Evaluation of Rosenmuller Fossa with cone beam computed tomography: A retrospective radio-anatomical study

Background: Rosenmuller fossa (RF) is known as a lateral pharyngeal recess, is bilaterally located beneath the skull base and behind the torus tubarius. Nasopharyngeal carcinoma is most commonly located in the RF. The purpose of this study is to evaluation of RF with cone beam computed tomography Methods: A total of 150 subjects (80 females, 70 males, 6-88 years) were included in the study. Subjects were divided into age groups (6- 20 years, 21-30 years, 31-40 years, 41-50 years, 51-60 years, over 60 years) and gender. Result: There is no statistically significant difference between class (RF type) and gender (p = 0.086). There is a statistically significant association between the categories of age group and class variables (p = 0.015). RF type 1 was more common in the 6-20 age and 21-30 age groups, whereas RF type 3 was more common in the 41-50 age and 51-60 age groups. Conclusion: When the literature was investigated, it was not found a study evaluating RF with cone beam computed tomography. When considering clinical significance, RF should be searched and examined in larger populations. KEYWORDS  Cone beam computed tomography, Rosenmuller  Fossa, Nasopharyngeal Carcinom

Derin öğrenme yöntemi ile panoramik radyografiden diş eksikliklerinin tespiti: Bir yapay zekâ pilot çalışması

Amaç: Bu çalışmanın amacı, panoramik radyografide diş eksikliklerinin değerlendirilmesi için tasarlanmış tanı amaçlı bilgisayar yazılımının işlevini geliştirmek ve değerlendirmektir.Gereç ve Yöntemler: Veri seti eksik diş tespiti için 99 tam diş ve 54 eksik diş olmak üzere 153 görüntüden oluşmaktadır. Tüm görüntüler Ağız, Diş ve Çene Radyolojisi uzmanları tarafından tekrar kontrol edilmiş ve doğrulanmıştır. Veri setindeki tüm görüntüler eğitim öncesinde 971 X 474 piksel olarak yeniden boyutlandırılmıştır. Açık kaynak kodlu python programlama dili ve OpenCV, NumPy, Pandas, ile Matplotlib kütüphaneleri etkin olarak kullanılarak bir rastgele dizilim oluşturulmuştur. Önceden eğitilmiş bir Google Net Inception v3 CNN ağı ön işleme için kullanılmış ve veri setleri transfer öğrenimi kullanılarak eğitilmiştir.Bulgular: Eğitim de kullanılan görüntülerin modeli tahminlendirmesi ile çıkan başarı oranı % 94.7’dir. Eğitimde kullanılmayan test için ayrılan görüntülerin tahminlemesindeki başarı oranı % 75’dir. Sonuç: Derin öğrenme tekniklerinde veri seti arttıkça başarı oranları da artmaktadır. Daha fazla görüntüyle oluşacak veri setininin eğitim modellerinde başarı oranları yükselecektir. Gelecek çalışmalar daha büyük veri setleriyle yapılmalıdır.ANAHTAR KELİMELER Panoramik radyografi, derin öğrenme, yapay zek

Evaluation of tooth development stages with deep learning-based artificial intelligence algorithm

Background: This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients. Methods: The study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model. Results: Among the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively. Conclusions: In conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options

SUCCESS OF ARTIFICIAL INTELLIGENCE SYSTEM IN DETERMINING ALVEOLAR BONE LOSS FROM DENTAL PANORAMIC RADIOGRAPHY IMAGES

Objectives: The aim of this study was to detect alveolar bone loss from dental panoramic radiographic images using artificial intelligence systems. Material and Methods: A total of 2276 panoramic radiographic images were used in this study. While 1137 of them belong to cases with bone destruction, 1139 were periodontally healthy. The dataset is divided into three parts as training (n=1856) , validation (n=210) and testing set (n= 210). All images in the data set were resized to 1472x718 pixels before training. A random sequence was created using the open-source python programming language and OpenCV, NumPy, Pandas, and Matplotlib libraries effectively. A pre-trained Google Net Inception v3 CNN network was used for preprocessing and data sets were trained using transfer learning. Diagnostic performance was evaluated with the confusion matrix using sensivitiy, specificity, precision, accuracy and F1 score. Results: Of the 105 cases with bone loss, 99 were detected by the AI system. Sensitivity was 0.94, specificity 0.88, precision 0.89, accuracy 0.91 and F1 score 0.91. Conclusion: The convolutional neural network model is successful in determining periodontal bone losses. It can be used as a system to facilitate the work of physicians in diagnosis and treatment planning in the future

