Removal of the Dental Implant Displaced Into the Maxillary Sinus Through the Inferior Nasal Meatus via Transnasal Endoscopy.

Worldwide, the reconstruction of the posterior edentulous maxilla with dental implants has become a common practice in clinical settings. However, the poor bone condition in this area is sometimes accompanied by complications. Dental implant displacement into the maxillary sinus is viewed as a rare complication. A case of a 72-year-old man in whom an endoscopic technique was used to remove a dental implant that had been mistakenly planted into the maxillary sinus was reported in this study. The authors approached the sinus through the inferior nasal meatus, and the dental implant was removed through the widened ostium. The endoscopic surgical approach described in this study is reliable and minimally invasive for removing residual roots displaced into the maxillary sinus. Therefore, it has been concluded that this clinical procedure is worth using.

Removal of the Residual Roots Displaced Into the Maxillary Sinus via Endoscopy.

Minimally invasive endoscopic surgery has been developed for various indications in the craniomaxillofacial area. The case report presented in this article is focused on the possibility of removing the residual roots displaced into the maxillary sinus by means of an endoscopic technique. When planning endoscopic surgery to access the residual roots displaced in the maxillary sinus, we performed 2 different approaches into the maxillary sinus, a transnasal approach through the middle or inferior turbinate and transoral approach via the anterior maxillary sinus wall. The endoscopic surgical approach described is reliable and minimally invasive for removing the residual roots displaced into the maxillary sinus. Therefore, we concluded that the application of this clinical procedure is worth promoting.

MARS: A protein family involved in the formation of vertical skeletal elements.

Vertical organizations of skeletal elements are found in various vertebrate teeth and invertebrate exoskeletons. The molecular mechanism behind the development of such structural organizations is poorly known, although it is generally held that organic matrix proteins play an essential role. While most crustacean cuticular organizations exhibit horizontal chitinous layering, a typical vertical organization is found towards the surface of the teeth in the mandibles of the crayfish Cherax quadricarinatus. Candidate genes encoding for mandible-forming structural proteins were mined in C. quadricarinatus molt-related transcriptomic libraries by using a binary patterning approach. A new protein family, termed the Mandible Alanine Rich Structural (MARS) protein family, with a modular sequence design predicted to form fibers, was found. Investigations of spatial and temporal expression of the different MARS genes suggested specific expression in the mandibular teeth-forming epithelium, particularly during the formation of the chitinous vertical organization. MARS loss-of-function RNAi experiments resulted in the collapse of the organization of the chitin fibers oriented vertically to the surface of the crayfish mandibular incisor tooth. A general search of transcriptomic libraries suggested conservation of MARS proteins across a wide array of crustaceans. Our results provide a first look into the molecular mechanism used to build the complex crustacean mandible and into the specialized vertical structural solution that has evolved in skeletal elements.

Artificial Intelligence for Classifying and Archiving Orthodontic Images.

One of the main requirements for orthodontic treatment is continuous image acquisition. However, the conventional system of orthodontic image acquisition, which includes manual classification, archiving, and monitoring, is time-consuming and prone to errors caused by fatigue. This study is aimed at developing an effective artificial intelligence tool for the automated classification and monitoring of orthodontic images. We comprehensively evaluated the ability of a deep learning model based on Deep hidden IDentity (DeepID) features to classify and archive photographs and radiographs. This evaluation was performed using a dataset of >14,000 images encompassing all 14 categories of orthodontic images. Our model automatically classified orthodontic images in an external dataset with an accuracy of 0.994 and macro area under the curve of 1.00 in 0.08 min. This was 236 times faster than a human expert (18.93 min). Furthermore, human experts with deep learning assistance required an average of 8.10 min to classify images in the external dataset, much shorter than 18.93 min. We conclude that deep learning can improve the accuracy, speed, and efficiency of classification, archiving, and monitoring of orthodontic images.

Artificial intelligence-aided detection of ectopic eruption of maxillary first molars based on panoramic radiographs.

OBJECTIVES: Ectopic eruption (EE) of maxillary permanent first molars (PFMs) is among the most frequent ectopic eruption, which leads to premature loss of adjacent primary second molars, impaction of premolars and a decrease in dental arch length. Apart from oral manifestations such asdelayed eruption of PFMs and discoloration of primary second molars, panoramic radiographs can reveal EE of maxillary PFMs as well. Identifying eruption anomalies in radiographs can be strongly experience-dependent, leading us to develop here an automatic model that can aid dentists in this task and allow timelier interventions. METHODS: Panoramic X-ray images from 1480 patients aged 4-9 years old were used to train an auto-screening model. Another 100 panoramic images were used to validate and test the model. RESULTS: The positive and negative predictive values of this auto-screening system were 0.86 and 0.88, respectively, with a specificity of 0.90 and a sensitivity of 0.86. Using the model to aid dentists in detecting EE on the 100 panoramic images led to higher sensitivity and specificity than when three experienced pediatric dentists detected EE manually. CONCLUSIONS: Deep learning-based automatic screening system is useful and promising in the detection EE of maxillary PFMs with relatively high specificity. However, deep learning is not completely accurate in the detection of EE. To minimize the effect of possible false negative diagnosis, regular follow-ups and re-evaluation are required if necessary. CLINICAL SIGNIFICANCE: Identification of EE through a semi-automatic screening model can improve the efficacy and accuracy of clinical diagnosis compared to human experts alone. This method may allow earlier detection and timelier intervention and management.

Artificial intelligence for caries and periapical periodontitis detection.

