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Abstract Background Owing to the remarkable advancements of artificial intelligence (AI) applications, AI-based detection of dental caries is continuously improving. We evaluated the efficacy of the detection of dental caries with quantitative light-induced fluorescence (QLF) images using a convolutional neural network (CNN) model. Methods Overall, 2814 QLF intraoral images were obtained from 606 participants at a dental clinic using Qraypen C® (QC, AIOBIO, Seoul, Republic of Korea) from October 2020 to October 2022. These images included all the types of permanent teeth of which surfaces were smooth or occlusal. Dataset were randomly assigned to the training (56.0%), validation (14.0%), and test (30.0%) subsets of the dataset for caries classification. Moreover, masked images for teeth area were manually prepared to evaluate the segmentation efficacy. To compare diagnostic performance for caries classification according to the types of teeth, the dataset was further classified into the premolar (1,143 images) and molar (1,441 images) groups. As the CNN model, Xception was applied. Results Using the original QLF images, the performance of the classification algorithm was relatively good showing 83.2% of accuracy, 85.6% of precision, and 86.9% of sensitivity. After applying the segmentation process for the tooth area, all the performance indics including 85.6% of accuracy, 88.9% of precision, and 86.9% of sensitivity were improved. However, the performance indices of each type of teeth (both premolar and molar) were similar to those for all teeth. Conclusion The application of AI to QLF images for caries classification demonstrated a good performance regardless of teeth type among posterior teeth. Additionally, tooth area segmentation through background elimination from QLF images exhibited a better performance.

Oral mucosal burns can occur after contact with various chemical agents, and commonly manifest as areas of mucosal sloughing and ulceration. Policresulen (Albothyl, Celltrion Pharm Inc.) is an over-the-counter topical antiseptic that is frequently used to treat stomatitis. Policresulen solution is highly acidic, with an approximate pH of 0.6; it can thus cause mucosal injury when improperly applied in the oral cavity. Here, we present a rare case of an oral mucosal burn resulting from incorrect self-administration of policresulen and emphasize the importance of increasing understanding of this adverse drug event among consumers and health professionals.

With recent developments in digital dentistry, research on techniques and materials for three-dimensional (3D) printing is actively underway. We report the clinical applications and outcomes of 3D printing of temporary crowns fabricated with polylactic acid (PLA) using a fused deposition modeling (FDM) printer. Five participants were recruited from among patients scheduled to be treated with a single full-coverage crown at a dental clinic in a university medical center from June to August 2022. We used 3D-printed crowns fabricated with PLA using an FDM printer as temporary crowns and were assessed for discomfort, fracture, and dislodging. The 3D-printed temporary crowns were maintained without fracture, dislodging, or discomfort until the permanent prosthesis was ready. The average time required for printing the temporary crowns was approximately 7 minutes. The 3D printing of temporary crowns with PLA using an FDM printer is a convenient process for dentists. However, these crowns have some limitations, such as rough surface texture and translucency; therefore, the 3D printing process should be improved to produce better prostheses.

Camera image-based deep learning (DL) techniques have achieved promising results in dental caries screening. To apply the intraoral camera image-based DL technique for dental caries detection and assess its diagnostic performance, we employed the ensemble technique in the image classification task. 2,682 intraoral camera images were used as the dataset for image classification according to dental caries presence and caries-lesion localization using DL models such as ResNet-50, Inception-v3, Inception-ResNet-v2, and Faster R-convolutional neural network according to diagnostic study design. 534 participants whose mean age [SD] was 47.67 [±13.94] years were enrolled. The dataset was divided into training (56.0%), validation (14.0%), and test subset (30.0%) annotated by one experienced dentist as a reference standard about dental caries detection and lesion location. The confusion matrix, area under the receiver operating characteristic curve (AUROC), and average precision (AP) were evaluated for performance analysis. In the end-to-end dental caries image classification, the ensemble DL models had consistently improved performance, in which as the best results, the ensemble model of Inception-ResNet-v2 achieved 0.94 of AUROC and 0.97 of AP. On the other hand, the explainable model achieved 0.91 of AUROC and 0.96 of AP after the ensemble application. For dental caries classification using intraoral camera images, the application of ensemble techniques exhibited consistently improved performance regardless of the DL models. Furthermore, the trial to create an explainable DL model based on carious lesion detection yielded favorable results.

