The development and training effect of innovative undergraduate dental talents training project: A 6-year retrospective study.

INTRODUCTION: To summarize the development of Innovative Undergraduate Dental Talents Training Project (IUDTTP) and investigate the training effect of this extracurricular dental basic research education activity from 2015 to 2020 to obtain educational implications. MATERIALS AND METHODS: The Guanghua School of Stomatology established the IUDTTP in 2015. The authors recorded the development process and analysed the participation situation, training effect, academic performance and overall satisfaction during 2015-2020 through documental analysis, questionnaire and quiz. The t-test, chi-square test and ANOVA were used to test the difference. RESULTS: The educational goal, education module and assessment system of IUDTTP evolved and developed every year. A total of 336 students and 79 mentors attended the IUDTTP from 2015 to 2020, with the participation rate increasing from 45.1% to 73.5%. The participants exhibited favourable basic research abilities, manifesting as the increase of funded projects and published papers and satisfying quiz scores. Almost all students (94.94%) admitted their satisfaction with the IUDTTP. Moreover, the attended students surpassed the non-participants in terms of GPA, the number of acquired scholarships and outstanding graduates (p < .05). Likewise, the enrolment rate of postgraduate participants was significantly higher than non-participants. CONCLUSIONS: To date, the training effect indicated that the IUDTTP has fulfilled the education aim. It brought positive effects on promoting research interest, cultivating research capacities and enhancing academic performance. The potential deficiencies of extracurricular educational activities, including inflexibility in schedule and insufficiency in systematisms, may be remedied by more systematic educational settings in the future.

Abnormal maxillary sinus diagnosing on CBCT images via object detection and 'straight-forward' classification deep learning strategy.

BACKGROUND: Pathological maxillary sinus would affect implant treatment and even result in failure of maxillary sinus lift and implant surgery. However, the maxillary sinus abnormalities are challenging to be diagnosed through CBCT images, especially for young dentists or dentists in grassroots medical institutions without systematical education of general medicine. OBJECTIVES: To develop a deep-learning-based screening model incorporating object detection and 'straight-forward' classification strategy to screen out maxillary sinus abnormalities on CBCT images. METHODS: The large area of background noise outside maxillary sinus would affect the generalisation and prediction accuracy of the model, and the diversity and imbalanced distribution of imaging manifestations may bring challenges to intellectualization. Thus we adopted an object detection to limit model's observation zone and 'straight-forward' classification strategy with various tuning methods to adapt to dental clinical need and extract typical features of diverse manifestations so that turn the task into a 'normal-or-not' classification. RESULTS: We successfully constructed a deep-learning model consist of well-trained detector and diagnostor module. This model achieved ideal AUROC and AUPRC of 0.953 and 0.887, reaching more than 90% accuracy at optimal cut-off. McNemar and Kappa test verified no statistical difference and high consistency between the prediction and ground truth. Dentist-model comparison test showed the model's statistically higher diagnostic performance than dental students. Visualisation method confirmed the model's effectiveness in region recognition and feature extraction. CONCLUSION: The deep-learning model incorporating object detection and straightforward classification strategy could achieve satisfying predictive performance for screening maxillary sinus abnormalities on CBCT images.

The picture of undergraduate dental basic research education: a scoping review.

BACKGROUND: Undergraduate dental basic research education (UDBRE) is broadly regarded as an important approach for cultivating scientific research talent. This scoping review aims to summarize the current status of UDBRE in terms of educational goals, teaching program and content, assessment system, training outcomes, barriers, and reflections. METHODS: The authors performed a systematic literature search in PubMed, Web of Science, and Education Resources Information Center (ERIC) to identify peer-reviewed articles written in English from their inception to January 29, 2021. Articles were reviewed and screened according to the inclusion and exclusion criteria. Related data from the included publications were then collected and summarized. RESULTS: The authors searched 646 publications and selected 16 articles to include in the study. The education goals included cultivating five major dental basic research capabilities (n=10, 62.5%) and developing interest in basic research (n=2, 12.5%). As for the teaching program, the mentor-guided student research project was the most popular (n=11, 68.8%), followed by didactic courses (n=5, 31.3%), experimental skills training (n=1, 6.3%), and the combination of the above forms (n=3, 18.8%). However, the assessment system and training outcome diverged. Existing evidence showed that UDBRE reached satisfying education outcomes. Barriers included excessive curriculum burden (n=2, 12.5%), tutor shortage (n=3, 18.8%), lack of financial support (n=5, 31.3%), and inadequate research skills and knowledge (n=5, 31.3%). CONCLUSIONS: Although efforts were made, the variation between studies revealed the immature status of UDBRE. A practical UDBRE education system paradigm was put forward. Meanwhile, more research is required to optimize a robust UDBRE system with clear education goals, well-designed teaching forms, and convincing assessment systems.

