Automatic dental age calculation from panoramic radiographs using deep learning: a two-stage approach with object detection and image classification.

BACKGROUND: Dental age is crucial for treatment planning in pediatric and orthodontic dentistry. Dental age calculation methods can be categorized into morphological, biochemical, and radiological methods. Radiological methods are commonly used because they are non-invasive and reproducible. When radiographs are available, dental age can be calculated by evaluating the developmental stage of permanent teeth and converting it into an estimated age using a table, or by measuring the length between some landmarks such as the tooth, root, or pulp, and substituting them into regression formulas. However, these methods heavily depend on manual time-consuming processes. In this study, we proposed a novel and completely automatic dental age calculation method using panoramic radiographs and deep learning techniques. METHODS: Overall, 8,023 panoramic radiographs were used as training data for Scaled-YOLOv4 to detect dental germs and mean average precision were evaluated. In total, 18,485 single-root and 16,313 multi-root dental germ images were used as training data for EfficientNetV2 M to classify the developmental stages of detected dental germs and Top-3 accuracy was evaluated since the adjacent stages of the dental germ looks similar and the many variations of the morphological structure can be observed between developmental stages. Scaled-YOLOv4 and EfficientNetV2 M were trained using cross-validation. We evaluated a single selection, a weighted average, and an expected value to convert the probability of developmental stage classification to dental age. One hundred and fifty-seven panoramic radiographs were used to compare automatic and manual human experts' dental age calculations. RESULTS: Dental germ detection was achieved with a mean average precision of 98.26% and dental germ classifiers for single and multi-root were achieved with a Top-3 accuracy of 98.46% and 98.36%, respectively. The mean absolute errors between the automatic and manual dental age calculations using single selection, weighted average, and expected value were 0.274, 0.261, and 0.396, respectively. The weighted average was better than the other methods and was accurate by less than one developmental stage error. CONCLUSION: Our study demonstrates the feasibility of automatic dental age calculation using panoramic radiographs and a two-stage deep learning approach with a clinically acceptable level of accuracy.

Longevity of dental restorations in Sjogren's disease patients using electronic dental and health record data.

BACKGROUND: Decreased salivary secretion is not only a risk factor for carious lesions in Sjögren's disease (SD) but also an indicator of deterioration of teeth with every restorative replacement. This study determined the longevity of direct dental restorations placed in patients with SD using matched electronic dental record (EDR) and electronic health record (EHR) data. METHODS: We conducted a retrospective cohort study using EDR and EHR data of Indiana University School of Dentistry patients who have a SD diagnosis in their EHR. Treatment history of patients during 15 years with SD (cases) and their matched controls with at least one direct dental restoration were retrieved from the EDR. Descriptive statistics summarized the study population characteristics. Cox regression models with random effects analyzed differences between cases and controls for time to direct restoration failure. Further the model explored the effect of covariates such as age, sex, race, dental insurance, medical insurance, medical diagnosis, medication use, preventive dental visits per year, and the number of tooth surfaces on time to restoration failure. RESULTS: At least one completed direct restoration was present for 102 cases and 42 controls resulting in a cohort of 144 patients' EDR and EHR data. The cases were distributed as 21 positives, 57 negatives, and 24 uncertain cases based on clinical findings. The average age was 56, about 93% were females, 54% were White, 74% had no dental insurance, 61% had public medical insurance, < 1 preventive dental visit per year, 94% used medications and 93% had a medical diagnosis that potentially causes dry mouth within the overall study cohort. About 529 direct dental restorations were present in cases with SD and 140 restorations in corresponding controls. Hazard ratios of 2.99 (1.48-6.03; p = 0.002) and 3.30 (1.49-7.31, p-value: 0.003) showed significantly decreased time to restoration failure among cases and positive for SD cases compared to controls, respectively. Except for the number of tooth surfaces, no other covariates had a significant influence on the survival time. CONCLUSION: Considering the rapid failure of dental restorations, appropriate post-treatment assessment, management, and evaluation should be implemented while planning restorative dental procedures among cases with SD. Since survival time is decreased with an increase in the number of surfaces, guidelines for restorative procedures should be formulated specifically for patients with SD.

Digital surveillance: The interest in mouthwash-related information.

