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The study evaluated the diagnostic performance of an artificial intelligence system to detect separated endodontic instruments on periapical radiograph radiographs. Three hundred seven periapical radiographs were collected and divided into 222 for training and 85 for testing to be fed to the Mask R-CNN model. Periapical radiographs were assigned to the training and test set and labelled on the DentiAssist labeling platform. Labelled polygonal objects had their bounding boxes automatically generated by the DentiAssist system. Fractured instruments were classified and segmented. As a result of the proposed method, the mean average precision (mAP) metric was 98.809%, the precision value was 95.238, while the recall reached 98.765 and the f1 score 96.969%. The threshold value of 80% was chosen for the bounding boxes working with the Intersection over Union (IoU) technique. The Mask R-CNN distinguished separated endodontic instruments on periapical radiographs., (© 2023 Australian Society of Endodontology Inc.)

Objectives: Radiological examination has an important place in dental practice, and it is frequently used in intraoral imaging. The correct numbering of teeth on radiographs is a routine practice that takes time for the dentist. This study aimed to propose an automatic detection system for the numbering of teeth in bitewing images using a faster Region-based Convolutional Neural Networks (R-CNN) method., Methods: The study included 1125 bite-wing radiographs of patients who attended the Faculty of Dentistry of Ordu University from 2018 to 2019. A faster R-CNN an advanced object identification method was used to identify the teeth. The confusion matrix was used as a metric and to evaluate the success of the model., Results: The deep CNN system (CranioCatch, Eskisehir, Turkey) was used to detect and number teeth in bitewing radiographs. Of 715 teeth in 109 bite-wing images, 697 were correctly numbered in the test data set. The F1 score, precision and sensitivity were 0.9515, 0.9293 and 0.9748, respectively., Conclusions: A CNN approach for the analysis of bitewing images shows promise for detecting and numbering teeth. This method can save dentists time by automatically preparing dental charts.

Background/Aim: Age estimation is of great importance due to legal requirements. Although there are many methods used, most of them are based on age related dental changes. Artificial intelligence based programs, one of the most current and popular topics in recent years, are becoming more and more important in dental studies. This study aims to measure the performance of deep learning in forensic age estimation from mandibular third molars using panoramic radiographs. Material and Methods: In our study, panoramic radiographs of male and female patients between the ages of 16-26 years who applied to our department for various reasons were used. The pixel-based Convolutional Neural Networks (CNN) method, one of the types of artificial neural networks, was applied. The high performance ResNeXt-101 model and Adamax algorithm were selected. The learning rate was set to 0.001. The dataset was labeled with the DentiAssist platform and randomly divided into 80% training and 20% testing. 1296 data under 18 and 1036 data over 18 were used. Dropout method was applied in case of over memorization. In the last step of the hidden layer, a linear two-class prediction was obtained using a structured fully connected layer. Results: The performance metrics for the ResNeXt neural network were 4.36% accuracy, 83.95% precision, 84.56% recall, 84.56% F1-score and 84.14% F1-score (80% confidence interval) when adequate training was provided. Conclusions: Artificial intelligence, which eliminates the subjective margin of error compared to conventional methods and rapidly processes a large amount of data, has achieved promising results in forensic age determination.

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