Self-monitoring of Oral Health Using Smartphone Selfie Powered by Artificial Intelligence: Implications for Preventive Dentistry.

PURPOSE: With the increasing use of artificial intelligence (AI) in dentistry, it is feasible to self-monitor oral health using Oral Health AI Advisors (OHAI Advisors). This technological advancement offers the potential for early detection of oral diseases and facilitates early prevention. This systematic review aimed to evaluate the effectiveness of OHAI Advisors as a tool in preventive dentistry for the general population. MATERIALS AND METHODS: Standardised searches were performed and screened across four electronic databases. The primary outcomes were changes in clinical and behavioural measures, and evidence was synthesised. The quality of the included studies was assessed. RESULTS: The initial search identified 1639 articles, 64 full texts were reviewed, and four studies were included in the analyses. Qualitative synthesis revealed that short-term use of OHAI Advisors, for up to 6 months, statistically significantly reduced plaque and gingival index scores. Combining OHAI Advisors with verbal counseling enhanced their effectiveness. No studies investigated effects on oral health awareness, behavioural changes, or adherence to regular practice. The risk of bias in the included studies was moderate to low. CONCLUSION: OHAI Advisors appear to be effective for short-term oral hygiene maintenance. Further research is necessary to determine the preventive capability, focusing on assessing long-term outcomes on oral health and any changes in oral health behaviour.

PV Panel Model Parameter Estimation by Using Particle Swarm Optimization and Artificial Neural Network.

Photovoltaic (PV) panels are one of the popular green energy resources and PV panel parameter estimations are one of the popular research topics in PV panel technology. The PV panel parameters could be used for PV panel health monitoring and fault diagnosis. Recently, a PV panel parameters estimation method based in neural network and numerical current predictor methods has been developed. However, in order to further improve the estimation accuracies, a new approach of PV panel parameter estimation is proposed in this paper. The output current and voltage dynamic responses of a PV panel are measured, and the time series of the I-V vectors will be used as input to an artificial neural network (ANN)-based PV model parameter range classifier (MPRC). The MPRC is trained using an I-V dataset with large variations in PV model parameters. The results of MPRC are used to preset the initial particles' population for a particle swarm optimization (PSO) algorithm. The PSO algorithm is used to estimate the PV panel parameters and the results could be used for PV panel health monitoring and the derivation of maximum power point tracking (MMPT). Simulations results based on an experimental I-V dataset and an I-V dataset generated by simulation show that the proposed algorithms can achieve up to 3.5% accuracy and the speed of convergence was significantly improved as compared to a purely PSO approach.

Frontiers in Three-Dimensional Surface Imaging Systems for 3D Face Acquisition in Craniofacial Research and Practice: An Updated Literature Review.

Digitalizing all aspects of dental care is a contemporary approach to ensuring the best possible clinical outcomes. Ongoing advancements in 3D face acquisition have been driven by continuous research on craniofacial structures and treatment effects. An array of 3D surface-imaging systems are currently available for generating photorealistic 3D facial images. However, choosing a purpose-specific system is challenging for clinicians due to variations in accuracy, reliability, resolution, and portability. Therefore, this review aims to provide clinicians and researchers with an overview of currently used or potential 3D surface imaging technologies and systems for 3D face acquisition in craniofacial research and daily practice. Through a comprehensive literature search, 71 articles meeting the inclusion criteria were included in the qualitative analysis, investigating the hardware, software, and operational aspects of these systems. The review offers updated information on 3D surface imaging technologies and systems to guide clinicians in selecting an optimal 3D face acquisition system. While some of these systems have already been implemented in clinical settings, others hold promise. Furthermore, driven by technological advances, novel devices will become cost-effective and portable, and will also enable accurate quantitative assessments, rapid treatment simulations, and improved outcomes.

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.

Can smartphones be used for routine dental clinical application? A validation study for using smartphone-generated 3D facial images.

OBJECTIVES: To compare the accuracy of smartphone-generated three-dimensional (3D) facial images to that of direct anthropometry (DA) and 3dMD with the aim of assessing the validity and reliability of smartphone-generated 3D facial images for routine clinical applications. MATERIALS AND METHODS: Twenty-five anthropometric soft-tissue facial landmarks were labelled manually on 22 orthognathic surgery patients (11 males and 11 females; mean age 26.2 ± 5.3 years). For each labelled face, two imaging operations were performed using two different surface imaging systems: 3dMDface and Bellus3D FaceApp. Next, 42 inter-landmark facial measurements amongst the identified facial landmarks were measured directly on each labelled face and also digitally on 3D facial images. The measurements obtained from smartphone-generated 3D facial images (SGI) were statistically compared with those from DA and 3dMD. RESULTS: SGI had slightly higher measurement values than DA and 3dMD, but there was no statistically significant difference between the mean values of inter-landmark measures across the three methods. Clinically acceptable differences (≤3 mm or ≤5°) were observed for 67 % and 74 % of measurements with good agreement between DA and SGI, and 3dMD and SGI, respectively. An overall small systematic bias of ± 0.2 mm was observed between the three methods. Furthermore, the mean absolute difference between DA and SGI methods was highest for linear (1.41 ± 0.33 mm) as well as angular measurements (3.07 ± 0.73°). CONCLUSIONS: SGI demonstrated fair trueness compared to DA and 3dMD. The central region and flat areas of the face in SGI are more accurate. Despite this, SGI have limited clinical application, and the panfacial accuracy of the SGI would be more desirable from a clinical application standpoint. CLINICAL SIGNIFICANCE: The usage of SGI in clinical practice for region-specific macro-proportional facial assessment involving central and flat regions of the face or for patient education purposes, which does not require accuracy within 3 mm and 5° can be considered.

