Comprehensive Analysis of Bacterial Flora in Postoperative Maxillary Cyst Fluid by 16S rRNA Gene and Culture Methods

Intracystic fluid was aseptically collected from 11 patients with postoperative maxillary cyst (POMC), and DNA was extracted from the POMC fluid. Bacterial species were identified by sequencing after cloning of approximately 580 bp of the 16S rRNA gene. Identification of pathogenic bacteria was also performed by culture methods. The phylogenetic identity was determined by sequencing 517–596 bp in each of the 1139 16S rRNA gene clones. A total of 1114 clones were classified while the remaining 25 clones were unclassified. A total of 103 bacterial species belonging to 42 genera were identified in POMC fluid samples by 16S rRNA gene analysis. Species of Prevotella (91%), Neisseria (73%), Fusobacterium (73%), Porphyromonas (73%), and Propionibacterium (73%) were found to be highly prevalent in all patients. Streptococcus mitis (64%), Fusobacterium nucleatum (55%), Propionibacterium acnes (55%), Staphylococcus capitis (55%), and Streptococcus salivarius (55%) were detected in more than 6 of the 11 patients. The results obtained by the culture method were different from those obtained by 16S rRNA gene analysis, but both approaches may be necessary for the identification of pathogens, especially of bacteria that are difficult to detect by culture methods, and the development of rational treatments for patients with POMC

Deep Neural Networks for Dental Implant System Classification

In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images

Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images

It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020. Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated. The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN. We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification. Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications. Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification. Using CNNs conferred high validity in the classification of dental implant brands and treatment stages. Furthermore, multi-task learning facilitated analysis accuracy

Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography

In this study, the accuracy of the positional relationship of the contact between the inferior alveolar canal and mandibular third molar was evaluated using deep learning. In contact analysis, we investigated the diagnostic performance of the presence or absence of contact between the mandibular third molar and inferior alveolar canal. We also evaluated the diagnostic performance of bone continuity diagnosed based on computed tomography as a continuity analysis. A dataset of 1279 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014-2021) was used for the validation. The deep learning models were ResNet50 and ResNet50v2, with stochastic gradient descent and sharpness-aware minimization (SAM) as optimizers. The performance metrics were accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). The results indicated that ResNet50v2 using SAM performed excellently in the contact and continuity analyses. The accuracy and AUC were 0.860 and 0.890 for the contact analyses and 0.766 and 0.843 for the continuity analyses. In the contact analysis, SAM and the deep learning model performed effectively. However, in the continuity analysis, none of the deep learning models demonstrated significant classification performance

Is attention branch network effective in classifying dental implants from panoramic radiograph images by deep learning?

Attention mechanism, which is a means of determining which part of the forced data is emphasized, has attracted attention in various fields of deep learning in recent years. The purpose of this study was to evaluate the performance of the attention branch network (ABN) for implant classification using convolutional neural networks (CNNs). The data consisted of 10191 dental implant images from 13 implant brands that cropped the site, including dental implants as pretreatment, from digital panoramic radiographs of patients who underwent surgery at Kagawa Prefectural Central Hospital between 2005 and 2021. ResNet 18, 50, and 152 were evaluated as CNN models that were compared with and without the ABN. We used accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristics curve as performance metrics. We also performed statistical and effect size evaluations of the 30-time performance metrics of the simple CNNs and the ABN model. ResNet18 with ABN significantly improved the dental implant classification performance for all the performance metrics. Effect sizes were equivalent to "Huge" for all performance metrics. In contrast, the classification performance of ResNet50 and 152 deteriorated by adding the attention mechanism. ResNet18 showed considerably high compatibility with the ABN model in dental implant classification (AUC = 0.9993) despite the small number of parameters. The limitation of this study is that only ResNet was verified as a CNN; further studies are required for other CNN models

Effective deep learning for oral exfoliative cytology classification

The use of sharpness aware minimization (SAM) as an optimizer that achieves high performance for convolutional neural networks (CNNs) is attracting attention in various fields of deep learning. We used deep learning to perform classification diagnosis in oral exfoliative cytology and to analyze performance, using SAM as an optimization algorithm to improve classification accuracy. The whole image of the oral exfoliation cytology slide was cut into tiles and labeled by an oral pathologist. CNN was VGG16, and stochastic gradient descent (SGD) and SAM were used as optimizers. Each was analyzed with and without a learning rate scheduler in 300 epochs. The performance metrics used were accuracy, precision, recall, specificity, F1 score, AUC, and statistical and effect size. All optimizers performed better with the rate scheduler. In particular, the SAM effect size had high accuracy (11.2) and AUC (11.0). SAM had the best performance of all models with a learning rate scheduler. (AUC = 0.9328) SAM tended to suppress overfitting compared to SGD. In oral exfoliation cytology classification, CNNs using SAM rate scheduler showed the highest classification performance. These results suggest that SAM can play an important role in primary screening of the oral cytological diagnostic environment

Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars

Pell and Gregory, and Winter's classifications are frequently implemented to classify the mandibular third molars and are crucial for safe tooth extraction. This study aimed to evaluate the classification accuracy of convolutional neural network (CNN) deep learning models using cropped panoramic radiographs based on these classifications. We compared the diagnostic accuracy of single-task and multi-task learning after labeling 1330 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014-2021). The mandibular third molar classifications were analyzed using a VGG 16 model of a CNN. We statistically evaluated performance metrics [accuracy, precision, recall, F1 score, and area under the curve (AUC)] for each prediction. We found that single-task learning was superior to multi-task learning (all p < 0.05) for all metrics, with large effect sizes and low p-values. Recall and F1 scores for position classification showed medium effect sizes in single and multi-task learning. To our knowledge, this is the first deep learning study to examine single-task and multi-task learning for the classification of mandibular third molars. Our results demonstrated the efficacy of implementing Pell and Gregory, and Winter's classifications for specific respective tasks

Circulatory C-type natriuretic peptide reduces mucopolysaccharidosis-associated craniofacial hypoplasia in vivo

Skeletal alterations in the head and neck region, such as midfacial hypoplasia, foramen magnum stenosis and spinal canal stenosis, are commonly observed in patients with mucopolysaccharidosis (MPS). However, enzyme replacement therapy (ERT), one of the major treatment approaches for MPS, shows limited efficacy for skeletal conditions. In this study, we analysed the craniofacial morphology of mice with MPS type VII, and investigated the underlying mechanisms promoting jaw deformities in these animals. Furthermore, we investigated the effects of C-type natriuretic peptide (CNP), a potent endochondral ossification promoter, on growth impairment of the craniofacial region in MPS VII mice when administered alone or in combination with ERT. MPS VII mice exhibited midfacial hypoplasia caused by impaired endochondral ossification, and histological analysis revealed increased number of swelling cells in the resting zone of the spheno-occipital synchondrosis (SOS), an important growth centre for craniomaxillofacial skeletogenesis. We crossed MPS VII mice with transgenic mice in which CNP was expressed in the liver under the control of the human serum amyloid-P component promoter, resulting in elevated levels of circulatory CNP. The maxillofacial morphological abnormalities associated with MPS VII were ameliorated by CNP expression, and further prevented by a combination of CNP and ERT. Histological analysis showed that ERT decreased the swelling cell number, and CNP treatment increased the width of the proliferative and hypertrophic zones of the SOS. Furthermore, the foramen magnum and spinal stenoses observed in MPS VII mice were significantly alleviated by CNP and ERT combination. These results demonstrate the therapeutic potential of CNP, which can be used to enhance ERT outcome for MPS VII-associated head and neck abnormalities