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    Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study
    (Bmc, 2024) Ozsunkar, Pelin Senem; Ozen, Duygu CelIk; Abdelkarim, Ahmed Z.; Duman, Sacide; Ugurlu, Mehmet; Demir, Mehmet Ridvan; Kuleli, Batuhan
    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 x 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.
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    Evaluation of local anaesthesia techniques preferred by paediatric dentists in pulpal procedures for mandibular primary molars
    (Bmc, 2025) Ozsunkar, Pelin Senem; Duman, Sacide
    Aim: This study aimed to evaluate the local anaesthesia methods preferred by paediatric dentists for pulpal treatments of mandibular primary molars, along with alternative techniques used when the initial approach failed. Method: A 23-item web-based survey was conducted. The first part collected demographic data, while the second included 16 multiple-choice questions based on clinical scenarios involving pulpal treatment of mandibular primary molars. Participants indicated their primary and alternative anaesthesia choices. Data were analyzed using SPSS v25.0 (p < 0.05). Results: A total of 330 paediatric dentists participated (mean age: 30.64 +/- 5.01 years; 90% female). Anaesthesia preferences varied significantly by age, tooth position, and treatment type (p < 0.05). For pulpectomy in first primary molars, buccal infiltration was preferred in 4-year-olds (53.4%), whereas inferior alveolar nerve block (IANB) was more common in 7-year-olds (60.1%, p = 0.002). In second molars, IANB was commonly preferred in both age groups, with significantly higher use in older children (p = 0.001). In pulpotomy procedures, buccal infiltration anaesthesia (BI) was mainly used in 4-year-olds (62.8% for first molars; 37.6% for second molars), while IANB was preferred in 7-year-olds (56.3% and 70.7%, p = 0.004 and p < 0.001). When the initial anaesthesia failed, intrapulpal (IPA) and intraligamentary anaesthesia (ILA) were the most common alternatives. Conclusion: Paediatric dentists' anaesthesia preferences are influenced by patient age, tooth position, and treatment type. The study reflects the importance of adapting anaesthesia techniques to clinical conditions and patient needs.