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.

Three-dimensional assessment of facial asymmetry in class III subjects, part 2: evaluating asymmetry index and asymmetry scores.

OBJECTIVES: To evaluate the outcomes of corrective surgical treatment for craniofacial asymmetry using four different methods with the aim of developing the best technique for craniofacial asymmetry assessment. MATERIALS AND METHODS: CBCT images of twenty-one class III subjects with surgically corrected craniofacial asymmetry and twenty-one matched controls were analyzed. Twenty-seven hard tissue landmarks were used to quantify asymmetry using the following methodologies: the asymmetry index (AI), asymmetry scores based on the clinically derived midline (CM), Procrustes analysis (PA), and modified Procrustes analysis (MPA). RESULTS: Modified Procrustes analysis successfully identified pre-operative asymmetry and revealed severe asymmetry at the mandibular regions compared to controls, which was comparable to the asymmetry index and clinically derived midline methods, while Procrustes analysis masked the asymmetric characteristics. Likewise, when comparing the post-surgical outcomes, modified Procrustes analysis not only efficiently determined the changes evidencing decrease in facial asymmetry but also revealed significant residual asymmetry in the mandible, which was congruent with the asymmetry index and clinically derived midline methods but contradictory to the results shown by Procrustes analysis. CONCLUSIONS: In terms of quantifying cranio-facial asymmetry, modified Procrustes analysis has evidenced to produce promising results that were comparable to the asymmetry index and the clinically derived midline, making it a more viable option for craniofacial asymmetry assessment. CLINICAL RELEVANCE: Modified Procrustes analysis is proficient in evaluating cranio-facial asymmetry with more valid clinical representation and has potential applications in assessing asymmetry in a wide spectrum of patients, including syndromic patients.

PV Panel Model Parameter Estimation by Using Neural Network.

Photovoltaic (PV) panels have been widely used as one of the solutions for green energy sources. Performance monitoring, fault diagnosis, and Control of Operation at Maximum Power Point (MPP) of PV panels became one of the popular research topics in the past. Model parameters could reflect the health conditions of a PV panel, and model parameter estimation can be applied to PV panel fault diagnosis. In this paper, we will propose a new algorithm for PV panel model parameters estimation by using a Neural Network (ANN) with a Numerical Current Prediction (NCP) layer. Output voltage and current signals (VI) after load perturbation are observed. An ANN is trained to estimate the PV panel model parameters, which is then fined tuned by the NCP to improve the accuracy to about 6%. During the testing stage, VI signals are input into the proposed ANN-NCP system. PV panel model parameters can then be estimated by the proposed algorithms, and the estimated model parameters can be then used for fault detection, health monitoring, and tracking operating points for MPP conditions.

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.

Accuracy of artificial intelligence-designed single-molar dental prostheses: A feasibility study.

STATEMENT OF PROBLEM: Computer-aided design and computer-aided manufacturing (CAD-CAM) technology has greatly improved the efficiency of the fabrication of dental prostheses. However, the design process (CAD stage) is still time-consuming and labor intensive. PURPOSE: The purpose of this feasibility study was to investigate the accuracy of a novel artificial intelligence (AI) system in designing biomimetic single-molar dental prostheses by comparing and matching them to the natural molar teeth. MATERIAL AND METHODS: A total of 169 maxillary casts were obtained from healthy dentate participants. The casts were digitized, duplicated, and processed with the removal of the maxillary right first molar. A total of 159 pairs of original and processed casts were input into the Generative Adversarial Networks (GANs) for training. In validation, 10 sets of processed casts were input into the AI system, and 10 AI-designed teeth were generated through backpropagation. Individual AI-designed teeth were then superimposed onto each of the 10 original teeth, and the morphological differences in mean Hausdorff distance were measured. True reconstruction was defined as correct matching between the AI-designed and original teeth with the smallest mean Hausdorff distance. The ratio of true reconstruction was calculated as the Intersection-over-Union. The reconstruction performance of the AI system was determined by the Hausdorff distance and Intersection-over-Union. RESULTS: Data of validation showed that the mean Hausdorff distance ranged from 0.441 to 0.752 mm and the Intersection-over-Union of the system was 0.600 (60%). CONCLUSIONS: This study demonstrated the feasibility of AI in designing single-molar dental prostheses. With further training and optimization of algorithms, the accuracy of biomimetic AI-designed dental prostheses could be further enhanced.