BACKGROUND: The comprehension of the interests of Internet users regarding their health-related searches may reveal the community's demands about oral health. The study aimed to characterize the interests of Google users related to mouthwash in Australia, Brazil, Chile, Japan, Mexico, Russia, the United Kingdom, the United States, Saudi Arabia and South Africa applying the Google Trends. METHODS: This longitudinal retrospective study analysed the mouthwash-related interest of Google users from January 2004 to December 2020. The monthly variation of relative search volume (RSV) and the main queries related were determined using Google Trends. Autoregressive integrated moving average (ARIMA) forecasting models were built to establish the predictive RSV values for mouthwash for additional 12 months. Auto-correlation plots and a general additive model (GAM) were used to diagnose trends and seasonality in RSV curves. In addition, the influence of social isolation related to the outbreak of COVID-19 were analysed. RESULTS: The RSVs curves showed a considerable increase in searches related to mouthwash to AUS, BRA, JAP, MEX, GBR and USA (RSV > 25), while the growth was slight to CHI, KSA, RSA and RUS (RSV < 25) over the years, without influence of monthly seasonality. All countries showed a significant increase in mouthwash interest after the outbreak of COVID-19, except for KSA and RUS. The mouthwash-related searches were associated to specific brands or chemical compositions, treatments, whitening agents, homemade mouthwash and indications for the 'best mouthwash'. CONCLUSIONS: In general, there was an increasing interest of Google users in mouthwash-related topics between 2004 and 2020. In addition, in most countries, there was an expansion in searches during the social isolation of the COVID-19 pandemics.

Natural language processing for clinical notes in dentistry: A systematic review.

OBJECTIVE: To identify and synthesise research on applications of natural language processing (NLP) for information extraction and retrieval from clinical notes in dentistry. MATERIALS AND METHODS: A predefined search strategy was applied in EMBASE, CINAHL and Medline. Studies eligible for inclusion were those that that described, evaluated, or applied NLP to clinical notes containing either human or simulated patient information. Quality of the study design and reporting was independently assessed based on a set of questions derived from relevant tools including CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). A narrative synthesis was conducted to present the results. RESULTS: Of the 17 included studies, 10 developed and evaluated NLP methods and 7 described applications of NLP-based information retrieval methods in dental records. Studies were published between 2015 and 2021, most were missing key details needed for reproducibility, and there was no consistency in design or reporting. The 10 studies developing or evaluating NLP methods used document classification or entity extraction, and 4 compared NLP methods to non-NLP methods. The quality of reporting on NLP studies in dentistry has modestly improved over time. CONCLUSIONS: Study design heterogeneity and incomplete reporting of studies currently limits our ability to synthesise NLP applications in dental records. Standardisation of reporting and improved connections between NLP methods and applied NLP in dentistry may improve how we can make use of clinical notes from dentistry in population health or decision support systems. PROTOCOL REGISTRATION: PROSPERO CRD42021227823.

Infodemiology for oral health and disease: A scoping review.

INTRODUCTION: Increasing affordability, accessibility and penetration of internet services worldwide, have substantially changed the ways of gathering health-related information. This has led to the origin of concept infodemiology that allows the information to be collected and analysed in near real time. Globally, oral diseases affect nearly 3.5 billion people; thus, volume and profile of oral health searches would help in understanding specific community dental needs and formulation of pertinent oral health strategies. AIM: To review the published literature on infodemiological aspects of oral health and disease. METHODOLOGY: This scoping review was conducted in accordance with PRISMA-ScR guidelines. Electronic search engines (Google Scholar) and databases (PubMed, Web of science, Scopus) were searched from 2002 onwards. RESULTS: Thirty-eight articles were included in this review. The infodemiological studies for oral health and disease were mainly used in two domains. Out of 38 articles, 24 accessed the quality of available online information and 15 studied online oral health-related information seeking behaviour. CONCLUSION: The most commonly searched oral diseases were toothache, oral cancer, dental caries, periodontal disease, oral maxillofacial surgical procedures and paediatric oral diseases. Most of the studies belonged to developed countries and Google was the most researched search engine.

A comprehensive artificial intelligence framework for dental diagnosis and charting.

