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Abstract The classification and localization of odontogenic lesions from panoramic radiographs is a challenging task due to the positional biases and class imbalances of the lesions. To address these challenges, a novel neural network, DOLNet, is proposed that uses mutually influencing hierarchical attention across different image scales to jointly learn the global representation of the entire jaw and the local discrepancy between normal tissue and lesions. The proposed approach uses local attention to learn representations within a patch. From the patch-level representations, we generate inter-patch, i.e., global, attention maps to represent the positional prior of lesions in the whole image. Global attention enables the reciprocal calibration of path-level representations by considering non-local information from other patches, thereby improving the generation of whole-image-level representation. To address class imbalances, we propose an effective data augmentation technique that involves merging lesion crops with normal images, thereby synthesizing new abnormal cases for effective model training. Our approach outperforms recent studies, enhancing the classification performance by up to 42.4% and 44.2% in recall and F1 scores, respectively, and ensuring robust lesion localization with respect to lesion size variations and positional biases. Our approach further outperforms human expert clinicians in classification by 10.7 % and 10.8 % in recall and F1 score, respectively.

Objectives: Because of the heterogeneous nature of the evidence regarding dentists’ job satisfaction, an overview was necessary to examine dentists’ level of job satisfaction and to determine related work environmental factors. Materials and methods: A literature search was conducted using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Electronic database searches of PubMed/MEDLINE, EMBASE, and Web of Science were performed until March 1, 2020. Two independent authors collected data and assessed the methodological quality of primary studies using the Newcastle Ottawa Scale. Results: Nine studies were included from the 1987 initially retrieved. Among the included studies, 5 exhibited a neutral level of satisfaction and originated from China, South Korea, Egypt, and the United States, and 3 studies from Canada, Lithuania, and the United States showed a high level of satisfaction. Only 1 study did not report the mean job satisfaction score. According to bias evaluation, 9 studies were considered low risk. Conclusion: The findings showed that dentists were satisfied with their jobs at a moderate to high level, and specialists were more satisfied than general dentists. Regarding work environmental factors, the 6 most satisfied factors were patient relationships, respect, delivery of care, staff, professional relationship, and professional environment. Five of the least satisfied factors were personal time, stress, income, practice management, and professional time. However, longitudinal studies would be required to identify changes in these factors. Further studies should be performed in middle- and low-income countries using the Dentist Satisfaction Survey, including stress evaluation.

Background/purpose: Pre-eruptive intracoronal resorption (PEIR) is usually detected accidently in radiographs. However, treatment modality is still not reported systematically. The current study aimed to conduct a systematic review of the treatment modality of PEIR case reports and to report a case on the preservation of a vital pulp with surgical exposure in permanent maxillary canine. Materials and methods: We systematically searched case reports from PubMed/MEDLINE, EMBASE, and Web of science databases. The treatment modality, suspected etiology, and follow up periods were collected from each study and reviewed by two authors independently. Results: The initial search identified 100 studies. After the title/abstract screening, 37 articles received a full-text reading; and finally, 24 articles (29 patients and 37 affected teeth) were selected for the final review. Among the 24 unerupted teeth, surgical opening and restoration treatment of PEIR was chosen as a high priority for treatment options (n = 9, 36%). Among the 13 erupted teeth, restoration was applied for the prevention such as developing in size and fracture (n = 4, 31%). Conclusion: According to this systematic review, treatment modalities were based on the progressive nature of the lesion size and eruption state to establish the optimal approach for each patient. Clinicians should take into account the eruption status, lesion progression, the size of the lesion, and the degree of pulp involvement.

This study aimed to identify predictors for successful post-treatment outcomes in early orthopedic class III malocclusion treatment with a facemask and hyrax expander appliance. The study was performed on lateral cephalograms from 37 patients at the start of treatment (T0), post-treatment (T1), and a minimum of three years after treatment (T2). The patients were grouped as stable or unstable according to the existence of a 2-mm overjet at T2. For statistical analysis, independent t-tests were used to compare the baseline characteristics and measurements of the two groups, considering a significance level of < 0.05. Thirty variables of pretreatment cephalograms were considered during logistic regression analysis to identify predictors. A discriminant equation was established using a stepwise method. The success rate and area under the curve were calculated, with AB to the mandibular plane, ANB, ODI, APDI, and A–B plane angles as predictors. The A–B plane angle was the most significantly different between the stable and unstable groups. In terms of the A–B plane angle, the success rate of early class III treatment with a facemask and hyrax expander appliance was 70.3%, and the area under the curve indicated a fair grade.

This review aimed to explore whether studies employing a convolutional neural network (CNN) for odontogenic cyst and tumor detection follow the methodological reporting recommendations, the checklist for artificial intelligence in medical imaging (CLAIM). We retrieved the CNN studies using panoramic and cone-beam-computed tomographic images from inception to April 2021 in PubMed, EMBASE, Scopus, and Web of Science. The included studies were assessed according to the CLAIM. Among the 55 studies yielded, 6 CNN studies for odontogenic cyst and tumor detection were included. Following the CLAIM items, abstract, methods, results, discussion across the included studies were insufficiently described. The problem areas included item 2 in the abstract; items 6–9, 11–18, 20, 21, 23, 24, 26–31 in the methods; items 33, 34, 36, 37 in the results; item 38 in the discussion; and items 40–41 in “other information.” The CNN reports for odontogenic cyst and tumor detection were evaluated as low quality. Inadequate reporting reduces the robustness, comparability, and generalizability of a CNN study for dental radiograph diagnostics. The CLAIM is accepted as a good guideline in the study design to improve the reporting quality on artificial intelligence studies in the dental field.