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.

Three-dimensional assessment of facial asymmetry in Class III subjects. Part 1: a retrospective study evaluating postsurgical outcomes.

OBJECTIVE: The present study aimed to determine the site and severity of maxillomandibular asymmetry before and after orthognathic surgery in asymmetric patients. MATERIALS AND METHODS: Presurgery and postsurgery cone beam computed tomography (CBCT) data of 21 facial asymmetry patients (7 males and 14 females, mean age: 23.0 ± 3.36 years) with soft tissue chin deviation ≥ 3 mm who had undergone bimaxillary surgery were evaluated. Seven midline and twenty bilateral hard tissue landmarks were identified for the evaluation of facial asymmetry and outcomes were assessed against age- and gender-matched control subjects. RESULTS: In the asymmetry group, bilateral landmarks exhibited significant deviation in the mandible and midface regions. Before surgery, asymmetry was more severe at the mandibular midline and sites close to it, in the asymmetry group. Bimaxillary surgery proved to be highly effective, with a significant correction of the menton to a clinically normal value (2.90 mm, p < 0.001). After surgery, significant residual asymmetry was observed at the mental foramen (p = 0.001) in the R-L direction. Moreover, significant asymmetry persisted at the sigmoid notch (p = 0.001) in the S-I direction. CONCLUSIONS: Mandibular midline landmarks and chin peripheral regions contribute significantly to overall facial asymmetry characteristics. Despite significant correction after bimaxillary surgery, asymmetry persisted at several sites, thereby requiring secondary correction. Comprehensive 3D presurgical planning is central for asymmetry correction in a single surgery. CLINICAL RELEVANCE: The present study specifies the location of residual asymmetry sites and advocates the correction of those sites during initial surgery.

Volumetric analysis of mucous retention cysts in the maxillary sinus: A retrospective study using cone-beam computed tomography.

PURPOSE: The aim of this study was to evaluate the volumetric characteristics of mucous retention cysts (MRCs) in the maxillary sinus and to analyze potential associations of MRCs with dentoalveolar pathologies. MATERIALS AND METHODS: Cone-beam computed tomography (CBCT) scans exhibiting bilateral maxillary sinuses that were acquired from January 2016 to February 2019 were initially screened. A total of 227 scans (454 sinuses) that fulfilled the inclusion criteria were included. The presence, location, and volumetric characteristics of the diagnosed MRCs were evaluated on CBCT images using the 3D-Slicer software platform. The presence of MRCs was correlated with potential influencing factors including age, sex, and dentoalveolar pathology. For MRCs located on the sinus floor, factors with a potential impact on the volume, surface, and diameter were analyzed. RESULTS: An MRC was present in 130 (28.6%) of the 454 sinuses. Most MRCs were located on the sinus walls and floor. The mean MRC volume, surface, and diameter were 551.21±1368.04 mm3, 228.09±437.56 mm2, and 9.63±5.40 mm, respectively. Significantly more sinuses with associated endodontically treated teeth/periapical lesions were diagnosed with an MRC located on the sinus floor. For MRCs located on the sinus floor, endodontic status exhibited a significant association with increased volume, surface, and diameter. CONCLUSION: Periapical lesions might be a contributing factor associated with the presence and volume of MRCs located on the sinus floor. The 3D-Slicer software platform was found to be a useful tool for clinicians to analyze the size of MRCs before surgical interventions such as sinus floor elevation procedures.

Anthropometric accuracy of three-dimensional average faces compared to conventional facial measurements.

This study aimed to evaluate and compare the accuracy of average faces constructed by different methods. Original three-dimensional facial images of 26 adults in Chinese ethnicity were imported into Di3DView and MorphAnalyser for image processing. Six average faces (Ave_D15, Ave_D24, Ave_MG15, Ave_MG24, Ave_MO15, Ave_MO24) were constructed using "surface-based registration" method with different number of landmarks and template meshes. Topographic analysis was performed, and the accuracy of six average faces was assessed by linear and angular parameters in correspondence with arithmetic means calculated from individual original images. Among the six average faces constructed by the two systems, Ave_MG15 had the highest accuracy in comparison with the conventional method, while Ave_D15 had the least accuracy. Other average faces were comparable regarding the number of discrepant parameters with clinical significance. However, marginal and non-registered areas were the most inaccurate regions using Di3DView. For MorphAnalyser, the type of template mesh had an effect on the accuracy of the final 3D average face, but additional landmarks did not improve the accuracy. This study highlights the importance of validating software packages and determining the degree of accuracy, as well as the variables which may affect the result.