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Abstract Background Maxillofacial complex automated segmentation could alternative traditional segmentation methods to increase the effectiveness of virtual workloads. The use of DL systems in the detection of maxillary sinus and pathologies will both facilitate the work of physicians and be a support mechanism before the planned surgeries. Objective The aim was to use a modified You Only Look Oncev5x (YOLOv5x) architecture with transfer learning capabilities to segment both maxillary sinuses and maxillary sinus diseases on Cone-Beam Computed Tomographic (CBCT) images. Methods Data set consists of 307 anonymised CBCT images of patients (173 women and 134 males) obtained from the radiology archive of the Department of Oral and Maxillofacial Radiology. Bilateral maxillary sinuses CBCT scans were used to identify mucous retention cysts (MRC), mucosal thickenings (MT), total and partial opacifications, and healthy maxillary sinuses without any radiological features. Results Recall, precision and F1 score values for total maxillary sinus segmentation were 1, 0.985 and 0.992, respectively; 1, 0.931 and 0.964 for healthy maxillary sinus segmentation; 0.858, 0.923 and 0.889 for MT segmentation; 0.977, 0.877 and 0.924 for MRC segmentation; 1, 0.942 and 0.970 for sinusitis segmentation. Conclusion This study demonstrates that maxillary sinuses can be segmented, and maxillary sinus diseases can be accurately detected using the AI model.

Abstract 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.

Abstract Background The purpose of this study was to investigate the morphology of maxillary first premolar mesial root concavity and to analyse its relation to periodontal bone loss (BL) using cone beam computed tomography (CBCT) and panoramic radiographs. Methods The mesial root concavity of maxillary premolar teeth was analysed via CBCT. The sex and age of the patients, starting position and depth of the root concavity, apicocoronal length of the concavity on the crown or root starting from the cementoenamel junction (CEJ), total apicocoronal length of the concavity, amount of bone loss both in CBCT images and panoramic radiographs, location of the furcation, length of the buccal and palatinal roots, and buccopalatinal cervical root width were measured. Results A total of 610 patients’ CBCT images were examined, and 100 were included in the study. The total number of upper premolar teeth was 200. The patients were aged between 18 and 65 years, with a mean age of 45.21 ± 13.13 years. All the teeth in the study presented mesial root concavity (100%, n = 200). The starting point of concavity was mostly on the cervical third of the root (58.5%). The mean depth and buccolingual length measurements were 0.96 mm and 4.32 mm, respectively. Depth was significantly related to the amount of alveolar bone loss (F = 5.834, p = 0.001). The highest average concavity depth was 1.29 mm in the group with 50% bone loss. The data indicated a significant relationship between the location of the furcation and bone loss (X2 = 25.215, p = 0.003). Bone loss exceeded 50% in 100% of patients in whom the furcation was in the cervical third and in only 9.5% of patients in whom the furcation was in the apical third (p = 0.003). Conclusions According to the results of this study, the depth of the mesial root concavity and the coronal position of the furcation may increase the amount of alveolar bone loss. Clinicians should be aware of these anatomical factors to ensure accurate treatment planning and successful patient management.

Abstract Dental fillings, frequently used in dentistry to address various dental tissue issues, may pose problems when not aligned with the anatomical contours and physiology of dental and periodontal tissues. Our study aims to detect the prevalence and distribution of normal and overhanging filling restorations using a deep CNN architecture trained through supervised learning, on panoramic radiography images. A total of 10480 fillings and 2491 overhanging fillings were labeled using CranioCatch software from 2473 and 1850 images, respectively. After the data obtaining phase, validation (80%), training 10%), and test-groups (10%) were formed from images for both labelling. The YOLOv5x architecture was used to develop the AI model. The model’s performance was assessed through a confusion matrix and sensitivity, precision, and F1 score values of the model were calculated. For filling, sensitivity is 0.95, precision is 0.97, and F1 score is 0.96; for overhanging were determined to be 0.86, 0.89, and 0.87, respectively. The results demonstrate the capacity of the YOLOv5 algorithm to segment dental radiographs efficiently and accurately and demonstrate proficiency in detecting and distinguishing between normal and overhanging filling restorations.

