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Objectives: This study intended to evaluate patients' attitudes toward the use of AI in dental radiographic detection of occlusal caries and the impact of AI-based diagnosis on their trust in dentists., Methods: A total of 272 completed questionnaires were included in this study. In the first part of the study, approval was obtained from the patients, and data were collected about their socio-demographic characteristics. In the second part the 11-item Dentist Trust Scale was applied. In the third and fourth parts, there were questions about two clinical scenarios, the patients' knowledge of attitudes toward AI, and how the AI-based diagnosis had affected their trust. Evaluation was performed using a Likert-type scale. Data were analyzed with the Chi-square, one-way ANOVA, and ordinal logistic regression tests (p < 0.05)., Results: The patients believed that "AI is useful" (3.86 ± 1.03) and were not afraid of the use of AI in dentistry (2.40 ± 1.05). Educational level was considerably related to the patients' attitudes to the use of AI for dental diagnostics (p < 0.05). The patients stated that "dentists are extremely thorough and careful" (4.39 ± 0.77)., Conclusions: The patients displayed a positive attitude to AI-based diagnosis in the dental field and appear to exhibit trust in dentists. The use of Al in routine clinical practice can provide important benefit to physicians as a clinical decision support system in dentistry and understanding patients' attitudes may allow dentists to shape AI-supported dentistry in the future., Competing Interests: Declarations. Conflict of interest: The authors declare no competing interests. Ethics approval and consent to participate: Ethical approval (2023.01.03–1333) for this cross-sectional study was granted by the Ethics Committee of Ankara Yıldırım Beyazit University. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008. Informed consent was obtained from all patients for being included in the study. This article does not contain any studies with human or animal subjects performed by the any of the authors., (© 2024. The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.)

Objective: This study aims to investigate the effect of number of data on model performance, for the detection of tooth numbering problem on dental panoramic radiographs, with the help of image processing and deep learning algorithms., Study Design: The data set consists of 3000 anonymous dental panoramic X-rays of adult individuals. Panoramic X-rays were labeled on the basis of 32 classes in line with the FDI tooth numbering system. In order to examine the relationship between the number of data used in image processing algorithms and model performance, four different datasets which include 1000, 1500, 2000 and 2500 panoramic X-rays, were used. The training of the models was carried out with the YOLOv4 algorithm and trained models were tested on a fixed test dataset with 500 data and compared based on F1 score, mAP, sensitivity, precision and recall metrics., Results: The performance of the model increased as the number of data used during the training of the model increased. Therefore, the last model trained with 2500 data showed the highest success among all the trained models., Conclusion: Dataset size is important for dental enumeration, and large samples should be considered as more reliable., (© 2023. The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.)