Analysis of the sagittal root angle and its correlation with hard and soft tissue indices in anterior teeth for immediate implant evaluation: a retrospective study.

BACKGROUND: To assess the root angle characteristics of maxillary incisors, and to analyze the relationship between the root angle and other implant-related anatomical indices to use the sagittal root angle as an index for immediate implant evaluation and design. METHODS: A random sample consisting of 400 cone-beam computed tomography (CBCT) images and 65 maxillary plaster models were selected for the present study. CBCT and stereolithography (STL) scan images were imported as DICOM files into coDiagnostiX software for matching the hard and soft tissue. The angle between the long axis of the anterior tooth and the corresponding alveolar bone and implant-related hard and soft tissue indices were measured in the sagittal section. Descriptive statistics, frequency analysis, multi-level comparisons, and correlation analyses were performed. RESULTS: The average sagittal root angles were 15° at the central incisor and 19° at the lateral incisor. The root angle in males was significantly larger than that in females, and increased with age. The largest angle, 22.35°, was found in the lateral incisors of the oldest (> 50 years old) male group. The root angle was found to correlate with coronal buccal bone thickness, coronal palatal bone thickness, apical buccal bone thickness, palatal bone thickness, and the below apex bone thickness. CONCLUSIONS: The sagittal root angle could reflect the distribution of other implant-related anatomical indices, which may provide additional reference for the evaluation of immediate implant placement.

Integrated correlation analysis of the thickness of buccal bone and gingiva of maxillary incisors.

OBJECTIVE: This study aimed to validate the integrated correlation between the buccal bone and gingival thickness of the anterior maxilla, and to gain insight into the reference plane selection when measuring these two tissues before treatment with implants. METHODOLOGY: Cone beam computed tomography (CBCT) and model scans of 350 human subjects were registered in the coDiagnostiX software to obtain sagittal maxillary incisor sections. The buccal bone thickness was measured at the coronal (2, 4, and 6 mm apical to the cementoenamel junction [CEJ]) and apical (0, 2, and 4 mm coronal to the apex plane) regions. The buccal gingival thickness was measured at the supra-CEJ (0, 1mm coronal to the CEJ) and sub-CEJ regions (1, 2, 4, and 6 mm apical to the CEJ). Canonical correlation analysis was performed for intergroup correlation analysis and investigation of key parameters. RESULTS: The mean thicknesses of the buccal bone and gingiva at different levels were 0.64~1.88 mm and 0.66~1.37 mm, respectively. There was a strong intergroup canonical correlation between the thickness of the buccal bone and that of the gingiva (r=0.837). The thickness of the buccal bone and gingiva at 2 mm apical to the CEJ are the most important indices with the highest canonical correlation coefficient and loadings. The most and least prevalent subgroups were the thin bone and thick gingiva group (accounting for 47.6%) and the thick bone and thick gingiva group (accounting for 8.6%). CONCLUSION: Within the limitations of this retrospective study, the thickness of the buccal bone is significantly correlated with that of the buccal gingiva, and the 2 mm region apical to the CEJ is a vital plane for quantifying the thickness of these two tissues.

Quantification of the apical palatal bone index for maxillary incisor immediate implant assessment: A retrospective cross-sectional study.