BACKGROUND: The aim of this study was to develop artificial intelligence (AI) guided framework to recognize tooth numbers in panoramic and intraoral radiographs (periapical and bitewing) without prior domain knowledge and arrange the intraoral radiographs into a full mouth series (FMS) arrangement template. This model can be integrated with different diseases diagnosis models, such as periodontitis or caries, to facilitate clinical examinations and diagnoses. METHODS: The framework utilized image segmentation models to generate the masks of bone area, tooth, and cementoenamel junction (CEJ) lines from intraoral radiographs. These masks were used to detect and extract teeth bounding boxes utilizing several image analysis methods. Then, individual teeth were matched with a patient's panoramic images (if available) or tooth repositories for assigning tooth numbers using the multi-scale matching strategy. This framework was tested on 1240 intraoral radiographs different from the training and internal validation cohort to avoid data snooping. Besides, a web interface was designed to generate a report for different dental abnormalities with tooth numbers to evaluate this framework's practicality in clinical settings. RESULTS: The proposed method achieved the following precision and recall via panoramic view: 0.96 and 0.96 (via panoramic view) and 0.87 and 0.87 (via repository match) by handling tooth shape variation and outperforming other state-of-the-art methods. Additionally, the proposed framework could accurately arrange a set of intraoral radiographs into an FMS arrangement template based on positions and tooth numbers with an accuracy of 95% for periapical images and 90% for bitewing images. The accuracy of this framework was also 94% in the images with missing teeth and 89% with restorations. CONCLUSIONS: The proposed tooth numbering model is robust and self-contained and can also be integrated with other dental diagnosis modules, such as alveolar bone assessment and caries detection. This artificial intelligence-based tooth detection and tooth number assignment in dental radiographs will help dentists with enhanced communication, documentation, and treatment planning accurately. In addition, the proposed framework can correctly specify detailed diagnostic information associated with a single tooth without human intervention.

Current state of dental informatics in the field of health information systems: a scoping review.

BACKGROUND: Over the past 50 years, dental informatics has developed significantly in the field of health information systems. Accordingly, several studies have been conducted on standardized clinical coding systems, data capture, and clinical data reuse in dentistry. METHODS: Based on the definition of health information systems, the literature search was divided into three specific sub-searches: "standardized clinical coding systems," "data capture," and "reuse of routine patient care data." PubMed and Web of Science were searched for peer-reviewed articles. The review was conducted following the PRISMA-ScR protocol. RESULTS: A total of 44 articles were identified for inclusion in the review. Of these, 15 were related to "standardized clinical coding systems," 15 to "data capture," and 14 to "reuse of routine patient care data." Articles related to standardized clinical coding systems focused on the design and/or development of proposed systems, on their evaluation and validation, on their adoption in academic settings, and on user perception. Articles related to data capture addressed the issue of data completeness, evaluated user interfaces and workflow integration, and proposed technical solutions. Finally, articles related to reuse of routine patient care data focused on clinical decision support systems centered on patient care, institutional or population-based health monitoring support systems, and clinical research. CONCLUSIONS: While the development of health information systems, and especially standardized clinical coding systems, has led to significant progress in research and quality measures, most reviewed articles were published in the US. Clinical decision support systems that reuse EDR data have been little studied. Likewise, few studies have examined the working environment of dental practitioners or the pedagogical value of using health information systems in dentistry.

Nutritional Assessment of Denture Wearers Using Matched Electronic Dental-Health Record Data.