Background Clinical management of ventilator-assisted individuals (VAIs) was challenged by social distancing rules during the COVID-19 pandemic. In May 2020, the Long-Term In-Home Ventilator Engagement (LIVE) Program was launched in Ontario, Canada to provide intensive digital care case management to VAIs. The purpose of this qualitative study was to explore the acceptability of the LIVE Program hosted via a digital platform during the COVID-19 pandemic from diverse perspectives. Methods We conducted a qualitative descriptive study (May 2020–April 2021) comprising semi-structured interviews with participants from eight home ventilation specialty centers in Ontario, Canada. We purposively recruited patients, family caregivers, and providers enrolled in LIVE. Content analysis and the theoretical concepts of acceptability, feasibility, and appropriateness were used to interpret findings. Results A total of 40 individuals (2 VAIs, 18 family caregivers, 20 healthcare providers) participated. Participants described LIVE as acceptable as it addressed a longstanding imperative to improve care access, ease of use, and training provided; feasible for triaging problems and sharing information; and appropriate for timeliness of provider responses, workflows, and perceived value. Negative perceptions of acceptability among healthcare providers concerned digital workload and fit with existing clinical workflows. Perceived benefits accorded to LIVE included enhanced physical and psychological safety in the home, patient–provider relations, and VAI engagement in their own care. Conclusions Study findings identify factors influencing the LIVE Program's acceptability by patients, family caregivers, and healthcare providers during pandemic conditions including enhanced access to care, ease of case management triage, and VAI safety. Findings may inform the implementation of digital health services to VAIs in non-pandemic circumstances.

Background Residual monomer tests using high-performance liquid chromatography and cytotoxicity tests were performed to analyze the effect on the oral mucosa of a self-curing resin for provisional crown production. Methods A cytotoxicity test was performed to confirm whether leaked residual monomers directly affected oral mucosal cells. The cytotoxicity of the liquid and solid resin polymers was measured using a water-soluble tetrazolium (WST) test and microplate reader. Results In the WST assay using a microplate reader, 73.4% of the cells survived at a concentration of 0.2% liquid resin polymer. The cytotoxicity of the liquid resin polymer was low at ≤0.2%. For the solid resins, when 100% of the eluate was used from each specimen, the average cell viability was 91.3% for the solid resin polymer and 100% for the hand-mixed self-curing resin, which is higher than the cell viability standard of 70%. The cytotoxicity of the solid resin polymer was low. Conclusion Because the polymerization process of the self-curing resin may have harmful effects on the oral mucosa during the second and third stages, the solid resin should be manufactured indirectly using a dental model.

Abstract Background Intraoral photographic images are helpful in the clinical diagnosis of caries. Moreover, the application of artificial intelligence to these images has been attempted consistently. This study aimed to evaluate a deep learning algorithm for caries detection through the segmentation of the tooth surface using these images. Methods In this prospective study, 2348 in-house intraoral photographic images were collected from 445 participants using a professional intraoral camera at a dental clinic in a university medical centre from October 2020 to December 2021. Images were randomly assigned to training (1638), validation (410), and test (300) datasets. For image segmentation of the tooth surface, classification, and localisation of caries, convolutional neural networks (CNN), namely U-Net, ResNet-18, and Faster R-CNN, were applied. Results For the classification algorithm for caries images, the accuracy and area under the receiver operating characteristic curve were improved to 0.813 and 0.837 from 0.758 to 0.731, respectively, through segmentation of the tooth surface using CNN. Localisation algorithm for carious lesions after segmentation of the tooth area also showed improved performance. For example, sensitivity and average precision improved from 0.890 to 0.889 to 0.865 and 0.868, respectively. Conclusion The deep learning model with segmentation of the tooth surface is promising for caries detection on photographic images from an intraoral camera. This may be an aided diagnostic method for caries with the advantages of being time and cost-saving.