Abstract Background This retrospective study aimed to develop a deep learning algorithm for the interpretation of panoramic radiographs and to examine the performance of this algorithm in the detection of periodontal bone losses and bone loss patterns. Methods A total of 1121 panoramic radiographs were used in this study. Bone losses in the maxilla and mandibula (total alveolar bone loss) (n = 2251), interdental bone losses (n = 25303), and furcation defects (n = 2815) were labeled using the segmentation method. In addition, interdental bone losses were divided into horizontal (n = 21839) and vertical (n = 3464) bone losses according to the defect patterns. A Convolutional Neural Network (CNN)-based artificial intelligence (AI) system was developed using U-Net architecture. The performance of the deep learning algorithm was statistically evaluated by the confusion matrix and ROC curve analysis. Results The system showed the highest diagnostic performance in the detection of total alveolar bone losses (AUC = 0.951) and the lowest in the detection of vertical bone losses (AUC = 0.733). The sensitivity, precision, F1 score, accuracy, and AUC values were found as 1, 0.995, 0.997, 0.994, 0.951 for total alveolar bone loss; found as 0.947, 0.939, 0.943, 0.892, 0.910 for horizontal bone losses; found as 0.558, 0.846, 0.673, 0.506, 0.733 for vertical bone losses and found as 0.892, 0.933, 0.912, 0.837, 0.868 for furcation defects (respectively). Conclusions AI systems offer promising results in determining periodontal bone loss patterns and furcation defects from dental radiographs. This suggests that CNN algorithms can also be used to provide more detailed information such as automatic determination of periodontal disease severity and treatment planning in various dental radiographs.

Abstract Background Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x algorithm in the detection of white spot lesions in post-orthodontic oral photographs using the limited data available and to make a preliminary study for fully automated models that can be clinically integrated in the future. Methods A total of 435 images in JPG format were uploaded into the CranioCatch labeling software and labeled white spot lesions. The labeled images were resized to 640 × 320 while maintaining their aspect ratio before model training. The labeled images were randomly divided into three groups (Training:349 images (1589 labels), Validation:43 images (181 labels), Test:43 images (215 labels)). YOLOv5x algorithm was used to perform deep learning. The segmentation performance of the tested model was visualized and analyzed using ROC analysis and a confusion matrix. True Positive (TP), False Positive (FP), and False Negative (FN) values were determined. Results Among the test group images, there were 133 TPs, 36 FPs, and 82 FNs. The model’s performance metrics include precision, recall, and F1 score values of detecting white spot lesions were 0.786, 0.618, and 0.692. The AUC value obtained from the ROC analysis was 0.712. The mAP value obtained from the Precision-Recall curve graph was 0.425. Conclusions The model’s accuracy and sensitivity in detecting white spot lesions remained lower than expected for practical application, but is a promising and acceptable detection rate compared to previous study. The current study provides a preliminary insight to further improved by increasing the dataset for training, and applying modifications to the deep learning algorithm. Clinical revelance Deep learning systems can help clinicians to distinguish white spot lesions that may be missed during visual inspection.

Abstract Background Panoramic radiographs, in which anatomic landmarks can be observed, are used to detect cases closely related to pediatric dentistry. The purpose of the study is to investigate the success and reliability of the detection of maxillary and mandibular anatomic structures observed on panoramic radiographs in children using artificial intelligence. Methods A total of 981 mixed images of pediatric patients for 9 different pediatric anatomic landmarks including maxillary sinus, orbita, mandibular canal, mental foramen, foramen mandible, incisura mandible, articular eminence, condylar and coronoid processes were labelled, the training was carried out using 2D convolutional neural networks (CNN) architectures, by giving 500 training epochs and Pytorch-implemented YOLO-v5 models were produced. The success rate of the AI model prediction was tested on a 10% test data set. Results A total of 14,804 labels including maxillary sinus (1922), orbita (1944), mandibular canal (1879), mental foramen (884), foramen mandible (1885), incisura mandible (1922), articular eminence (1645), condylar (1733) and coronoid (990) processes were made. The most successful F1 Scores were obtained from orbita (1), incisura mandible (0.99), maxillary sinus (0.98), and mandibular canal (0.97). The best sensitivity values were obtained from orbita, maxillary sinus, mandibular canal, incisura mandible, and condylar process. The worst sensitivity values were obtained from mental foramen (0.92) and articular eminence (0.92). Conclusions The regular and standardized labelling, the relatively larger areas, and the success of the YOLO-v5 algorithm contributed to obtaining these successful results. Automatic segmentation of these structures will save time for physicians in clinical diagnosis and will increase the visibility of pathologies related to structures and the awareness of physicians.

