The effect of temporomandibular joint dysfunction on the craniocervical mandibular system: A retrospective study.

BACKGROUND: Temporomandibular disorders are the most common condition affecting the orofacial region, resulting in pain and dysfunction. OBJECTIVE: This study aimed to elucidate the ambiguous association between cervical features and temporomandibular disorders by measuring the rotations between the skull-atlas, atlas-axis and mandible-atlas and examining the relationship between these rotations and temporomandibular disorders. METHODS: Cone-beam computed tomography (CBCT) images from 176 patients, 97 females and 79 males with an average age of 25.7 years were used in this study. The patients were divided into two groups: those with joint dysfunction (n = 88) and those without (n = 88). The study employed various methods to determine rotations in the skull-atlas, atlas-axis and mandible atlas based on anatomical landmarks and measurements. These methods include the use of specific planes, angles and distances to identify and measure rotation. Data analysis was performed using the TURCOSA statistical software (Turcosa Analytics Ltd Co, Turkey, www.turcosa.com.tr). RESULTS: The results showed that the degree of rotation between the skull and the atlas was higher in the TMD group than in the control group (p < .001). Similarly, Atlas-axis rotation was significantly higher in the TMD group (p < .001). However, no significant difference was found between mandible atlas rotations in the two groups (p = .546). The study also found a significant difference between the direction of rotation between the atlas and axis and the direction of mandible atlas rotation (p < .001) as well as between skull and atlas rotations and mandible-atlas rotations (p < .001). CONCLUSION: Overall, the study suggests that there is a relationship between the skeletal structures of the cranio-cervico-mandibular system and TMD. Skull-atlas and atlas-axis rotations may play an important role in the aetiology of TMD in individuals with TMD. Therefore, it is important to evaluate rotations in the skull-atlas-axis region for the treatment of TMD.

Assessment of the Naso-Maxilla and Related Dentoalveolar Region with Cone Beam Computed Tomography.

PURPOSE: To evaluate via CBCT the anatomical variations of the maxillary teeth and associated major anatomical cavities, the maxillary sinus and nasal fossa. MATERIAL AND METHODS: CBCT scans of 221 patients were used to examine maxillary sinus variations, the posterior superior alveolar artery (PSAA) course, nasal septum variations, middle and inferior concha-meatus variations, canalis sinuosus, infraorbital ethmoid cell, infraorbital canal, anterior nasal spina, and nasopalatine canal. RESULTS: The incidence of anatomical variations was 32.6% for maxillary sinus septa, 50.9% for PSAA, 23.1% for nasal septum deviation, 6.3% for nasal septum spur and pneumatization, 3.6% for paradoxical middle concha, 14.9% for middle concha hypertrophy, 39.6% for middle concha bullosa, 0.45% for bifid inferior concha, 0.9% for paradoxical inferior concha, 60.1% for inferior conch hypertrophy, 1.8% for inferior concha bullosa, and 40.3% for the infraorbital ethmoid cell. The study mainly observed group 2 anterior nasal spina with a rate of 35.7%, group 1 nasopalatine canal with a rate of 37.1%, and infraorbital duct type 2 with a rate of 70%. In 20.4% and 47% of cases, canalis sinuosus was located in the right and left sides of the maxilla, respectively. CONCLUSIONS: Maxillary sinus variations, PSAA prevalence and localization, nasal septum and concha variations, anterior nasal spina subgroups, nasopalatine canal subgroups, canalis sinuosus prevalence, and localization and infraorbital ethmoid cell prevalence were found to be consistent with the literature. Moreover, a rare case of the lower bifid concha was identified. The nasomaxillary complex and related dental structures, which are a multidisciplinary study area, should be carefully examined in the presence of pain of unknown origin and the planning of surgical procedures.

An artificial intelligence approach to automatic tooth detection and numbering in panoramic radiographs.

BACKGROUND: Panoramic radiography is an imaging method for displaying maxillary and mandibular teeth together with their supporting structures. Panoramic radiography is frequently used in dental imaging due to its relatively low radiation dose, short imaging time, and low burden to the patient. We verified the diagnostic performance of an artificial intelligence (AI) system based on a deep convolutional neural network method to detect and number teeth on panoramic radiographs. METHODS: The data set included 2482 anonymized panoramic radiographs from adults from the archive of Eskisehir Osmangazi University, Faculty of Dentistry, Department of Oral and Maxillofacial Radiology. A Faster R-CNN Inception v2 model was used to develop an AI algorithm (CranioCatch, Eskisehir, Turkey) to automatically detect and number teeth on panoramic radiographs. Human observation and AI methods were compared on a test data set consisting of 249 panoramic radiographs. True positive, false positive, and false negative rates were calculated for each quadrant of the jaws. The sensitivity, precision, and F-measure values were estimated using a confusion matrix. RESULTS: The total numbers of true positive, false positive, and false negative results were 6940, 250, and 320 for all quadrants, respectively. Consequently, the estimated sensitivity, precision, and F-measure were 0.9559, 0.9652, and 0.9606, respectively. CONCLUSIONS: The deep convolutional neural network system was successful in detecting and numbering teeth. Clinicians can use AI systems to detect and number teeth on panoramic radiographs, which may eventually replace evaluation by human observers and support decision making.