Abstract Socializing students to Academic Integrity (AI) in the face of great cultural, linguistic and socioeconomic diversity in the student population in higher education calls for innovative strategies that are aligned with equity, diversity and inclusion principles. Through a mixed method of quantitative analysis of learner engagement data from the Learning Management System (LMS) and analysis of anonymous evaluation survey, along with thematic analysis of students’ open-ended responses in the evaluation survey, the authors explored how students responded to AI Socialization during a 4-week non-credit, online co-curricular program called ‘Reading and Writing Excellence’ (RWE). Nine groups of undergraduate students (N=182) from 34 disciplines in different global locations during the COVID-19 pandemic were introduced to a curated set of AI online resources. Through a learner-driven, instructor-facilitated approach the AI Socialization also engaged students in language development and empowered them to communicate about their disciplinary course topics through written journal entries, receiving instructor feedback that increased their cultural and linguistic capital for further academic writing. This approach led to a high volume of written output (on average 6064 words per student written over a 4-week period). Nonparametric ANOVA was used to establish that low-proficiency students were able to produce as much written output as their more proficient peers. Survey results for various aspects important to academic integrity show students’ self-perception of readiness for academic writing: paraphrasing and summarizing (92%); organization of ideas (92%); critical thinking (93%); logic/argument (92%). Insights gained about educative engagement, language development and learner empowerment that can help students from diverse backgrounds to avoid Academic Integrity Violations (AIVs) and gain transformative access and success in higher education are incorporated into a set of recommendations that are applicable to a wide range of teaching contexts.

To determine the vertical dimension and centric relation during the construction of implant overdentures, the record base and wax rim may need to be adjusted. The conventional method has several drawbacks, as it requires repeated tightening and loosening of the impression coping. Here, we report a useful and novel method for interocclusal records using the healing abutments in implant overdentures. Our case demonstrates that this method is easier and simpler and prevents gingival collapse.

Glass ionomer cement (GIC) is a tailor-made material that is used as a filling material in dentistry. GIC is cured by an acid-base reaction consisting of a glass filler and ionic polymers. When the glass filler and ionic polymers are mixed, ionic bonds of the material itself are formed. In addition, the extra polymer anion reacts with calcium in enamel or dentin to increase adhesion to the tooth tissue. GICs are widely used as adhesives for artificial crowns or orthodontic brackets, and are also used as tooth repair material, cavity liner, and filling materials. In this review, the current status of GIC research and development and its prospects for the future have been discussed in detail.

Background This study was performed to provide a long-term bacterial seal through the formation of reparative dentin bridge, calcium silicate-based pulp capping materials have been used at sites of pulpal exposure. The aim of this study was to evaluate the mineralization-inducing potentials of calcium silicate-based pulp capping materials (ProRoot MTA [PR], Biodentine [BD], and TheraCal LC [TC]) in human dental pulp cells (HDPCs). Methods Specimens of test materials were placed in deionized water for various incubation times to measure the pH variation and the concentration of calcium released. The morphology of HDPCs cultured on the specimens was examined using a confocal laser scanning microscope (CLSM). Alizarin red S staining and alkaline phosphatase assays were used to evaluate mineralization-inducing potentials of the capping materials. Results BD showed the highest calcium release in all test periods, followed by PR and TC. (p

Abstract Background Until now, there is no effective anti-fibrotic therapy available for liver cirrhosis. Stem cell therapies have been studied for the treatment of hepatic fibrosis. However, the use of embryonic stem cells or induced pluripotent stem cells (iPSC) has limitations such as ethical concern or malignancy potential. Induced hepatocyte-like cells (iHEPs) generated by direct reprogramming technology may overcome these limitations. Methods In this study, we generated iHEPs by direct reprogramming from mouse embryonic fibroblast (MEF) either using specific transcription factors such as c-Myc and Klf-4 (type A), or adding small molecules to HNF1α (type B). Results We investigated the effect of iHEPs on acute liver injury and chronic hepatic fibrosis animal models induced by CCl4 intra-peritoneal injection in BALB/C nude mice. In acute liver injury model, serum AST/ALT levels peaked at 24 h after CCl4 injection. Intra-splenic transplantation of iHEPs significantly attenuated CCl4-induced acute liver injury. GFP-labeled iHEPs (type A) migrated to the liver after intra-splenic transplantation that was confirmed by Western blotting and immunofluorescence staining. We found that GFP and albumin were co-localized in migrated iHEPs in the liver suggesting migrated iHEPs were functional. In chronic hepatic fibrosis mice experiment, transplantation of either type A or type B iHEPs significantly attenuated liver fibrosis induced by CCl4 injection for 10 weeks. Conclusions Our study suggests that iHEPs may be used as a novel therapeutic strategy for the treatment of hepatic fibrosis.