Class III malocclusion represents a growth-related dentofacial deformity with mandibular prognathism in relation to the maxilla and/or cranial base. Its prevalence varies greatly among and within different races, ethnic groups, and geographic regions studied. Class III malocclusion has a multifactorial etiology, which is the expression of a moderate distortion of normal development as a result of interaction between innate factors or genetic hereditary with environmental factors. Various skeletal topographies of underlying Class III malocclusion are due to discrepancy in the maxillary and mandibular growth along with vertical and/or transverse problems apart from sagittal malformations. The spectrum of complications for Class III malocclusion ranges in gravity from dentoalveolar problems with functional anterior shift of the mandible to true skeletal problems with serious maxillomandibular discrepancies, which makes its diagnosis highly challenging in growing children. Concern regarding early treatment and the need for interceptive care in the case of Class III malocclusion has always been a dilemma, knowing that not all problems will be solved in these cases until maxillomandibular growth is further completed, and the long-term outcome of various treatment approaches may depend on the growth tendency of an individual. Interceptive treatment of Class III malocclusions should be undertaken if it prevents damage to the oral tissues and/or significantly reduces the amount or severity of future orthodontic and surgical interventions. This paper presents an overview of developing Class III malocclusion, with the emphasis on challenges and their solutions based on the best current available evidence.

Objective: This scoping review aims to identify the various areas and current status of the application of artificial intelligence (AI) for aiding individuals with cleft lip and/or palate.Introduction: Cleft lip and/or palate contributes significantly toward the global burden on the healthcare system. Artificial intelligence is a technology that can help individuals with cleft lip and/or palate, especially those in areas with limited access to receive adequate care.Inclusion Criteria: Studies that used artificial intelligence to aid the diagnosis, treatment, or its planning in individuals with cleft lip and/or palate were included.Methodology: A search of the Pubmed, Embase, and IEEE Xplore databases was conducted using search terms artificial intelligence and cleft lip and/or palate. Gray literature was searched using Google Scholar. The study was conducted according to the PRISMA- ScR guidelines.Results: The initial search identified 458 results, which were screened based on title and abstracts. After the screening, removal of duplicates, and a full-text reading of selected articles, 26 publications were included. They explored the use of AI in cleft lip and/or palate to aid in decisions regarding diagnosis, treatment, especially speech therapy, and prediction.Conclusion: There is active interest and immense potential for the use of artificial intelligence in cleft lip and/or palate. Most studies currently focus on speech in cleft palate. Multi-center studies that include different populations, with collaboration amongst academicians and researchers, can further develop the technology.

The inception of laboratory work for the construction of a removable, tooth-moving appliance by sectioning teeth from the malocclusion model to align them with wax and achieve minor dental correction has evolved into a state of digital planning and appliance manufacturing for a wide spectrum of malocclusions. The groundbreaking technology of directly printing clear aligners has drawn the interest of clinicians and researchers in the orthodontic community today. This workflow enables the development of an in-house aligner system with complete control over desired aligner thickness, extent, and attachments; it is also technically resource-efficient with greater accuracy by excluding all the intermediate steps involved in the thermoforming method of manufacturing. This promising exploratory subject demands to be well-received with further research-based improvements. This article intends to summarize the digital orthodontic workflow and the literature evidence.

Objective: The aim of the study was to analyze the caries protective factors, salivary parameters, and microbial counts in high caries risk children with cleft lip and/or palate (CL/P). Design: This was a cross-sectional study. Setting: This study was conducted in a tertiary health care teaching hospital in New Delhi, India. Participants: The study was conducted in 40 children, 20 with CL/P and 20 without aged between 5 and 12 years. Methods: Children with 2 or more caries lesions in both groups were included in this study. Demographic details, dental caries of affected teeth (World Health Organization criteria for Decayed Missing Filled Teeth [WHO-DMFT] and International Caries Detection and Assessment System [ICDAS II]), caries protective factors, salivary parameters, and microbial counts were recorded by one calibrated investigator. Main Outcome Measures: Caries protective factors, salivary parameters, and microbial profile. Results: The Chi-square (χ²) test and Pearson correlation were used for statistical analysis. All the children participating in the study brushed their teeth only once in a day and consumed sweets more than twice a day. None of the children had ever received fluoride varnish. Resting saliva had a low buffering capacity in 80% of children with CL/P and 95% of children without CL/P. Microbial assessment of stimulated saliva showed that with the increases in the numbers (DMFT scores ≥4) and severity (ICDAS codes from 1-2 to 5-6) of caries lesions, both Streptococci and Lactobacilli counts were ≥10⁵ colony-forming units/mL of saliva in the both groups. Conclusions: Children with CL/P showed limited access to caries protective measures and low buffering capacity in resting saliva, along with elevated levels of salivary Streptococci and Lactobacilli in stimulated saliva.

For the assessment of optimum treatment timing in dentofacial orthopedics, understanding the growth process is of paramount importance. The evaluation of skeletal maturity based on study of the morphology of the cervical vertebrae has been devised to minimize radiation exposure of a patient due to hand wrist radiography. Cervical vertebral maturation assessment (CVMA) predictions have been examined in the state-of-the-art machine learning techniques in the recent past which require more attention and validation by clinicians and practitioners. This paper aimed to answer the question “How are machine learning techniques being employed in studies concerning cervical vertebral maturation assessment using lateral cephalograms?” A systematic search through the available literature was performed for this work based upon the Population, Intervention, Comparison and Outcome (PICO) framework. The searches were performed in Ovid Medline, Embase, PubMed and Cochrane Central Register of Controlled Trials (CENTRAL) and Cochrane Database of Systematic Reviews (CDSR). A search of the grey literature was also performed in Google Scholar and OpenGrey. We also did a hand-searching in the Angle Orthodontist, Journal of Orthodontics and Craniofacial Research, Progress in Orthodontics, and the American Journal of Orthodontics and Dentofacial Orthopedics. References from the included articles were also searched. A total of 25 papers which were assessed for full text, and 13 papers were included for the systematic review. The machine learning methods used were scrutinized according to their performance and comparison to human observers/experts. The accuracy of the models ranged between 60 and 90% or above, and satisfactory agreement and correlation with the human observers. Machine learning models can be used for detection and classification of the cervical vertebrae maturation. In this systematic review (SR), the studies were summarized in terms of ML techniques applied, sample data, age range of sample and conventional method for CVMA, which showed that further studies with a uniform distribution of samples equally in stages of maturation and according to the gender is required for better training of the models in order to generalize the outputs for prolific use to target population.

4D printing is an innovative digital manufacturing technology that originated by adding a fourth dimension, i.e., time, to pre-existing 3D technology or additive manufacturing (AM). AM is a fast-growing technology used in many fields, which develops accurate 3D objects based on models designed by computers. Dentistry is one such field in which 3D technology is used for manufacturing objects in periodontics (scaffolds, local drug-delivering agents, augmentation of ridges), implants, prosthodontics (partial and complete dentures, obturators), oral surgery for reconstructing jaw, and orthodontics. Dynamism is a vital property needed for the survival of materials used in the oral cavity since the oral cavity is constantly subjected to various insults. 4D printing technology has overcome the disadvantages of 3D printing technology, i.e., it cannot create dynamic objects. Therefore, constant knowledge of 4D technology is required. 3D printing technology has shortcomings, which are discussed in this review. This review summaries various printing technologies, materials used, stimuli, and potential applications of 4D technology in dentistry.