Enamel demineralization is one of the most common adverse reactions to orthodontic treatment. The existence of orthodontic appliances affects oral hygiene maintenance, which easily leads to plaque accumulation and oral flora dysbiosis, and cariogenic bacteria produce acid to cause enamel demineralization. It not only affects aesthetics but may develop into caries and endanger oral health. Therefore, enamel demineralization has become an urgent problem. Nanoparticles generally refer to solid particles with diameters of 1 to 100 nm and have unique physicochemical properties that provide a new strategy for preventing enamel demineralization during orthodontics. Reviewing the relevant literature, nanoparticles used for the prevention of enamel demineralization in orthodontics may be classified into antibacterial, remineralization and carrier-type nanoparticles according to their functions. Most research was performed on the application of nanoparticles to modify orthodontic adhesives for enhancement of antibacterial or remineralization properties, but some studies also focused on the modification of orthodontic appliances with nanoparticles for surface coating or overall doping to provide antimicrobial properties. The advantage of these two approaches is that they are not dependent on patient compliance. Nanoparticle-modified fluoride varnishes and nanocarriers loaded with antimicrobial or remineralization agents may be used to promote oral health care in orthodontic patients, which have a sustained preventive effect but depend on the cooperation of the patient. It was indicated that the small size effect of nanoparticles provides better performance, but there may be certain safety issues, and there is still some influence on the physicochemical properties of the modified materials themselves. These issues must be further explored. Although there are some limitations in the current studies, nanoparticles are expected to play an important role in the prevention of enamel demineralization during orthodontics in the future.

In orthodontic and orthognathic practice, cephalometric analysis is an integral tool throughout the clinical process. However, as landmark identification is still unautomated, both the conventional and semiautomated approaches are open to considerable subjectivity and could be time-consuming for inexperienced clinicians. Deep learning (DL), a state-of-the-art artificial intelligence (AI) technique, is highly effective in image recognition. In recent years, many studies have focused on the application of DL in cephalometric analysis, including automated landmark detection, automated diagnosis, cervical vertebral maturation stage determination, adenoid hypertrophy analysis and upper airway identification. Studies show that DL can effectively improve the efficiency of cephalometric analysis. In most studies, the accuracy of DL can reach more than 80%, and its difference from the gold standard is clinically acceptable, demonstrating good potential for future applications. However, most studies are limited to landmark detection, and the broadness and richness of the training dataset are limited. Future studies should broaden the research scope, improve the algorithm, elevate the richness of the datasets, and combine DL with other AI algorithms to improve its accuracy, stability and generalizability.

During orthodontic treatment, clinical monitoring of patients is a crucial factor in determining treatment success. It aids in timely problem detection and resolution, ensuring adherence to the intended treatment plan. In recent years, digital technology has increasingly permeated orthodontic clinical diagnosis and treatment, facilitating clinical decision-making, treatment planning, and follow-up monitoring. This review summarizes recent advancements in digital technology for monitoring orthodontic tooth movement, related complications, and appliance-wearing compliance. It aims to provide insights for researchers and clinicians to enhance the application of digital technology in orthodontics, improve treatment outcomes, and optimize patient experience. The digitization of diagnostic data and the visualization of dental models make chair-side follow-up monitoring more convenient, accurate, and efficient. At the same time, the emergence of remote monitoring technology allows orthodontists to promptly identify oral health issues in patients and take corresponding measures. Furthermore, the multimodal data fusion method offers valuable insights into the monitoring of the root-alveolar relationship. Artificial intelligence technology has made initial strides in automating the identification of orthodontic tooth movement, associated complications, and patient compliance evaluation. Sensors are effective tools for monitoring patient adherence and providing data-driven support for clinical decision-making. The application of digital technology in orthodontic monitoring holds great promise. However, challenges like technical bottlenecks, ethical considerations, and patient acceptance remain.