Objectives: This study aimed to develop an artificial intelligence (Al) model that can determine automatic tooth numbering, frenulum attachments, gingival overgrowth areas, and gingival inflammation signs on intraoral photographs and to evaluate the performance of this model. Method and materials: A total of 654 intraoral photographs were used in the study (n = 654). All photographs were reviewed by three periodontists, and all teeth, frenulum attachment, gingival overgrowth areas, and gingival inflammation signs on photographs were labeled using the segmentation method in a web-based labeling software. In addition, tooth numbering was carried out according to the FDI system. An Al model was developed with the help of YOLOv5x architecture with labels of 16,795 teeth, 2,493 frenulum attachments, 1,211 gingival overgrowth areas, and 2,956 gingival inflammation signs. The confusion matrix system and ROC (receiver operator characteristic) analysis were used to statistically evaluate the success of the developed model. Results: The sensitivity, precision, F1 score, and AUC (area under the curve) for tooth numbering were 0.990, 0.784, 0.875, and 0.989; for frenulum attachment these were 0.894, 0.775,0.830, and 0.827; for gingival overgrowth area these were 0.757, 0.675, 0.714, and 0.774; and for gingival inflammation sign 0.737, 0.823, 0.777, and 0.802, respectively. Conclusion: The results of the present study show that Al systems can be successfully used to interpret intraoral photographs. These systems have the potential to accelerate the digital transformation in the clinical and academic functioning of dentistry with the automatic determination of anatomical structures and dental conditions from intraoral photographs. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine the diagnostic performance of detecting pulp stones with a deep learning model on bite-wing radiographs. Material and Methods: 2203 radiographs were scanned retrospectively. 1745 pulp stones were marked on 1269 bite-wing radiographs with the CranioCatch labeling program (CranioCatch, Eskişehir, Turkey) in patients over 16 years old after the consensus of two experts of Maxillofacial Radiologists. This dataset was divided into 3 grou as training (n = 1017 (1396 labels), validation (n = 126 (174 labels)) and test (n = 126) (175 labels) sets, respectively. The deep learning model was developed using Mask R-CNN architecture. A confusion matrix was used to evaluate the success of the model. Results: The results of precision, sensitivity, and F1 obtained using the Mask R-CNN architecture in the test dataset were found to be 0.9115, 0.8879, and 0.8995, respectively. Discussion- Conclusion: Deep learning algorithms can detect pulp stones. With this, clinicians can use software systems based on artificial intelligence as a diagnostic support system. Mask R-CNN architecture can be used for pulp stone detection with approximately 90% sensitivity. The larger data sets increase the accuracy of deep learning systems. More studies are needed to increase the success rates of deep learning models.

A few cases of pneumonia were reported by Wuhan Municipal Health Commission in Wuhan, Hubei Province, Republic of China and this mysterious pneumonia was recognized as novel coronavirus disease (COVID-19) in the course of time on 31 December 2019. Based on the literature knowledge, COVID-19 outbreak was came into existence through an animal-to-human transmission, then continued human-to-human diffusion. Especially dentists among the medical professionals are at high-risk group of SARS-CoV-2 virus contamination because of several routine dental procedures having the risk to convey the SARS-CoV-2 virus via droplets and close contact. In this mini review, it was aimed to give information about patient management during COVID-19 pandemic for dental practitioners, periodontologists and dental hygienists.

Aim: The aim of this study is to create a model that enables the detection of dentigerous cysts on panoramic radiographs in order to enable dentistry students to meet and apply artificial intelligence applications. Methods: E.O. and I.T. who are 5th year students of the faculty of dentistry, detected 36 orthopantomographs whose histopathological examinations were determined as Dentigerous Cyst, and the affected teeth and cystic cavities were segmented using CranioCatch's artificial intelligence supported clinical decision support system software. Since the sizes of the images in the dataset are different from each other, all images were resized as 1024x514 and augmented as vertical flip, horizontal flip and both flips were applied on the train-validation. Within the obtained data set, 200 epochs were trained with PyTorch U-Net with a learning rate of 0.001, train: 112 images (112 labels), val: 16 images (16 labels). With the model created after the segmentations were completed, new dentigerous cyst orthopantomographs were tested and the success of the model was evaluated. Results: With the model created for the detection of dentigerous cysts, the F1 score (2TP / (2TP+FP+FN)) precision (TP/ (TP+N)) and sensitivity (TP/ (TP+FN)) were found to be 0.67, 0.5 and 1, respectively. Conclusion: With a CNN approach for the analysis of dentigerous cyst images, the precision has been found to be 0.5 even in a small database. These methods can be improved, and new graduate dentists can gain both experience and save time in the diagnosis of cystic lesions with radiographs.

Abstract Background The aim of this study was to evaluate the success of the artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images. Methods Seventy-five CBCT images were included in this study. In these images, bone height and thickness in 508 regions where implants were required were measured by a human observer with manual assessment method using InvivoDental 6.0 (Anatomage Inc. San Jose, CA, USA). Also, canals/sinuses/fossae associated with alveolar bones and missing tooth regions were detected. Following, all evaluations were repeated using the deep convolutional neural network (Diagnocat, Inc., San Francisco, USA) The jaws were separated as mandible/maxilla and each jaw was grouped as anterior/premolar/molar teeth region. The data obtained from manual assessment and AI methods were compared using Bland–Altman analysis and Wilcoxon signed rank test. Results In the bone height measurements, there were no statistically significant differences between AI and manual measurements in the premolar region of mandible and the premolar and molar regions of the maxilla (p > 0.05). In the bone thickness measurements, there were statistically significant differences between AI and manual measurements in all regions of maxilla and mandible (p

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.