OBJECTIVES: Periapical periodontitis and caries are common chronic oral diseases affecting most teenagers and adults worldwide. The purpose of this study was to develop an evaluation tool to automatically detect dental caries and periapical periodontitis on periapical radiographs using deep learning. METHODS: A modified deep learning model was developed using a large dataset (4129 images) with high-quality annotations to support the automatic detection of both dental caries and periapical periodontitis. The performance of the model was compared to the classification performance of dentists. RESULTS: The deep learning model automatically distinguished dental caries with an F1-score of 0.829 and periapical periodontitis with an F1-score of 0.828. The comparison of model-only and expert-only detection performance showed that the accuracy of the fully automatic method was significantly higher than that of the young dentists. With deep learning assistance, the experts not only reached a higher diagnostic accuracy with an average F1-score of 0.7844 for dental caries and 0.8208 for periapical periodontitis compared to expert-only scenarios, but also increased inter-observer agreement from 0.585/0.590 to 0.726/0.713 for dental caries and from 0.623/0.563 to 0.752/0.740 for periapical periodontitis. CONCLUSIONS: Based on these experimental results, deep learning can improve the accuracy and consistency of evaluating dental caries and periapical periodontitis on periapical radiographs. CLINICAL SIGNIFICANCE: Deep learning models can improve accuracy and consistency and reduce the workload of dentists, making artificial intelligence a powerful tool for clinical practice.

Machine learning in orthodontics: Challenges and perspectives.

Artificial intelligence (AI) applications have significantly improved our everyday quality of life. The last decade has witnessed the emergence of up-and-coming applications in the field of dentistry. It is hopeful that AI, especially machine learning (ML), due to its powerful capacity for image processing and decision support systems, will find extensive application in orthodontics in the future. We performed a comprehensive literature review of the latest studies on the application of ML in orthodontic procedures, including diagnosis, decision-making and treatment. Machine learning models have been found to perform similar to or with even higher accuracy than humans in landmark identification, skeletal classification, bone age prediction, and tooth segmentation. Meanwhile, compared to human experts, ML algorithms allow for high agreement and stability in orthodontic decision-making procedures and treatment effect evaluation. However, current research on ML raises important questions regarding its interpretability and dataset sample reliability. Therefore, more collaboration between orthodontic professionals and technicians is urged to achieve a positive symbiosis between AI and the clinic.

Scaling Metal-Elastomer Composites toward Stretchable Multi-Helical Conductive Paths for Robust Responsive Wearable Health Devices.

Stretchable electronics have advanced rapidly and many applications require high repeatability and robustness under various mechanical deformations. It has been described here that how a highly stretchable and reliable conductor composite made from helical copper wires and a soft elastomer, named eHelix, can provide mechanically robust and strain-insensitive electronic conductivity for wearable devices. The reversibility of the mechanical behavior of the metal-elastomer system has been studied using finite element modeling methods. Optimal design parameters of such helical metal-elastomer structures are found. The scaling of multiple copper wires into such helical shapes to form a Multi-eHelix system is further shown. With the same elastomer volume, Multi-eHelix has more conductive paths and a higher current density than the single-eHelix. Integrations of these eHelix stretchable conductors with fabrics showed wearable displays that can survive machine-washes and hundreds of mechanical loading cycles. The integration of the eHelix developed by us with a wearable optical heart rate sensor enabled a wearable health monitoring system that can display measured heart rates on clothing. Furthermore, Multi-eHelix conductors are used to connect flexible printed circuit boards and piezoresistive sensors on a tactile sensing glove for the emerging sensorized prosthetics.

Risk factors for postoperative hemorrhage in patients with oral squamous cell carcinoma: A retrospective study.

BACKGROUND: Postoperative hemorrhage is one of the life-threatening complications of oral cancer surgery. The purpose of this study was to identify the risk factors in a large cohort. PATIENTS AND METHODS: Patients with oral squamous cell carcinoma who had received surgery were enrolled. The variables between patients with and without postoperative hemorrhage were compared using univariate and multivariate models. RESULTS: Of the 1513 patients (or 1581 cases) enrolled in the study, 34 patients suffered from postoperative hemorrhage. In the univariate analysis, cigarette, and alcohol consumption, floor of mouth tumors, T4 classified tumors, flap reconstruction, surgical site infection, and flap necrosis were risk factors for postoperative hemorrhage. In the multivariate model, flap necrosis and surgical site infection were independent risk factors for postoperative hemorrhage. CONCLUSIONS: The patients with surgical site infection or flap necrosis should be closely monitored in order to avoid postoperative hemorrhage.

Computer-assisted resection and reconstruction of bilateral osteoradionecrosis of the mandible using 2 separate flaps prepared from a single fibula.

OBJECTIVES: Osteoradionecrosis of the mandible is a late radiation-induced complication, which is a major concern in survivors of head and neck cancer. STUDY DESIGN: In this study, we present a case of a patient with nasopharyngeal carcinoma who developed extensive bilateral osteoradionecrosis of the ascending ramus of the mandible. After preoperative virtual surgical planning, the obtained data were used to fabricate patient-specific cutting templates. The bilateral mandibular defects were reconstructed using 2 separate flaps prepared from a single fibula. RESULTS: Both defects were successfully reconstructed, and satisfactory aesthetic and functional results were achieved. CONCLUSIONS: Bilateral mandibular osteoradionecrosis can be managed with virtual surgical planning, and the defects can be reconstructed using 2 separate flaps prepared from a single fibula.