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

YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition

Objectives: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs. Methods: A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed. Results: The sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation. Conclusions: YOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition

Correction: YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition

...

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

KONİK IŞINLI BİLGİSAYARLI TOMOGRAFİDE MAKSİLLOFASİYAL BÖLGEDE GÖRÜLEN ANATOMİK YAPILARIN BİLİNİRLİĞİNİN DEĞERLENDİRİLMESİ: BİR RADYO-ANATOMİK PİLOT ÇALIŞMA

Amaç: Bu çalışmanın amacı, maksillofasiyal bölgede görülen anatomik yapıların Konik Işınlı Bilgisayarlı Tomografi (KIBT) görüntülerinin 4. ve 5. sınıf öğrencileri arasından seçilen iki grup arasındaki bilinirliklerinin değerlendirmesini yapmak ve KIBT’taki anatomik noktalar hakkında bilgi vermektir. Gereç ve Yöntem: Eskişehir Osmangazi Üniversitesi Diş Hekimliği Fakültesindeki Ağız, Diş ve Çene Radyoloji stajını almış 4. ve 5. Sınıf öğrencilerinden toplam 56 öğrencinin katılımıyla gerçekleştirilen bu çalışma, 2017 yılının Nisan ayında yapılmıştır. Bu çalışmada, diş hekimliği radyolojisinde önemli yere sahip olan 36 farklı anatomik yapı rakamlarla işaretlenerek öğrencilere sorulmuş ve diş hekimliği öğrencilerinin doğru cevap verme sayılarına göre veriler kaydedilmiştir. Bulgular: Çalışmamızda doğru cevaplama oranlarını karşılaştırdığımızda 4. sınıf öğrencilerinden oluşan grubun sadece 5 tane (Crista Galli, Farinks, Kondiler Proçes, İnferior Mandibular Kanal, Maksiller İnsiziv Kanal) anatomik yapıda daha iyi olduğu, diğer 31 tane anatomik yapıda ise; 5. sınıf öğrencilerinden oluşan grubun daha yüksek doğru cevaplama oranına sahip olduğu görülmüştür. Sonuç: Anatomik yapıların KIBT’ta nasıl göründüğünü bilmek hata yapma oranımızı en aza indirmektedir. Diş hekimliği öğrencilerine Ağız, Diş ve Çene Radyoloji stajında KIBT eğitimi verilmesi diş hekimlerinin KIBT’ı daha etkin bir şekilde kullanmalarını sağlayabilirBackground: the aim of this study was to evaluate the awareness of two seperate groups of 4th and 5th grade students about the anatomical structures seen in the maxillofacial region in the cone-beam computed tomography (CBCT) images and to give information about anatomical points in cone-beam computed tomography. Materials and Methods: This study was carried out in April of 2017 with 56 students from Eskisehir Osmangazi University Faculty of Dentistry. in this study, 36 different anatomical regions were marked with numbers, and some anatomical points have important positions in dental radiology were asked and recorded according to the correct answer numbers of the students. the anatomical points could be clearly distinguished on the radiograph. Results: in our study, when comparing the correct answer rates with the 4th and 5th class groups, the 5th class group had a higher correct answer rate in 31 anatomical structures, whereas the 4th class group had better in only 5 anatomical structures (Crista Galli, Pharynx, Processus Condylaris, Canalis Mandibularis Inferior, Incisive Canal). Conclusion: Knowing how anatomical structures look in reality and in CBCT make decrease our mistakes. Providing CBCT training at the Oral and Maxillofacial Radiology internship to dentists can help use CBCT more effectively