Performance of Generative Artificial Intelligence in Dental Licensing Examinations.

OBJECTIVES: Generative artificial intelligence (GenAI), including large language models (LLMs), has vast potential applications in health care and education. However, it is unclear how proficient LLMs are in interpreting written input and providing accurate answers in dentistry. This study aims to investigate the accuracy of GenAI in answering questions from dental licensing examinations. METHODS: A total of 1461 multiple-choice questions from question books for the US and the UK dental licensing examinations were input into 2 versions of ChatGPT 3.5 and 4.0. The passing rates of the US and UK dental examinations were 75.0% and 50.0%, respectively. The performance of the 2 versions of GenAI in individual examinations and dental subjects was analysed and compared. RESULTS: ChatGPT 3.5 correctly answered 68.3% (n = 509) and 43.3% (n = 296) of questions from the US and UK dental licensing examinations, respectively. The scores for ChatGPT 4.0 were 80.7% (n = 601) and 62.7% (n = 429), respectively. ChatGPT 4.0 passed both written dental licensing examinations, whilst ChatGPT 3.5 failed. ChatGPT 4.0 answered 327 more questions correctly and 102 incorrectly compared to ChatGPT 3.5 when comparing the 2 versions. CONCLUSIONS: The newer version of GenAI has shown good proficiency in answering multiple-choice questions from dental licensing examinations. Whilst the more recent version of GenAI generally performed better, this observation may not hold true in all scenarios, and further improvements are necessary. The use of GenAI in dentistry will have significant implications for dentist-patient communication and the training of dental professionals.

Denture Plaque Biofilm Visual Assessment Methods: A Systematic Review.

Denture plaque, a biofilm that develops on denture surfaces, could contribute to many oral and systemic afflictions. Hence, a quantitative assessment of denture plaque is important to evaluate the denture hygiene of denture wearers, particularly to prevent plaque biofilm-associated diseases. The aim of this systematic review, therefore, was to review and summarize the visual denture hygiene assessment methods using denture plaque indices and with planimetries published in the literature. English language studies published up to March 2022 in four electronic databases, PubMed, Medline, Embase, and Cochrane Library, were searched, followed by a manual search of Google Scholar by two assessors. The review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) whenever possible. Details of the visual assessment methods, including the types of denture assessed, its materials and its surfaces, as well as the use of a disclosing agent, were the main outcomes. Of 492 screened studies, 74 were included per the inclusion and exclusion criteria. Of these, 60 studies utilized various denture plaque indices while 18 used planimetries. 43 out of 60 studies with indices and 17 out of 18 studies with planimetries used disclosing agents for visual evaluation of plaque. A total of 21 indices were described in the included studies, of which seven graded a divided denture surface, while the remainder graded entire denture surface. Of the 18 planimetric assessments, one study quantified squares of the disclosed plaques on denture images, 16 studies quantified such pixels with computer programs, and a single study quantified points, pixels, and contour of plaque areas. In summary, denture plaque indices appear to be popular in denture plaque assessment due to their simplicity. Computerized planimetric assessment, though more time-consuming, provides a more accurate assessment of plaque load as it is less prone to subjectivity and assessor errors.

Which clinical and laboratory procedures should be used to fabricate digital complete dentures? A systematic review.

