With the introduction of rare earth magnets like neodymium-iron-boron (NdFeB), it has become possible to produce small magnets with high forces, necessary for its usage in the field of dentistry, such as for orthodontic tooth movement. The ultimate goal of this project is to establish magnetic force-driven orthodontic treatment as a future treatment modality for comprehensive orthodontic treatment.In order to utilize magnets for orthodontic treatment, we must first understand the characteristics of tooth movement created by magnetic forces. In this study, we aimed to digitally assess the efficacy of magnetic attraction and repulsion forces by means of a 3D digital analysis of movement (distance, direction, angulation and duration) and rotation (yaw, pitch and roll) of the crown and root of teeth in an ex vivo typodont model. We performed space closure and space gain treatment of maxillary central incisors (n = 30) and analyzed the movement and rotation of the teeth and root apex with 3D digital analysis. The results of the typodont model indicated significant differences on amount, speed and rotation of tooth and root movement created by magnetic attraction and repulsion forces.We also mimicked a moderate crowding typodont case and successfully treated it with a combination of attraction and repulsion magnetic forces. The moderate crowding case utilized magnets and a titanium archwire to guide the planned tooth movements and prevent undesired or unexpected movement. Further ex vivo experiments and considerations for biosafety will be necessary to investigate magnet force-driven orthodontics as a future modality of orthodontic treatment.

PurposeOligodontia significantly affects oral function and esthetics. Recognition of skeletal and dental patterns may aid in proper diagnosis and development of appropriate interventions. The aim of this study was to analyze skeletal and dental patterns for pre-adolescent patients with a diagnosis of oligodontia.Patients and methodsThis study included 19 oligodontia patients (age: 9.5±1.3, Hellman’s developmental stage IIIA~IIIB) along with a control group that comprised of 19 participants (age: 9.9±1.6) without any skeletal disharmony or congenitally missing teeth, with an Angle class I relationship and general crowding. Average cephalometric measurements among the oligodontia group were compared to the control group. The correlation between number of congenitally missing teeth (CMT) and each measurement was investigated. Skeletal measurements for both male and female patients in the oligodontia group and the control group were also compared.ResultsNo significant difference between the experimental and the control group was observed with respect to skeletal angular and linear measurements, except the gonial angle. Differences in dental pattern measurements were observed. The oligodontia group had significantly smaller Mo-Ms and Is-Mo than the control group (p<0.01). No correlation was detected between severity of oligodontia (number of CMT) and skeletal measurement except for SNB (R=−0.4). For females with oligodontia, Mo-Ms (eruption of maxillary first molar) and Is-Mo (mesial location of maxillary first molar) significantly differed from females in the control group (p<0.01). In contrast, no differences in Mo-Ms or Is-Mo were detected for male patients when oligodontia and control group were compared.ConclusionAmong pre-adolescent Japanese patients with oligodontia in Hellman’s developmental age IIIA~IIIB, no significant differences in skeletal characteristics were established when compared to the control group. However, tooth position of maxillary first molars indicated smaller vertical descent and mesial shift, which may suggest weak maxillary vertical development.

The accurate diagnosis of individual tooth prognosis has to be determined comprehensively in consideration of the broader treatment plan. The objective of this study was to establish an effective artificial intelligence (AI)-based module for an accurate tooth prognosis decision based on the Harvard School of Dental Medicine (HSDM) comprehensive treatment planning curriculum (CTPC). The tooth prognosis of 2359 teeth from 94 cases was evaluated with 1 to 5 levels (1—Hopeless, 5—Good condition for long term) by two groups (Model-A with 16, and Model-B with 13 examiners) based on 17 clinical determining factors selected from the HSDM-CTPC. Three AI machine-learning methods including gradient boosting classifier, decision tree classifier, and random forest classifier were used to create an algorithm. These three methods were evaluated against the gold standard data determined by consensus of three experienced prosthodontists, and their accuracy was analyzed. The decision tree classifier indicated the highest accuracy at 0.8413 (Model-A) and 0.7523 (Model-B). Accuracy with the gradient boosting classifier and the random forest classifier was 0.6896, 0.6687, and 0.8413, 0.7523, respectively. Overall, the decision tree classifier had the best accuracy among the three methods. The study contributes to the implementation of AI in the decision-making process of tooth prognosis in consideration of the treatment plan.

Cognitive health is subject to decline with increasing numbers of lost teeth which impacts mastication. This study is a descriptive data analysis of the association between masticatory and cognitive conditions using a large database. We obtained the dental and medical records from Japan's universal healthcare system (UHCS) from the national database in 2017. The data from 94% of the Japanese population aged 65 and over is included. It is inclusive of diagnostic codes for various types of cognitive impairment, as well as dental treatment records from 2012 to 2017. The cognitive impairment group was compared to those without a diagnosis of cognitive impairment. Crude odds ratio between loss of mastication with natural teeth (exposure) and cognitive impairments (outcome) were compared. Patients who have lost masticatory function are likely to have cognitive impairment with an odds ratio of 1.89 (p<0.0001) for early elderly (aged 65-75) and 1.33 (p<0.0001) for advanced elderly (over 75). Patients who are edentulous and function with complete dentures are likely to have cognitive impairment with an odds ratio of 2.38 (p<0.0001) and 1.38 (p<0.0001), respectively. The data shows a convincing and significant result of an association between cognitive health and oral health, related to masticatory conditions.

Objectives. Although digital technology has been widely integrated into dental education, there is limited literature investigating the extent of the integration of computer-aided design and computer-aided manufacturing (CAD-CAM) for removable systems in the dental curriculum. The purpose of this study was to assess the current implementation of CAD-CAM complete and partial dentures in predoctoral (PP) and advanced graduate prosthodontic (AGP) education in US dental schools. The study also aimed to identify potential barriers to its implementation in the dental curriculum. Methods. An online survey with 15 questions was created using online survey software. The survey was distributed to the directors of predoctoral prosthodontics in 56 schools and advanced graduate programs of prosthodontics in 52 schools listed in the 2018–19 American Dental Education Association (ADEA) Directory. Results. The percentage of programs (PP and AGP) implementing CAD-CAM complete dentures (CAD-CAM CDs) and CAD-CAM removable partial dentures (CAD-CAM RPDs) in their didactic, preclinical, and clinical curricula was recorded. CAD-CAM CDs are taught in didactic courses in 54.2% of PP and 65.2% of AGP. However, CAD-CAM RPDs are only taught in 37.5% of PP and 47.8% of AGP. Programs are largely limited by a lack of funds, resources, time, and faculty members. Conclusion. While digital technologies have indeed become more prevalent in dental education, many institutions face barriers to implementation. More research must be conducted in order to support the continued incorporation of digital technologies into dental education.