Advanced periodontitis poses a significant threat to oral health, causing extensive damage and loss of both hard and soft periodontal tissues. While traditional therapies such as scaling and root planing can effectively halt the disease’s progression, they often fail to fully restore the original architecture and function of periodontal tissues due to the limited capacity for spontaneous regeneration. To address this challenge, periodontal tissue engineering has emerged as a promising approach. This technology centers on the utilization of biomaterial scaffolds, which function as three-dimensional (3D) templates or frameworks, supporting and guiding the regeneration of periodontal tissues, including the periodontal ligament, cementum, alveolar bone, and gingival tissue. These scaffolds mimic the extracellular matrix (ECM) of native periodontal tissues, aiming to foster cell attachment, proliferation, differentiation, and, ultimately, the formation of new, functional periodontal structures. Despite the inherent challenges associated with preclinical testing, the intensification of research on biomaterial scaffolds, coupled with the continuous advancement of fabrication technology, leads us to anticipate a significant expansion in their application for periodontal tissue regeneration. This review comprehensively covers the recent advancements in biomaterial scaffolds engineered specifically for periodontal tissue regeneration, aiming to provide insights into the current state of the field and potential directions for future research.

Periodontitis is a dysbiotic biofilm-induced and host-mediated inflammatory disease of tooth supporting tissues that leads to progressive destruction of periodontal ligament and alveolar bone, thereby resulting in gingival recession, deep periodontal pockets, tooth mobility and exfoliation, and aesthetically and functionally compromised dentition. Due to the improved biopharmaceutical and pharmacokinetic properties and targeted and controlled drug release, nano-based drug delivery systems have emerged as a promising strategy for the treatment of periodontal defects, allowing for increased efficacy and safety in controlling local inflammation, establishing a regenerative microenvironment, and regaining bone and attachments. This review provides an overview of nano-based drug delivery systems and illustrates their practical applications, future prospects, and limitations in the field of periodontal tissue regeneration.

The soft tissue morphology after orthognathic surgery has always been difficult to predict in preoperative planning. This narrative review aims to evaluate the current prediction methods and point out the direction for future research to improve predicting accuracy and speed. PubMed was the database for the review. The keywords for searching include: orthognathic surgery, prediction methods, simulation methods, artificial intelligence (AI), machine learning (ML), deep learning (DL), soft tissue, and surgical planning. Sixty‐seven articles were found and 60 relevant articles were evaluated. The review finds out that with the transition from two‐dimensional (2D) prediction to three‐dimensional (3D) prediction, AI algorithms, especially DL methods, are brought forward promisingly, such as deep neural network (DNN), PointNet, and Transformer. DL methods have the advantages of high accuracy, fast speed, and simplified use, therefore have broad development prospects. However, DL methods need to be further improved, mainly in data insufficiency, overfitting, and the lack of interpretability. To conclude, the review proposes methods that could improve the accuracy and availability of prediction by DL, such as learning from large and high‐quality databases, establishing separate databases for groups with different features, optimizing and simplifying the algorithm structure, utilizing integrated multimodal data, and implementing visualization techniques.