ABSTRACTBackground:The purpose of this in-vitro study was to evaluate the centering ability and the apical transportation of curved root canals after preparation with WaveOne Gold single-file reciprocating system with guidance of different glide path systems by using Cone-Beam Computed Tomography. Methods:Seventy-two extracted mandibular first molar teeth with curved mesial roots were selected for this study. Specimens were randomly divided into six experimental groups according to the root canal preparation (n = 12): Group G-File; Group One G; Group ProGlider; Group PathFile; Group K-files; and Group control. After forming a glide path, root canal preparation procedure was completed with WaveOne Gold primary instrument (#25). Cone-beam computed tomographic images of specimens were taken before and after root canal preparation procedure. Apical transportation and centering ability were evaluated at 1, 4, and 7 mm from the apical foramen. The data were statistically analyzed using the Kruskal Wallis test at a significance level of P = 0.05. Results:There were no significant differences among all experimental groups. (p > 0.05) Conclusion:Creating a glide path in curved root canals with either NiTi glide path files or stainless still manuel K-files before root canal preparation with WaveOne Gold Primary caused similar apical transportation and centering ability results with no glide path used group.

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 öğrencisinin 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 sağlayabilir.

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 zekâ

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 Carcinoma

Nasolabial cysts are uncommon nonodontogenic lesions that occur in the nasal alar region. These lesions usually present with asymptomatic swelling but can cause pain if infected. In this case report, we describe the inadequacy of conventional radiography in a nasolabial cyst case, as well as the magnetic resonance imaging (MRI) and ultrasonography (US) findings in a 54-year-old female patient.

Odontoma is the most common odontogenic tumor that is radiographically and histologically characterized by the production of mature enamel, dentin, cementum and pulp tissue. It grows slowly and has nonaggressive behavior. This case report presents clinical, radiological and pathological findings of bilateral complex odontoma that is rarely in literature in a 30-year-old female patient.

Impaction of a mandibular premolar is relatively uncommon. Ectopic placement is more unusual and there has been no discussion in the literature of an ectopic mandibular premolar in the coronoid process. In this case report, we present an impacted ectopic mandibular permanent premolar in the sigmoid notch (incisura mandibulae) region. Etiology of the tooth and treatment options are discussed and illustrated by Cone Beam Computed Tomography (CBCT) images.

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. [ABSTRACT FROM AUTHOR]

Objective: The purpose of the study was to create an artificial intelligence (AI) system for detecting idiopathic osteosclerosis (IO) on panoramic radiographs for automatic, routine, and simple evaluations. Subject and Methods: In this study, a deep learning method was carried out with panoramic radiographs obtained from healthy patients. A total of 493 anonymized panoramic radiographs were used to develop the AI system (CranioCatch, Eskisehir, Turkey) for the detection of IOs. The panoramic radiographs were acquired from the radiology archives of the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University. GoogLeNet Inception v2 model implemented with TensorFlow library was used for the detection of IOs. Confusion matrix was used to predict model achievements. Results: Fifty IOs were detected accurately by the AI model from the 52 test images which had 57 IOs. The sensitivity, precision, and F-measure values were 0.88, 0.83, and 0.86, respectively. Conclusion: Deep learning-based AI algorithm has the potential to detect IOs accurately on panoramic radiographs. AI systems may reduce the workload of dentists in terms of diagnostic efforts. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to evaluate the success of the artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images. Methods: Seventy-five CBCT images were included in this study. In these images, bone height and thickness in 508 regions where implants were required were measured by a human observer with manual assessment method using InvivoDental 6.0 (Anatomage Inc. San Jose, CA, USA). Also, canals/sinuses/fossae associated with alveolar bones and missing tooth regions were detected. Following, all evaluations were repeated using the deep convolutional neural network (Diagnocat, Inc., San Francisco, USA) The jaws were separated as mandible/maxilla and each jaw was grouped as anterior/premolar/molar teeth region. The data obtained from manual assessment and AI methods were compared using Bland–Altman analysis and Wilcoxon signed rank test. Results: In the bone height measurements, there were no statistically significant differences between AI and manual measurements in the premolar region of mandible and the premolar and molar regions of the maxilla (p > 0.05). In the bone thickness measurements, there were statistically significant differences between AI and manual measurements in all regions of maxilla and mandible (p < 0.001). Also, the percentage of right detection was 72.2% for canals, 66.4% for sinuses/fossae and 95.3% for missing tooth regions. Conclusions: Development of AI systems and their using in future for implant planning will both facilitate the work of physicians and will be a support mechanism in implantology practice to physicians. [ABSTRACT FROM AUTHOR]