PURPOSE: To assess the nutritional profile of denture wearers through a retrospective cohort study using nutritional biomarkers from matched electronic dental and health record (EDR-EHR) data. MATERIALS AND METHODS: The case group (denture wearers) included matched EDR-EHR data of patients who received removable partial, complete, and implant-supported prosthodontic treatments between January 1, 2010 and December 31, 2018, study time. The control (nondenture wearers) group did not have recorded denture treatments and included patient records within 1 year of the denture index date (first date of case patients' receiving complete or partial denture) of the matching cases. The qualified patients' EDR were matched with their EHR based on the availability of laboratory reports within 2 years of receiving the dentures (index date). Nutritional biomarkers were selected from laboratory reports for complete blood count, comprehensive and basic metabolic profile, lipid, and thyroid panels. Summary statistics were performed, and general linear mixed effect models were used to evaluate the rate of change over time (slope) of nutritional biomarkers before and after the index date. Likelihood ratio tests were performed to determine the differences between dentures and controls. RESULTS: The final cohort included 10,481 matched EDR-EHR data with 3,519 denture wearers and 6,962 controls that contained laboratory results within the study time. The denture wearers' mean age was 57 ±10 years and the control group was 56 ±10 years with 55% females in both groups. Pre-post analysis among denture wearers revealed decreased serum albumin (p = 0.002), calcium (p = 0.039), creatinine (p < 0.001) during the post-index time. Hemoglobin (Hb) was higher pre-index, and was decreasing during the time period but did not change post-index (p < 0.001). Among denture wearers, completely edentulous patients had a significant decrease in serum albumin, creatinine, blood urea nitrogen (BUN), but increased estimated glomerular filtration rate (eGFR). In partially edentulous patients, total cholesterol decreased (p = 0.018) and TSH (p = 0.004), BUN (p < 0.001) increased post-index. Patients edentulous in either upper or lower arch had decreased BUN and eGFR during post-index. Compared to controls, denture wearers showed decreased serum albumin and protein (p = 0.008), serum calcium (p = 0.001), and controls showed increased Hb (p = 0.035) during post-index. CONCLUSIONS: The study results indicate nutritional biomarker variations among denture wearers suggesting a risk for undernutrition and the potential of using selected nutritional biomarkers to monitor nutritional profile.

Standardization of three-dimensional pose of cylindrical implants from intraoral radiographs: a preliminary study.

BACKGROUND: To introduce a theoretical solution to a posteriori describe the pose of a cylindrical dental fixture as appearing on radiographs; to experimentally validate the method described. METHODS: The pose of a conventional dental implant was described by a triplet of angles (phi-pitch, theta-roll, and psi-yaw) which was calculated throughout vector analysis. Radiographic- and simulated-image obtained with an algorithm were compared to test effectiveness, reproducibility, and accuracy of the method. The length of the dental implant as appearing on the simulated image was calculated by the trigonometric function and then compared with real length as it appeared on a two-dimensional radiograph. RESULTS: Twenty radiographs were analyzed for the present in silico and retrospective study. Among 40 fittings, 37 resulted as resolved with residuals ≤ 1 mm. Similar results were obtained for radiographic and simulated implants with absolute errors of - 1.1° ± 3.9° for phi; - 0.9° ± 4.1° for theta; 0° ± 1.1° for psi. The real and simulated length of the implants appeared to be heavily correlated. Linear dependence was verified by the results of the robust linear regression: 0.9757 (slope), + 0.1344 mm (intercept), and an adjusted coefficient of determination of 0.9054. CONCLUSIONS: The method allowed clinicians to calculate, a posteriori, a single real triplet of angles (phi, theta, psi) by analyzing a two-dimensional radiograph and to identify cases where standardization of repeated intraoral radiographies was not achieved. The a posteriori standardization of two-dimensional radiographs could allowed the clinicians to minimize the patient's exposure to ionizing radiations for the measurement of marginal bone levels around dental implants.

ESTABLISHING A QUALITY IMPROVEMENT CULTURE WITHIN A LARGE INTEGRATED MEDICAL-DENTAL HEALTH SYSTEM WITH A POPULATION BASED FOCUS.

OBJECTIVES: Quality improvement strategies have been an integral part of healthcare to attain improved care delivery and effective health outcomes. The dental quality initiative improvement (DQII) presented in this manuscript represents a case study of successful implementation of a quality improvement culture within a large integrated-medical-dental health system serving a largely rural population. METHODS: The key elements of DQII included steering committee establishment, definition or dental quality measures and development/implementation of a dental quality analytics dashboard (DQAD) that provides relevant data on dental quality measures. Qualitative metrics were applied to look at the improvement in performance for the various measures relative to quality benchmarks. RESULTS: DQII facilitated improved oversight of care continuity and provider performance surrounding quality measures at granular and/or institutional level. Improvement associated with care delivery performance relative to benchmarks was observed. CONCLUSIONS: DQII further advanced the quality improvement culture prevalent in our learning healthcare environment with its focus on value-based care delivery. DQII initiative and establishment of DQAD provided ability to track performance in operational care delivery for dental providers in a clinical setting in real time.

Developing a new intelligent system for the diagnosis of oral medicine with case-based reasoning approach.

