Five-year change of panoramic radiomorphometric indices and fractal dimension values in type 2 diabetes patients.

OBJECTIVES: Diabetes mellitus is a chronic disease characterized by dysregulation of glucose metabolism, with characteristic long-term complications accompanied by changes in bone quality. The purpose of this study is to compare the results with a control group by performing radiomorphometric analyses on panoramic radiographs obtained 5 years apart to examine changes in the mandibular bone cortex and microstructures of type 2 diabetes mellitus (T2DM) patients. METHODS: Two panoramic radiographs that were taken 5 years (mean 5.26 ± 0.134) apart from 52 patients with T2DM (n:26) and a control group (n:26) were used. A total of 104 images were evaluated. Analyses were done from the condyle (FD1), angulus (FD2), distal second premolar apex (FD3), and anterior to the mental foramen (FD4) for fractal dimension (FD) in the mandible. Symphysis index (SI), anterior index (AI), molar index (MI), posterior index (PI), and panoramic mandibular index (PMI) measurements were taken for cortical analysis. Three-way ANOVA, three-way robust ANOVA, two-way ANOVA, and two-way robust ANOVA tests were used for statistical analysis (p < 0.05). RESULTS: After a 5-year period, there was a significant decrease in all FD measures of the mandible in both T2DM and control groups (p < 0.05). This resulted in a statistical difference in the main effect of time. After a 5-year period, no significant difference in mandibular cortical measures was identified between the T2DM and control groups (p > 0.05). CONCLUSION: According to panoramic radiography, the mandibular trabecular structure deteriorated after 5 years, whereas cortical values remained the same. It concluded that T2DM had no effect on these results.

Classification of temporomandibular joint osteoarthritis on cone beam computed tomography images using artificial intelligence system.

BACKGROUND: The use of artificial intelligence has many advantages, especially in the field of oral and maxillofacial radiology. Early diagnosis of temporomandibular joint osteoarthritis by artificial intelligence may improve prognosis. OBJECTIVE: The aim of this study is to perform the classification of temporomandibular joint (TMJ) osteoarthritis and TMJ segmentation on cone beam computed tomography (CBCT) sagittal images with artificial intelligence. METHODS: In this study, the success of YOLOv5 architecture, an artificial intelligence model, in TMJ segmentation and osteoarthritis classification was evaluated on 2000 sagittal sections (500 healthy, 500 erosion, 500 osteophyte, 500 flattening images) obtained from CBCT DICOM images of 290 patients. RESULTS: The sensitivity, precision and F1 scores of the model for TMJ osteoarthritis classification are 1, 0.7678 and 0.8686, respectively. The accuracy value for classification is 0.7678. The prediction values of the classification model are 88% for healthy joints, 70% for flattened joints, 95% for joints with erosion and 86% for joints with osteophytes. The sensitivity, precision and F1 score of the YOLOv5 model for TMJ segmentation are 1, 0.9953 and 0.9976, respectively. The AUC value of the model for TMJ segmentation is 0.9723. In addition, the accuracy value of the model for TMJ segmentation was found to be 0.9953. CONCLUSION: Artificial intelligence model applied in this study can be a support method that will save time and convenience for physicians in the diagnosis of the disease with successful results in TMJ segmentation and osteoarthritis classification.

Nasopharynx evaluation in children of unilateral cleft palate patients and normal with cone beam computed tomography.

OBJECTIVE: This study aimed to examine the morphological characteristics of the nasopharynx in unilateral Cleft lip/palate (CL/P) children and non-cleft children using cone beam computed tomography (CBCT). METHODS: A retrospective study consisted of 54 patients, of which 27 patients were unilateral CL/P, remaining 27 patients have no CL/P. Eustachian tubes orifice (ET), Rosenmuller fossa (RF) depth, presence of pharyngeal bursa (PB), the distance of posterior nasal spine (PNS)-pharynx posterior wall were quantitatively evaluated. RESULTS: The main effect of the CL/P groups was found to be effective on RF depth-right (p < 0.001) and RF depth-left (p < 0.001). The interaction effect of gender and CL/P groups was not influential on measurements. The cleft-side main effect was found to be effective on RF depth-left (p < 0.001) and RF depth-right (p = 0002). There was no statistically significant relationship between CL/P groups and the presence of bursa pharyngea. CONCLUSIONS: Because it is the most common site of nasopharyngeal carcinoma (NPC), the anatomy of the nasopharynx should be well known in the early diagnosis of NPC.

Detecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithm.

OBJECTIVES: Artificial intelligence (AI) techniques like convolutional neural network (CNN) are a promising breakthrough that can help clinicians analyze medical imaging, diagnose taurodontism, and make therapeutic decisions. The purpose of the study is to develop and evaluate the function of CNN-based AI model to diagnose teeth with taurodontism in panoramic radiography. METHODS: 434 anonymized, mixed-sized panoramic radiography images over the age of 13 years were used to develop automatic taurodont tooth segmentation models using a Pytorch implemented U-Net model. Datasets were split into train, validation, and test groups of both normal and masked images. The data augmentation method was applied to images of trainings and validation groups with vertical flip images, horizontal flip images, and both flip images. The Confusion Matrix was used to determine the model performance. RESULTS: Among the 43 test group images with 126 labels, there were 109 true positives, 29 false positives, and 17 false negatives. The sensitivity, precision, and F1-score values of taurodont tooth segmentation were 0.8650, 0.7898, and 0.8257, respectively. CONCLUSIONS: CNN's ability to identify taurodontism produced almost identical results to the labeled training data, and the CNN system achieved close to the expert level results in its ability to detect the taurodontism of teeth.

Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images.

The present study aims to validate the diagnostic performance and evaluate the reliability of an artificial intelligence system based on the convolutional neural network method for the morphological classification of sella turcica in CBCT (cone-beam computed tomography) images. In this retrospective study, sella segmentation and classification models (CranioCatch, Eskisehir, Türkiye) were applied to sagittal slices of CBCT images, using PyTorch supported by U-Net and TensorFlow 1, and we implemented the GoogleNet Inception V3 algorithm. The AI models achieved successful results for sella turcica segmentation of CBCT images based on the deep learning models. The sensitivity, precision, and F-measure values were 1.0, 1.0, and 1.0, respectively, for segmentation of sella turcica in sagittal slices of CBCT images. The sensitivity, precision, accuracy, and F1-score were 1.0, 0.95, 0.98, and 0.84, respectively, for sella-turcica-flattened classification; 0.95, 0.83, 0.92, and 0.88, respectively, for sella-turcica-oval classification; 0.75, 0.94, 0.90, and 0.83, respectively, for sella-turcica-round classification. It is predicted that detecting anatomical landmarks with orthodontic importance, such as the sella point, with artificial intelligence algorithms will save time for orthodontists and facilitate diagnosis.

Zygomaticocoronoid ankylosis with possible myositis ossificans: a very rare case.

Ankylosis forming between the zygomatic arch and the coronoid process is a rarely encountered pathological extracapsular ankylosis. Its treatment protocol consists of surgical removal of the coronoid process with the ankylotic mass and jaw opening-closing exercises after surgery. Myositis ossificans (MO) is a self-limiting, benign ossifying lesion. It affects all types of soft tissues including subcutaneous adipose tissue, muscles, tendons and nerves. It is most frequently found in the muscle as a solitary lesion. The clinical appearance of MO is generally in the form of a mass characterized with an ossified soft tissue. When it develops alone, cross-sectional imaging might not be specific, and it may appear similar to worse etiologies. It is suggested multiple imaging modalities should be used in the assessment of a suspicious soft tissue mass. MO is a benign self-limiting disease. In this case report, in the radiographic examination of a 41-year-old female patient, ankylosis between the left coronoid process and the zygomatic bone accompanied by possible MO in the left medial pterygoid muscle was observed. Resection of the coronoid process with the ipsilateral route, resection of the ankylotic mass with the hemicoronal approach and resection of the contralateral coronoid process with the intraoral approach were performed, but the ossified formation in the medial pterygoid muscle was not touched.