BACKGROUND: Apical palatal bone is important in immediate implant evaluation. Current consensus gives qualitative suggestions regarding it, limiting its clinical decision-making value. OBJECTIVES: To quantify the apical palatal bone dimension in maxillary incisors and reveal its quantitative correlation with other implant-related hard tissue indices to give practical advice for pre-immediate implant evaluation and design. MATERIAL AND METHODS: A retrospective analysis of immediate implant-related hard tissue indices in maxillary incisors obtained by cone beam computed tomography (CBCT) was conducted. Palatal bone thickness at the apex level (Apical-P) on the sagittal section was selected as a parameter reflecting the apical palatal bone. Its quantitative correlation with other immediate implant-related hard tissue indices was revealed. Clinical advice of pre-immediate implant assessment was given based on the quantitative classification of Apical-P and its other correlated immediate implant-related hard tissue indices. RESULTS: Apical-P positively correlated with cervical palatal bone, whole cervical buccal-palatal bone, sagittal root angle, and basal bone width indices. while negatively correlated with apical buccal bone, cervical buccal bone, and basal bone length indices. Six quantitative categories of Apical-P are proposed. Cases with Apical-P below 4 mm had an insufficient apical bone thickness to accommodate the implant placement, while Apical-P beyond 12 mm should be cautious about the severe implant inclination. Cases with Apical-P of 4-12 mm can generally achieve satisfying immediate implant outcomes via regulating the implant inclination. CONCLUSIONS: Quantification of the apical palatal bone index for maxillary incisor immediate implant assessment can be achieved, providing a quantitative guide for immediate implant placement in the maxillary incisor zone.

Regulating Blood Clot Fibrin Films to Manipulate Biomaterial-Mediated Foreign Body Responses.

The clinical efficacy of implanted biomaterials is often compromised by host immune recognition and subsequent foreign body responses (FBRs). During the implantation, biomaterials inevitably come into direct contact with the blood, absorbing blood protein and forming blood clot. Many studies have been carried out to regulate protein adsorption, thus manipulating FBR. However, the role of clot surface fibrin films formed by clotting shrinkage in host reactions and FBR is often ignored. Because of the principle of fibrin film formation being relevant to fibrinogen or clotting factor absorption, it is feasible to manipulate the fibrin film formation via tuning the absorption of fibrinogen and clotting factor. As biological hydroxyapatite reserved bone architecture and microporous structure, the smaller particle size may expose more microporous structures and adsorb more fibrinogen or clotting factor. Therefore, we set up 3 sizes (small, <0.2 mm; medium, 1 to 2 mm; large, 3 to 4 mm) of biological hydroxyapatite (porcine bone-derived hydroxyapatite) with different microporous structures to investigate the absorption of blood protein, the formation of clot surface fibrin films, and the subsequent FBR. We found that small group adsorbed more clotting factors because of more microporous structures and formed the thinnest and sparsest fibrin films. These thinnest and sparsest fibrin films increased inflammation and profibrosis of macrophages through a potential signaling pathway of cell adhesion-cytoskeleton-autophagy, leading to the stronger FBR. Large group adsorbed lesser clotting factors, forming the thickest and densest fibrin films, easing inflammation and profibrosis of macrophages, and finally mitigating FBR. Thus, this study deepens the understanding of the role of fibrin films in host recognition and FBR and demonstrates the feasibility of a strategy to regulate FBR by modulating fibrin films via tuning the absorption of blood proteins.

Measuring the binary thickness of buccal bone of anterior maxilla in low-resolution cone-beam computed tomography via a bilinear convolutional neural network.

