Objective: This scoping review aims to determine the applications of Artificial Intelligence (AI) that are extensively employed in the field of Orthodontics, to evaluate its benefits, and to discuss its potential implications in this speciality. Recent decades have witnessed enormous changes in our profession. The arrival of new and more aesthetic options in orthodontic treatment, the transition to a fully digital workflow, the emergence of temporary anchorage devices and new imaging methods all provide both patients and professionals with a new focus in orthodontic care.Materials and methods: This review was performed following the Preferred Reporting Items for Systematic reviews and Meta‐Analyses extension for Scoping Reviews (PRISMA‐ScR) guidelines. The electronic literature search was performed through MEDLINE/PubMed, Scopus, Web of Science, Cochrane and IEEE Xplore databases with a 11‐year time restriction: January 2010 till March 2021. No additional manual searches were performed.Results: The electronic literature search initially returned 311 records, and 115 after removing duplicate references. Finally, the application of the inclusion criteria resulted in 17 eligible publications in the qualitative synthesis review.Conclusion: The analysed studies demonstrated that Convolution Neural Networks can be used for the automatic detection of anatomical reference points on radiological images. In the growth and development research area, the Cervical Vertebral Maturation stage can be determined using an Artificial Neural Network model and obtain the same results as expert human observers. AI technology can also improve the diagnostic accuracy for orthodontic treatments, thereby helping the orthodontist work more accurately and efficiently.

The use of artificial intelligence in orthodontics is emerging as a tool for localizing cephalometric points in two-dimensional X-rays. AI systems are being evaluated for their accuracy and efficiency compared to conventional methods performed by professionals. The main objective of this study is to identify the artificial intelligence algorithms that yield the best results for cephalometric landmark localization, along with their learning system. A literature search was conducted across PubMed-MEDLINE, Cochrane, Scopus, IEEE Xplore, and Web of Science. Observational and experimental studies from 2013 to 2023 assessing the detection of at least 13 cephalometric landmarks in two-dimensional radiographs were included. Studies requiring advanced computer engineering knowledge or involving patients with anomalies, syndromes, or orthodontic appliances, were excluded. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Newcastle–Ottawa Scale (NOS) tools. Of 385 references, 13 studies met the inclusion criteria (1 diagnostic accuracy study and 12 retrospective cohorts). Six were high-risk, and seven were low-risk. Convolutional neural networks (CNN)-based AI algorithms showed point localization accuracy ranging from 64.3 to 97.3%, with a mean error of 1.04 mm ± 0.89 to 3.40 mm ± 1.57, within the clinical range of 2 mm. YOLOv3 demonstrated improvements over its earlier version. CNN have proven to be the most effective AI system for detecting cephalometric points in radiographic images. Although CNN-based algorithms generate results very quickly and reproducibly, they still do not achieve the accuracy of orthodontists.

Purpose: This study aimed to examine health-related quality of life (HRQoL) in head and neck cancer patients at 1 and 5 years after successful treatment of their tumors, and to explore the usefulness of 2 instruments for assessing the need of dental care services. Methods: This cross-sectional pilot study included 20 adult patients with head and neck cancer who completed the Functional Assessment of Cancer Therapy-Head and Neck (FACT H&N) Symptom Index and the European Organization for Research and Treatment of Cancer Quality of Life Head and Neck Module (EORTC QLQ-H&N43) after 1 and 5 years of treatment. Results: Mean (standard deviation, SD) scores of the FACT H&N Symptom Index were higher (better HRQoL) at 5 years than at 1 year (24.1 [4.4] vs. 21.1 [6.4]; p = 0.236). Only three of the ten items of FACT H&N (swallow, pain in mouth/throat or neck, and solid foods) evaluated oral health. In the EORTC QLQ-H&N43 questionnaire, scores were lower at 5 years (better HRQoL) in almost all multi- and single-item symptoms. This questionnaire includes four multi-item scales (pain in the mouth, social eating, swallowing, and problems with teeth) measuring dental and orthodontic needs. Conclusion: HRQoL in patients with head and neck cancer improved with the length of follow-up. The EORTC QLQ-H&N43 has more items addressing oral health compared to the FACT H&N Symptom Index and may be more adequate to assess the need of dental therapy in clinical practice.

For orthodontists, the position of the incisors is a key factor in setting treatment goals. Achieving maximum stability requires that they be positioned in the medullary portion of the alveolar bone, balanced with the lingual and labial musculature. Incorrect orthodontic movements can result in root resorption, dehiscences, or even fenestrations. A systematic review of the bibliography was conducted in accordance with PRISMA recommendations. Searches were made in PubMed, Scopus, and Cochrane Library databases, using the same search terms in each, with no limitation on publication data, up to July 1st, 2024. The review accepted articles in any language. Randomized controlled trials, case-control studies, and cohort studies were included, both retrospective and prospective. Systematic reviews, meta-analyses, case reports, case series, literature reviews, and editorials were excluded. The quality of the articles was determined according to the CONSORT criteria. The initial database search identified 167 articles: 89 in PubMed, 74 in Scopus, and 4 in the Cochrane Library. Of these, 75 were duplicates, leaving 92. After applying the inclusion criteria, a total of 8 articles were included in this systematic review. This systematic review highlights the significant relationship between alveolar bone thickness and incisor inclination as assessed by CBCT. The bone thickness varies regionally, with the maxilla generally having thicker palatal bone and the mandible having thinner labial bone. CBCT is indispensable for evaluating cases involving severe skeletal discrepancies, preexisting bone defects, or planned movements beyond the alveolar limits, while traditional methods may suffice for less complex cases. Standardizing methodologies and conducting longitudinal studies with diverse populations are crucial for improving clinical guidelines and ensuring safe, effective treatment planning.

This in vitro study evaluated the shear bond strength (SBS) and adhesive remnant index (ARI) of orthodontic molar tubes bonded using conventional, hydrophilic, and self-etch adhesives under dry and saliva-contaminated conditions, while also assessing the impact of shear force direction. Extracted molars were bonded with Transbond XT™ (T), Transbond MIP™ (M), or Scotchbond Universal™ (S) under dry or saliva-contaminated conditions. Debonding was performed at 90° or 45°, introducing a clinically relevant but underexplored variable in orthodontic bond-strength testing. ARI scores were assessed via stereomicroscopy and visual inspection. Statistical tests (Kruskal–Wallis and Mann–Whitney) showed no significant SBS differences among adhesives under identical conditions (p > 0.05). However, all adhesives exhibited significantly reduced SBS under saliva contamination (p < 0.001; T: 5.4 vs. 4.1 MPa; M: 5.7 vs. 3.6 MPa; S: 5.5 vs. 4.5 MPa). In dry conditions, SBS was significantly higher with 45° debonding (p < 0.05). Under contamination, SBS varied by ARI score (p = 0.05), with ARI 0 specimens showing higher SBS than ARI 3. These findings confirm that moisture reduces bond strength across adhesive types, while 45° force application enhances SBS under dry conditions. ARI score variability under contamination may reflect complex failure modes.