Hiperparatiroidili hastalarda panoramik indeksler ve fraktal analiz kullanılarak mandibular kemiğin incelenmesi

Amaç: Bu çalışmada hiperparatiroidili hastaların mandibular kortikal ve trabeküler kemik yapısını incelemek amacıyla radyomorfometrik indeks ölçümleri ve fraktal analiz değerleri karşılaştırılmıştır.Gereç ve Yöntemler. Hiperparatiroidili 28 hastanın ve kontrol grubu olarak sağlıklı 28 bireyin panoramik radyografileri üzerinde mandibular trabeküler kemiğe ait fraktal boyut (FB) değerleri, panoramik mandibular indeks (PMI) ve mandibular kortikal genişlik (MKG) ölçüldü.Bulgular: Fraktal analiz sonuçlarına göre hasta grubu ile kontrol grubu arasında istatistiksel olarak anlamlı herhangi bir sonuç bulunmadı. Radyomorfometrik ölçümlerden PMI değerleri için hasta grubu ile kontrol grubu arasında istatistiksel olarak anlamlı fark bulunmadı. Hasta grubu ile kontrol grubu arasında MKG değerleri için anlamlı fark bulunmadı. Fraktal analiz, radyomorfometrik ölçümler ve biyokimya tetkikleri arasında istatistiksel olarak anlamlı bir korelasyon saptanmadı.Sonuç: Radyomorfometrik indeksler ve fraktal analiz yöntemi kullanılarak yapılan incelemelerde hiperparatiroidili hastaların mandibular kortikal ve trabeküler kemik yapısında normal hasta popülasyonuna göre bir farklılık saptanmadı.ANAHTAR KELİMELER Mandibula, panoramik radyografi, panoramik indeksler, fraktal anali

Gömülü ve sürmüş yirmi yaş dişlerinde bifid mandibular kanal prevalansının dental volumetrik tomografi ile karşılaştırılması

Amaç: Çeşitli görüntüleme teknikleri ile yapılan çalışmalarda bifid mandibular kanal gibi mandibular kanalın değişik varyasyonları bildirilmiştir. Panoramik radyografi ile yapılan çalışmalarda bildirilen bifid mandibular kanal varyasyonu % 1’den fazla değildir. Bu çalışmanın amacı konik ışınlı bilgisayarlı tomografi (KIBT) kullanılarak bifid mandibular kanal insidansını belirlemektir.Gereç ve Yöntemler: 204 yarım çenede KIBT görüntüleri kullanılarak retrospektif olarak bifid mandibular kanal varlığı değerlendirildi. Bulgular: Değerlendirilen 204 yarım çenenin 87 (% 39.2)’sinde bifid mandibular kanal görüldü.Sonuç: Panoramik radyografi kullanılarak yapılmış olan çalışmalara nazaran KIBT ile yapılan bu çalışmada çok daha yüksek bifid mandibular kanal insidansı bulunmuştur.ANAHTAR KELİMELER Bifid mandibular kanal, konik ışınlı bilgisayarlı tomografi, insidan

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

Morphometric and morphological evaluation of mastoid emissary canal using cone-beam computed tomography

Objectives: This study aimed to determine mastoid emissary canal’s (MEC) and mastoid foramen (MF) prevalence and morphometric characteristics on cone-beam computed tomography (CBCT) images to underline its clinical significance and discuss its surgical consequences. Methods: In the retrospective analysis, two oral and maxillofacial radiologists analyzed the CBCT images of 135 patients (270 sides). The biggest MF and MEC were measured in the images evaluated in MultiPlanar Reconstruction (MPR) views. The MF and MEC mean diameters were calculated. The mastoid foramina number was recorded. The prevalence of MF was studied according to gender and side of the patient. Results: The overall prevalence of MEC and MF was 119 (88.1%). The prevalence of MEC and MF is 55.5% in females and 44.5% in males. MEC and MF were identified as bilateral in 80 patients (67.20%) and unilateral in 39 patients (32.80%). The mean diameter of MF was 2.4 ± 0.9 mm. The mean height of MF was 2.3 ± 0.9. The mean diameter of the MEC was 2.1 ± 0.8, and the mean height of the MEC was 2.1 ± 0.8. There is a statistical difference between the genders (p = 0.043) in foramen diameter. Males had a significantly larger mean diameter of MF in comparison to females. Conclusion: MEC and MF must be evaluated thoroughly if the surgery is contemplated. Radiologists and surgeons should be aware of mastoid emissary canal morphology, variations, clinical relevance, and surgical consequences while operating in the suboccipital and mastoid areas to avoid unexpected and catastrophic complications. CBCT may be a reliable imaging diagnostic technique

