. The TMJ diskdisplacements were classified as; (0) Normal,

(1) ADDwR, (2) ADDwoR.

Results A total of 90 patients and 180 TMJs (19 men and 71 women; mean age,

33.6±16.8; range between 13-79 years) were included in this study. KNN classifier was

found to be the most optimal machine learning model for prediction of TMJ pathologies.

The AUC, sensitivity, and specificity for the training set were 0.944, 0.771, 0.918 for

normal, ADDwR and ADDwoR while testing set were 0.913, 0.716, 1 for normal, ADDwR

and ADDwoR. For TMJ Disk Diplacment Large Area High Gray Level Emphasis,

firstorder_Skewness, firstorder_minumum, RootMeanSquared,

GrayLevelNonUniformity, firstorder_ Kurtosis, Long Run High Gray Level Emphasis, were

selected.

Conclusions This study has proposed a machine learning model by KNN analysis on

TMJ MR images, which can be used to TMJ disc displacements.