STATEMENT OF PROBLEM: Digital workflows for digital complete denture fabrication have a variety of clinical and laboratory procedures, but their outcomes and associated complications are currently unknown. PURPOSE: The purpose of this systematic review was to evaluate the clinical and laboratory procedures for digital complete dentures, their outcomes, and associated complications. MATERIAL AND METHODS: Electronic literature searches were conducted on PubMed/Medline, Embase, and Web of Science for studies published from January 2000 to September 2022 and screened by 2 independent reviewers. Information on digital complete denture procedures, materials, their outcomes, and associated complications was extracted. RESULTS: Of 266 screened studies, 39 studies were included. While 26 assessed definitive complete dentures, 7 studies assessed denture bases, 2 assessed trial dentures, and 4 assessed the digital images only. Twenty-four studies used border molded impression technique, 3 studies used a facebow record, and 7 studies used gothic arch tracing. Only 13 studies performed trial denture placement. Twenty-one studies used milling, and 17 studies used 3D printing for denture fabrication. One study reported that the retention of maxillary denture bases fabricated from a border-molded impression (14.5 to 16.1 N) was statistically higher than the retention of those fabricated from intraoral scanning (6.2 to 6.6 N). The maximum occlusal force of digital complete denture wearers was similar across different fabrication procedures. When compared with the conventional workflow, digital complete dentures required statistically shorter clinical time with 205 to 233 minutes saved. Up to 37.5% of participants reported loss of retention and up to 31.3% required a denture remake. In general, ≥1 extra visit and 1 to 4 unscheduled follow-up visits were needed. The outcomes for patient satisfaction and oral health-related quality of life were similar between conventional, milled, and 3D-printed complete dentures. CONCLUSIONS: Making a border-molded impression is still preferred for better retention, and trial denture placement is still recommended to optimize the fabrication of definitive digital complete dentures.

A Systematic Review of the Use of mHealth in Oral Health Education among Older Adults.

Oral diseases are largely preventable. However, as the number of older adults is expected to increase, along with the high cost and various barriers to seeking continuous professional care, a sustainable approach is needed to assist older adults in maintaining their oral health. Mobile health (mHealth) technologies may facilitate oral disease prevention and management through oral health education. This review aims to provide an overview of existing evidence on using mHealth to promote oral health through education among older adults. A literature search was performed across five electronic databases. A total of five studies were identified, which provided low to moderate evidence to support using mHealth among older adults. The selected studies showed that mHealth could improve oral health management, oral health behavior, and oral health knowledge among older adults. However, more quality studies regarding using mHealth technologies in oral health management, oral health behavior, and oral health knowledge among older adults are needed.

Accuracy of Artificial Intelligence-Based Photographic Detection of Gingivitis.

OBJECTIVES: Gingivitis is one of the most prevalent plaque-initiated dental diseases globally. It is challenging to maintain satisfactory plaque control without continuous professional advice. Artificial intelligence may be used to provide automated visual plaque control advice based on intraoral photographs. METHODS: Frontal view intraoral photographs fulfilling selection criteria were collected. Along the gingival margin, the gingival conditions of individual sites were labelled as healthy, diseased, or questionable. Photographs were randomly assigned as training or validation datasets. Training datasets were input into a novel artificial intelligence system and its accuracy in detection of gingivitis including sensitivity, specificity, and mean intersection-over-union were analysed using validation dataset. The accuracy was reported according to STARD-2015 statement. RESULTS: A total of 567 intraoral photographs were collected and labelled, of which 80% were used for training and 20% for validation. Regarding training datasets, there were total 113,745,208 pixels with 9,270,413; 5,711,027; and 4,596,612 pixels were labelled as healthy, diseased, and questionable respectively. Regarding validation datasets, there were 28,319,607 pixels with 1,732,031; 1,866,104; and 1,116,493 pixels were labelled as healthy, diseased, and questionable, respectively. AI correctly predicted 1,114,623 healthy and 1,183,718 diseased pixels with sensitivity of 0.92 and specificity of 0.94. The mean intersection-over-union of the system was 0.60 and above the commonly accepted threshold of 0.50. CONCLUSIONS: Artificial intelligence could identify specific sites with and without gingival inflammation, with high sensitivity and high specificity that are on par with visual examination by human dentist. This system may be used for monitoring of the effectiveness of patients' plaque control.

Artificial intelligence-designed single molar dental prostheses: A protocol of prospective experimental study.

