Diagnosing oral and maxillofacial diseases using deep learning.

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.

Orofacial dysfunction screening examinations in children with sleep-disordered breathing symptoms.

Orofacial myofunctional disorders (OMD) and sleep-disordered breathing (SDB) may present as comorbidities. Orofacial characteristics might serve as a clinical marker of SDB, allowing early identification and management of OMD and improving treatment outcomes for sleep disorders. The study aims to characterize OMD in children with SDB symptoms and to investigate possible relationships between the presence of various components of OMD and symptoms of SDB. A cross-sectional study of healthy children aged 6-8 from primary schools was conducted in central Vietnam in 2019. SDB symptoms were collected using the parental Pediatric Sleep Questionnaire, Snoring Severity Scale, Epworth Daytime Sleepiness Scale, and lip-taping nasal breathing assessment. Orofacial myofunctional evaluation included assessment of tongue mobility, as well as of lip and tongue strength using the Iowa Oral Performance Instrument, and of orofacial characteristics by the protocol of Orofacial Myofunctional Evaluation with Scores. Statistical analysis was used to investigate the relationship between OMD components and SDB symptoms. 487 healthy children were evaluated, of whom 46.2% were female. There were 7.6% of children at high risk of SDB. Children with habitual snoring (10.3%) had an increased incidence of restricted tongue mobility and decreased lip and tongue strength. Abnormal breathing patterns (22.4%) demonstrated lower posterior tongue mobility and lower muscle strength. Daytime sleepiness symptoms were associated with changes in muscle strength, facial appearance, and impaired orofacial function. Lower strengths of lip and tongue or improper nasal breathing were more likely to be present in children with reported sleep apnea (6.6%). Neurobehavioral symptoms of inattention and hyperactivity were linked to anomalous appearance/posture, increases in tongue mobility and oral strength. This study demonstrates a prevalence of orofacial myofunctional anomalies in children exhibiting SDB symptoms. Children with prominent SDB symptoms should be considered as candidates for further orofacial myofunctional assessment.

Prediction Model for Future Success of Early Orthopedic Treatment of Class III Malocclusion.

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.

Effectiveness of Human-Artificial Intelligence Collaboration in Cephalometric Landmark Detection.

Detection of cephalometric landmarks has contributed to the analysis of malocclusion during orthodontic diagnosis. Many recent studies involving deep learning have focused on head-to-head comparisons of accuracy in landmark identification between artificial intelligence (AI) and humans. However, a human-AI collaboration for the identification of cephalometric landmarks has not been evaluated. We selected 1193 cephalograms and used them to train the deep anatomical context feature learning (DACFL) model. The number of target landmarks was 41. To evaluate the effect of human-AI collaboration on landmark detection, 10 images were extracted randomly from 100 test images. The experiment included 20 dental students as beginners in landmark localization. The outcomes were determined by measuring the mean radial error (MRE), successful detection rate (SDR), and successful classification rate (SCR). On the dataset, the DACFL model exhibited an average MRE of 1.87 ± 2.04 mm and an average SDR of 73.17% within a 2 mm threshold. Compared with the beginner group, beginner-AI collaboration improved the SDR by 5.33% within a 2 mm threshold and also improved the SCR by 8.38%. Thus, the beginner-AI collaboration was effective in the detection of cephalometric landmarks. Further studies should be performed to demonstrate the benefits of an orthodontist-AI collaboration.

Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network.

Recently, the estimation of bone maturation using deep learning has been actively conducted. However, many studies have considered hand-wrist radiographs, while a few studies have focused on estimating cervical vertebral maturation (CVM) using lateral cephalograms. This study proposes the use of deep learning models for estimating CVM from lateral cephalograms. As the second, third, and fourth cervical vertebral regions (denoted as C2, C3, and C4, respectively) are considerably smaller than the whole image, we propose a stepwise segmentation-based model that focuses on the C2-C4 regions. We propose three convolutional neural network-based classification models: a one-step model with only CVM classification, a two-step model with region of interest (ROI) detection and CVM classification, and a three-step model with ROI detection, cervical segmentation, and CVM classification. Our dataset contains 600 lateral cephalogram images, comprising six classes with 100 images each. The three-step segmentation-based model produced the best accuracy (62.5%) compared to the models that were not segmentation-based.

Dentist Job Satisfaction: A Systematic Review and Meta-analysis.

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.

Deep Anatomical Context Feature Learning for Cephalometric Landmark Detection.

In the past decade, anatomical context features have been widely used for cephalometric landmark detection and significant progress is still being made. However, most existing methods rely on handcrafted graphical models rather than incorporating anatomical context during training, leading to suboptimal performance. In this study, we present a novel framework that allows a Convolutional Neural Network (CNN) to learn richer anatomical context features during training. Our key idea consists of the Local Feature Perturbator (LFP) and the Anatomical Context loss (AC loss). When training the CNN, the LFP perturbs a cephalometric image based on prior anatomical distribution, forcing the CNN to gaze relevant features more globally. Then AC loss helps the CNN to learn the anatomical context based on spatial relationships between the landmarks. The experimental results demonstrate that the proposed framework makes the CNN learn richer anatomical representation, leading to increased performance. In the performance comparisons, the proposed scheme outperforms state-of-the-art methods on the ISBI 2015 Cephalometric X-ray Image Analysis Challenge.

Treatment of pre-eruptive intracoronal resorption: A systematic review and case report.

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.