OBJECTIVE: Since the clinical manifestations of many oral diseases can be quite similar despite the wide variety in etiology and pathology, the differential diagnosis of oral diseases is a complex and challenging process. Intelligent system for differential diagnosis of oral medicine using the artificial intelligence (AI) capabilities helps specialists in achieving differential diagnosis in a wide range of oral diseases. MATERIALS AND METHODS: First, the essential data elements to design and develop an intelligent system were identified in a cross-sectional descriptive study. The case-based reasoning method was selected to design and implement the system, which consists of three stages: collect the clinical data, construct the cases database, and case-based reasoning cycle. The problem is solved by CBR method in a cycle consisting of four main stages of retrieval, reuse, review, and retention. The evaluation process was conducted in a pilot-based way through the evaluation of the system's performance in the clinical setting and also using the usability assessment questionnaire. RESULTS: The output of the present project is a web-based intelligent information system, which is developed using the Visual Studio 2015 software. The database of this system is the Microsoft SQL Server version 2012, which has been programmed based on Net framework (version 4.5 or higher) using Visual Basic language. The results of the system evaluation by specialists in clinical settings showed that the system's diagnosis power in different aspects of the disease is influenced by their prevalence and incidence. CONCLUSIONS: System development using the artificial intelligence capabilities and through the clinical data analysis has potential to help specialists to determine the best diagnostic strategy to achieve a differential diagnosis of a wide range of oral diseases. The results of evaluation present the potential of the system to improve the quality and efficiency of patient care.

Developing and Testing Electronic Health Record-Derived Caries Indices.

Caries indices, the basis of epidemiologic caries measures, are not easily obtained in clinical settings. This study's objective was to design, test, and validate an automated program (Valid Electronic Health Record Dental Caries Indices Calculator Tool [VERDICT]) to calculate caries indices from an electronic health record (EHR). Synthetic use case scenarios and actual patient cases of primary, mixed, and permanent dentition, including decayed, missing, and filled teeth (DMFT/dmft) and tooth surfaces (DMFS/dmfs) were entered into the EHR. VERDICT measures were compared to a previously validated clinical electronic data capture (EDC) system and statistical program to calculate caries indices. Four university clinician-researchers abstracted EHR caries exam data for 45 synthetic use cases into the EDC and post-processed with SAS software creating a gold standard to compare the -VERDICT-derived caries indices. Then, 2 senior researchers abstracted EHR caries exam data and calculated caries indices for 24 patients, allowing further comparisons to VERDICT indices. Agreement statistics were computed among abstractors, and discrepancies were resolved by consensus. Agreement statistics between the 2 final-phase abstractors and the VERDICT measures showed extremely high concordance: Lin's concordance coefficients (LCCs) >0.99 for dmfs, dmft, DS, ds, DT, dt, ms, mt, FS, fs, FT, and ft; LCCs >0.95 for DMFS and DMFT; and LCCs of 0.92-0.93 for MS and MT. Caries indices, essential to developing primary health outcome measures for research, can be reliably derived from an EHR using VERDICT. Using these indices will enable population oral health management approaches and inform quality improvement efforts.

Economic Evaluation of Teledentistry in Cleft Lip and Palate Patients.

OBJECTIVE: To assess the use of Teledentistry (TD) in delivering specialist dental services at the Royal Children's Hospital (RCH) for rural and regional patients and to conduct an economic evaluation by building a decision model to estimate the costs and effectiveness of Teledental consultations compared with standard consultations at the RCH. METHODS: A model-based analysis was conducted to determine the potential costs of implementing TD at the RCH. The outcome measure was timely consultations (whether the patient presented within an appropriate time according to the recommended schedule). Dental records at the RCH of those who presented for orthodontic or pediatric dental consultations were assessed. A cost-effectiveness analysis (CEA), comparing TD with the traditional method of consultation, was conducted. One-way sensitivity analysis was performed to test the robustness of the results. Results and Materials: A total of 367 TD appropriate consultations were identified, of which 241 were timely (65.7%). The mean cost of a RCH consultation was A$431.29, with the mean TD consult costing A$294.35. This represents a cost saving of A$136.95 per appointment. The CEA found TD to be a dominant option, with cost savings of A$3,160.81 for every additional timely consult. The model indicated that 36.7 days of clinic time may be freed up at the RCH to treat other patients and expand capacity. These results were robust when performing one-way sensitivity analysis. CONCLUSION: When taking a societal perspective, the implementation of TD is likely to be a cost-effective alternative compared with the standard practice of face-to-face consultation at the RCH.