Background: The thickness of the buccal bone of the anterior maxilla is an important aesthetic-determining factor for dental implant, which is divided into the thick (≥1 mm) and thin type (<1 mm). However, as a micro-scale structure that is evaluated through low-resolution cone-beam computed tomography (CBCT), its thickness measurement is error-prone under the circumstance of enormous patients and relatively inexperienced primary dentists. Further, the challenges of deep learning-based analysis of the binary thickness of buccal bone include the substantial real-world variance caused by pixel error, the extraction of fine-grained features, and burdensome annotations. Methods: This study built bilinear convolutional neural network (BCNN) with 2 convolutional neural network (CNN) backbones and a bilinear pooling module to predict the binary thickness of buccal bone (thick or thin) of the anterior maxilla in an end-to-end manner. The methods of 5-fold cross-validation and model ensemble were adopted at the training and testing stages. The visualization methods of Gradient Weighted Class Activation Mapping (Grad-CAM), Guided Grad-CAM, and layer-wise relevance propagation (LRP) were used for revealing the important features on which the model focused. The performance metrics and efficacy were compared between BCNN, dentists of different clinical experience (i.e., dental student, junior dentist, and senior dentist), and the fusion of BCNN and dentists to investigate the clinical feasibility of BCNN. Results: Based on the dataset of 4,000 CBCT images from 1,000 patients (aged 36.15±13.09 years), the BCNN with visual geometry group (VGG)16 backbone achieved an accuracy of 0.870 [95% confidence interval (CI): 0.838-0.902] and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.924 (95% CI: 0.896-0.948). Compared with the conventional CNNs, BCNN precisely located the buccal bone wall over irrelevant regions. The BCNN generally outperformed the expert-level dentists. The clinical diagnostic performance of the dentists was improved with the assistance of BCNN. Conclusions: The application of BCNN to the quantitative analysis of binary buccal bone thickness validated the model's excellent ability of subtle feature extraction and achieved expert-level performance. This work signals the potential of fine-grained image recognition networks to the precise quantitative analysis of micro-scale structures.

Improving hard metal implant and soft tissue integration by modulating the "inflammatory-fibrous complex" response.

Soft tissue integration is one major difficulty in the wide applications of metal materials in soft tissue-related areas. The inevitable inflammatory response and subsequent fibrous reaction toward the metal implant is one key response for metal implant-soft tissue integration. It is of great importance to modulate this inflammatory-fibrous response, which is mainly mediated by the multidirectional interaction between fibroblasts and macrophages. In this study, macrophages are induced to generate M1 and M2 macrophage immune microenvironments. Their cytokine profiles have been proven to have potentially multi-regulatory effects on fibroblasts. The multi-reparative effects of soft tissue cells (human gingival fibroblasts) cultured on metal material (titanium alloy disks) in M1 and M2 immune microenvironments are then dissected. Fibroblasts in the M1 immune microenvironment tend to aggravate the inflammatory response in a pro-inflammatory positive feedback loop, while M2 immune microenvironment enhances multiple functions of fibroblasts in soft tissue integration, including soft tissue regeneration, cell adhesion on materials, and contraction to immobilize soft tissue. Enlighted by the close interaction between macrophages and fibroblasts, we propose the concept of an "inflammatory-fibrous complex" to disclose possible methods of precisely and effectively modulating inflammatory and fibrous responses, thus advancing the development of metal soft tissue materials.

Construction of an end-to-end regression neural network for the determination of a quantitative index sagittal root inclination.

BACKGROUND: Immediate implant placement in the esthetic area requires comprehensive assessments with nearly 30 quantitative indexes. Most artificial intelligence (AI)-driven measurements of quantitative indexes depend on segmentation or landmark detection, which require extra labeling of images and contain possible intraclass errors. METHODS: For the initial attempt, the method was tested on sagittal root inclination measurement. This study had developed an accurate and efficient end-to-end model incorporating a convolutional neural network (CNN) based on unlabeled cone-beam computed tomography (CBCT) images for immediate implant placement diagnosis and treatment. The model took pretrained ResNeXt101 as the backbone and was constructed based on 2,920 CBCT images with corresponding angles of the tooth axis and bone axis. The performance of our CNN model was evaluated on a separate test set. RESULTS: Our model exhibited high prediction accuracy in sagittal root inclination measurements, as evidenced by the low mean average error of 2.16°, the high correlation coefficient of 0.915 to manual measurement, and the narrow 95% confidence interval shown by Bland-Altman plots. The intraclass correlation coefficient further confirmed the measurement accuracy of our model was comparable with that of junior clinicians. The model took merely 0.001 seconds for each CBCT image, making it highly efficient. To better understand the model's quality, we visualized our end-to-end CNN model through Guided Backpropagation, Grad-CAM, and Guided Grad-CAM, and confirmed its effectiveness in region recognition. CONCLUSIONS: We succeeded in taking the first step in constructing the end-to-end immediate implant placement AI tool through sagittal root inclination measurements without intermediate steps and extra labeling on images.