A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs

The study aims to evaluate the diagnostic performance of an artificial intelligence system based on deep learning for the segmentation of occlusal, proximal and cervical caries lesions on panoramic radiographs. The study included 504 anonymous panoramic radiographs obtained from the radiology archive of Inonu University Faculty of Dentistry’s Department of Oral and Maxillofacial Radiology from January 2018 to January 2020. This study proposes Dental Caries Detection Network (DCDNet) architecture for dental caries segmentation. The main difference between DCDNet and other segmentation architecture is that the last part of DCDNet contains a Multi-Predicted Output (MPO) structure. In MPO, the final feature map split into three different paths for detecting occlusal, proximal and cervical caries. Extensive experimental analyses were executed to analyze the DCDNet network architecture performance. In these comparison results, while the proposed model achieved an average F1-score of 62.79%, the highest average F1-score of 15.69% was achieved with the state-of-the-art segmentation models. These results show that the proposed artificial intelligence-based model can be one of the indispensable auxiliary tools of dentists in the diagnosis and treatment planning of carious lesions by enabling their detection in different locations with high success

Morphologic evaluations of hypoglossal canal using cone beam computed tomography

Hypoglossal canal (HC) which begins from very slightly above the inner part of the anterolateral portion of the foramen magnum and is located above the occipital condyle of the occipital bone. The aim of this study is to examine HC morphology and variations using cone beam computed tomography (CBCT). The morphology and types of HC were investigated with 303 CBCT images (606 side). Type 1 variation in 606 HC examined becomes the most commonly observed type (57.3%) while type 5 variation was the least common type of variation (0.8%). Type 1 HC was statistically higher in males (p=0.004). Because of HC, which is an anthropologically important point and enters the field of images in CBCT scan, it is recommended that dental radiologists should be aware of their variations and be wary of the pathologies that may occur in this region

Second mesiobuccal canal segmentation with YOLOv5 architecture using cone beam computed tomography images

The objective of this study is to use a deep-learning model based on CNN architecture to detect the second mesiobuccal (MB2) canals, which are seen as a variation in maxillary molars root canals. In the current study, 922 axial sections from 153 patients’ cone beam computed tomography (CBCT) images were used. The segmentation method was employed to identify the MB2 canals in maxillary molars that had not previously had endodontic treatment. Labeled images were divided into training (80%), validation (10%) and testing (10%) groups. The artificial intelligence (AI) model was trained using the You Only Look Once v5 (YOLOv5x) architecture with 500 epochs and a learning rate of 0.01. Confusion matrix and receiver-operating characteristic (ROC) analysis were used in the statistical evaluation of the results. The sensitivity of the MB2 canal segmentation model was 0.92, the precision was 0.83, and the F1 score value was 0.87. The area under the curve (AUC) in the ROC graph of the model was 0.84. The mAP value at 0.5 inter-over union (IoU) was found as 0.88. The deep-learning algorithm used showed a high success in the detection of the MB2 canal. The success of the endodontic treatment can be increased and clinicians’ time can be preserved using the newly created artificial intelligence-based models to identify variations in root canal anatomy before the treatment.This work has been supported by Eskisehir Osmangazi University Scientific Research Projects Coordination Unit under grant number 202045E06