BACKGROUND: Dental prostheses, which aim to replace missing teeth and to restore patients' appearance and oral functions, should be biomimetic and thus adopt the occlusal morphology and three-dimensional (3D) position of healthy natural teeth. Since the teeth of an individual subject are controlled by the same set of genes (genotype) and are exposed to mostly identical oral environment (phenotype), the occlusal morphology and 3D position of teeth of an individual patient are inter-related. It is hypothesized that artificial intelligence (AI) can automate the design of single-tooth dental prostheses after learning the features of the remaining dentition. MATERIALS AND METHODS: This article describes the protocol of a prospective experimental study, which aims to train and to validate the AI system for design of single molar dental prostheses. Maxillary and mandibular dentate teeth models will be collected and digitized from at least 250 volunteers. The (original) digitized maxillary teeth models will be duplicated and processed by removal of right maxillary first molars (FDI tooth 16). Teeth models will be randomly divided into training and validation sets. At least 200 training sets of the original and the processed digitalized teeth models will be input into 3D Generative Adversarial Network (GAN) for training. Among the validation sets, tooth 16 will be generated by AI on 50 processed models and the morphology and 3D position of AI-generated tooth will be compared to that of the natural tooth in the original maxillary teeth model. The use of different GAN algorithms and the need of antagonist mandibular teeth model will be investigated. Results will be reported following the CONSORT-AI.

Measuring the retention of removable mandibular prostheses by a standardized model: A technical report.

Three cases of mandibular implant overdentures (IODs) were measured for retention by a developed model. A nylon thread was tied through a hole at the midline of the IOD and seated it in the patient's mouth properly. Briefly, a facebow was set on the patient, with its arms firmly held by an assistant. The other end of the thread was tied into a loop, and the thread was inserted through a hole at the Camper's line of the facebow and hooked onto a portable force gauge. The thread was then pulled in parallel with the arms of the facebow by the force gauge until the prosthesis dislodged while the patient opened his mouth. Denture retention was measured five times, and the mean was calculated. Appropriate analysis for validity and reliability of the model was performed, and statistical results showed that it was valid and reliable for measuring the retention of removable mandibular prostheses.

The patient general satisfaction of mandibular single-implant overdentures and conventional complete dentures: Study protocol for a randomized crossover trial.

BACKGROUND: Mandibular overdentures retained by a single implant placed in the midline of edentulous mandible have been reported to be more comfortable and function better than complete dentures. Although single-implant overdentures are still more costly than conventional complete dentures, there are a few studies which investigated whether mandibular single-implant overdentures are superior to complete dentures when patient general satisfaction is compared. The aim of this study is to assess patient general satisfaction with mandibular single-implant overdentures and complete dentures. METHODS: This study is a randomized crossover trial to compare mandibular single-implant overdentures and complete dentures in edentulous individuals. Participant recruitment is ongoing at the time of this submission. Twenty-two participants will be recruited. New mandibular complete dentures will be fabricated. A single implant will be placed in the midline of the edentulous mandible. The mucosal surface of the complete denture around the implant will be relieved for 3 months. The participants will then be randomly allocated into 2 groups according to the order of the interventions; group 1 will receive single-implant overdentures first and will wear them for 2 months, followed by complete dentures for 2 months. Group 2 will receive the same treatments in a reverse order. After experiencing the 2 interventions, the participants will choose one of the mandibular prostheses, and yearly follow-up visits are planned for 5 years. The primary outcome of this trial is patient ratings of general satisfaction on 100 mm visual analog scales. Assessments of the prostheses and oral health-related quality of life will also be recorded as patient-reported outcomes. The secondary outcomes are cost and time for treatment. Masticatory efficiency and cognitive capacity will also be recorded. Furthermore, qualitative research will be performed to investigate the factors associated with success of these mandibular denture types. Clinical outcomes, such as implant survival rate, marginal bone loss, and prosthodontic complications, will also be recorded. DISCUSSION: The results of this randomized crossover trial will clarify whether mandibular single implants and overdentures for edentulous individuals provide better patient general satisfaction when compared to conventional complete dentures. TRIAL REGISTRATION: This clinical trial was registered at the University Hospital Medical Information Network (UMIN) Center (UMIN000017883).