Plasma polymerized bio-interface directs fibronectin adsorption and functionalization to enhance "epithelial barrier structure" formation via FN-ITG ß1-FAK-mTOR signaling cascade.

BACKGROUND: Transepithelial medical devices are increasing utilized in clinical practices. However, the damage of continuous natural epithelial barrier has become a major risk factor for the failure of epithelium-penetrating implants. How to increase the "epithelial barrier structures" (focal adhesions, hemidesmosomes, etc.) becomes one key research aim in overcoming this difficulty. Directly targeting the in situ "epithelial barrier structures" related proteins (such as fibronectin) absorption and functionalization can be a promising way to enhance interface-epithelial integration. METHODS: Herein, we fabricated three plasma polymerized bio-interfaces possessing controllable surface chemistry. Their capacity to adsorb and functionalize fibronectin (FN) from serum protein was compared by Liquid Chromatography-Tandem Mass Spectrometry. The underlying mechanisms were revealed by molecular dynamics simulation. The response of gingival epithelial cells regarding the formation of epithelial barrier structures was tested. RESULTS: Plasma polymerized surfaces successfully directed distinguished protein adsorption profiles from serum protein pool, in which plasma polymerized allylamine (ppAA) surface favored adsorbing adhesion related proteins and could promote FN absorption and functionalization via electrostatic interactions and hydrogen bonds, thus subsequently activating the ITG ß1-FAK-mTOR signaling and promoting gingival epithelial cells adhesion. CONCLUSION: This study offers an effective perspective to overcome the current dilemma of the inferior interface-epithelial integration by in situ protein absorption and functionalization, which may advance the development of functional transepithelial biointerfaces. Tuning the surface chemistry by plasma polymerization can control the adsorption of fibronectin and functionalize it by exposing functional protein domains. The functionalized fibronectin can bind to human gingival epithelial cell membrane integrins to activate epithelial barrier structure related signaling pathway, which eventually enhances the formation of epithelial barrier structure.

Integrated correlation analysis of the thickness of buccal bone and gingiva of maxillary incisors

Abstract Objective This study aimed to validate the integrated correlation between the buccal bone and gingival thickness of the anterior maxilla, and to gain insight into the reference plane selection when measuring these two tissues before treatment with implants. Methodology Cone beam computed tomography (CBCT) and model scans of 350 human subjects were registered in the coDiagnostiX software to obtain sagittal maxillary incisor sections. The buccal bone thickness was measured at the coronal (2, 4, and 6 mm apical to the cementoenamel junction [CEJ]) and apical (0, 2, and 4 mm coronal to the apex plane) regions. The buccal gingival thickness was measured at the supra-CEJ (0, 1mm coronal to the CEJ) and sub-CEJ regions (1, 2, 4, and 6 mm apical to the CEJ). Canonical correlation analysis was performed for intergroup correlation analysis and investigation of key parameters. Results The mean thicknesses of the buccal bone and gingiva at different levels were 0.64~1.88 mm and 0.66~1.37 mm, respectively. There was a strong intergroup canonical correlation between the thickness of the buccal bone and that of the gingiva (r=0.837). The thickness of the buccal bone and gingiva at 2 mm apical to the CEJ are the most important indices with the highest canonical correlation coefficient and loadings. The most and least prevalent subgroups were the thin bone and thick gingiva group (accounting for 47.6%) and the thick bone and thick gingiva group (accounting for 8.6%). Conclusion Within the limitations of this retrospective study, the thickness of the buccal bone is significantly correlated with that of the buccal gingiva, and the 2 mm region apical to the CEJ is a vital plane for quantifying the thickness of these two tissues