Multi-Quantifying Maxillofacial Traits via a Demographic Parity-Based AI Model.

The multi-quantification of the distinct individualized maxillofacial traits, that is, quantifying multiple indices, is vital for diagnosis, decision-making, and prognosis of the maxillofacial surgery. While the discrete and demographically disproportionate distributions of the multiple indices restrict the generalization ability of artificial intelligence (AI)-based automatic analysis, this study presents a demographic-parity strategy for AI-based multi-quantification. In the aesthetic-concerning maxillary alveolar basal bone, which requires quantifying a total of 9 indices from length and width dimensional, this study collected a total of 4,000 cone-beam computed tomography (CBCT) sagittal images, and developed a deep learning model composed of a backbone and multiple regression heads with fully shared parameters to intelligently predict these quantitative metrics.

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.

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.

"Gingival Soft Tissue Integrative" Lithium Disilicate Glass-Ceramics with High Mechanical Properties and Sustained-Release Lithium Ions.

Due to their good mechanical performances and high biocompatibility, all-ceramic materials are widely applied in clinics, especially in orthopedic and dental areas. However, the "hard" property negatively affects its integration with "soft" tissue, which greatly limits its application in soft tissue-related areas. For example, dental implant all-ceramic abutments should be well integrated with the surrounding gingival soft tissue to prevent the invasion of bacteria.

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.

Mesopore Controls the Responses of Blood Clot-Immune Complex via Modulating Fibrin Network.

Formation of blood clots, particularly the fibrin network and fibrin network-mediated early inflammatory responses, plays a critical role in determining the eventual tissue repair or regeneration following an injury. Owing to the potential role of fibrin network in mediating clot-immune responses, it is of great importance to determine whether clot-immune responses can be regulated via modulating the parameters of fibrin network. Since the diameter of D-terminal of a fibrinogen molecule is 9 nm, four different pore sizes (2, 8, 14, and 20 nm) are rationally selected to design mesoporous silica to control the fibrinogen adsorption and modulate the subsequent fibrin formation process.