Objective: This study evaluated the use of a deep-learning approach for automated detection and numbering of deciduous teeth in children as depicted on panoramic radiographs. Methods and materials: An artificial intelligence (AI) algorithm (CranioCatch, Eskisehir-Turkey) using Faster R-CNN Inception v2 (COCO) models were developed to automatically detect and number deciduous teeth as seen on pediatric panoramic radiographs. The algorithm was trained and tested on a total of 421 panoramic images. System performance was assessed using a confusion matrix. Results: The AI system was successful in detecting and numbering the deciduous teeth of children as depicted on panoramic radiographs. The sensitivity and precision rates were high. The estimated sensitivity, precision, and F1 score were 0.9804, 0.9571, and 0.9686, respectively. Conclusion: Deep-learning-based AI models are a promising tool for the automated charting of panoramic dental radiographs from children. In addition to serving as a time-saving measure and an aid to clinicians, AI plays a valuable role in forensic identification.

The purpose of the paper was the assessment of the success of an artificial intelligence (AI) algorithm formed on a deep-convolutional neural network (D-CNN) model for the segmentation of apical lesions on dental panoramic radiographs. A total of 470 anonymized panoramic radiographs were used to progress the D-CNN AI model based on the U-Net algorithm (CranioCatch, Eskisehir, Turkey) for the segmentation of apical lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Eskisehir Osmangazi University. A U-Net implemented with PyTorch model (version 1.4.0) was used for the segmentation of apical lesions. In the test data set, the AI model segmented 63 periapical lesions on 47 panoramic radiographs. The sensitivity, precision, and F1-score for segmentation of periapical lesions at 70% IoU values were 0.92, 0.84, and 0.88, respectively. AI systems have the potential to overcome clinical problems. AI may facilitate the assessment of periapical pathology based on panoramic radiographs.

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Aim of the study: Ultrasonographic examination of intraosseous jaw pathologies may reveal interesting incidental, mobile hyperechoic particles (“snowflakes”) in anechoic areas. Purpose of this study is to explain and discuss this snowing-like ultrasonographic feature of intraosseous jaw pathologies. Material and methods: This study included 113 patients admitted to our clinic for examination: 43 (38.05%) males and 70 (61.9%) females with a mean age of 34.9 ± 17.2 years (range: 6–72 years). A total of 120 intraosseous lesions were evaluated prior to surgery using ultrasonography; these included non-neoplastic, odontogenic, and non-odontogenic lesions. Results: In total, 5 (4.1%) of the 120 lesions exhibited snowing-like feature on ultrasonography, including 2 (1.6% of total) of 3 incisive canal cysts, 2 (1.6% of total) of 7 dentigerous cysts, and 1 (0.8% of total) of 19 odontogenic keratocysts. Conclusions: Snowflakes evident on ultrasonography of intraosseous jaw lesions may be specific to certain pathologies. Future studies correlating radiologic and pathologic features of intraosseous jaw lesions should focus on ultrasonographic snowing-like appearance in different types of lesions and explore why they occur.