Differential diagnosis of jaw pain using informatics technology.

This study aimed to deduce evidence-based clinical clues that differentiate temporomandibular disorders (TMD)-mimicking conditions from genuine TMD by text mining using natural language processing (NLP) and recursive partitioning. We compared the medical records of 29 patients diagnosed with TMD-mimicking conditions and 290 patients diagnosed with genuine TMD. Chief complaints and medical histories were preprocessed via NLP to compare the frequency of word usage. In addition, recursive partitioning was used to deduce the optimal size of mouth opening, which could differentiate TMD-mimicking from genuine TMD groups. The prevalence of TMD-mimicking conditions was more evenly distributed across all age groups and showed a nearly equal gender ratio, which was significantly different from genuine TMD. TMD-mimicking conditions were caused by inflammation, infection, hereditary disease and neoplasm. Patients with TMD-mimicking conditions frequently used "mouth opening limitation" (P < .001), but less commonly used words such as "noise" (P < .001) and "temporomandibular joint" (P < .001) than patients with genuine TMD. A diagnostic classification tree on the basis of recursive partitioning suggested that 12.0 mm of comfortable mouth opening and 26.5 mm of maximum mouth opening were deduced as the most optimal mouth-opening cutoff sizes. When the combined analyses were performed based on both the text mining and clinical examination data, the predictive performance of the model was 96.6% with 69.0% sensitivity and 99.3% specificity in predicting TMD-mimicking conditions. In conclusion, this study showed that AI technology-based methods could be applied in the field of differential diagnosis of orofacial pain disorders.

Perceptions and attitudes toward performing risk assessment for periodontal disease: a focus group exploration.

BACKGROUND: Currently, many risk assessment tools are available for clinicians to assess a patient's periodontal disease risk. Numerous studies demonstrate the potential of these tools to promote preventive management and reduce morbidity due to periodontal disease. Despite these promising results, solo and small group dental practices, where most people receive care, have not adopted risk assessment tools widely, primarily due to lack of studies in these settings. The objective of this study was to explore the knowledge, attitudes, and beliefs of dental providers in these settings toward risk-based care through focus groups. METHODS: We conducted six focus group sessions with 52 dentists and dental hygienists practicing in solo and small group practices in Pittsburgh, PA and New York City (NYC), NY. An experienced moderator and a note-taker conducted the six sessions, each including 8-10 participants and lasting approximately 90 min. All sessions were audio-recorded and transcribed verbatim. Two researchers coded the focus group transcripts. Using a thematic analysis approach, they reviewed the coding results to identify important themes and selected representative excerpts that best described each theme. RESULTS: Providers strongly believed identifying risk factors could predict periodontal disease and use this information to change their patients' behavior. A successful risk assessment tool could assist them in educating and changing their patient's behaviors to adopt a healthy lifestyle, thus enabling them to play a major role in their patients' overall health. However, to achieve this goal, it is essential to educate all dental providers and not just dentists on performing risk assessment and translating the results into actionable recommendations for patients. According to study participants, the research community has focused more on translating research findings into a risk assessment tool, and less on how clinicians would use these tools during patient encounters and if it affects a patients' risk or outcome. CONCLUSIONS: Dental practitioners were open to performing risk assessment as routine care and playing a bigger role in their patients' overall health. Recommendations to overcome major barriers included educating dental providers at all levels, conducting more research about their adoption and use in real-world settings and developing appropriate reimbursement models.

Update on Electronic Dental Record and Clinical Computing Adoption Among Dental Practices in the United States.

This study sought to re-characterize trends and factors affecting electronic dental record (EDR) and technologies adoption by dental practices and the impact of the Health Information Technology for Economic and Clinical Health (HITECH) act on adoption rates through 2012. A 39-question survey was disseminated nationally over 3 months using a novel, statistically-modeled approach informed by early response rates to achieve a predetermined sample. EDR adoption rate for clinical support was 52%. Adoption rates were higher among: (1) younger dentists; (2) dentists ≤ 15 years in practice; (3) females; and (4) group practices. Top barriers to adoption were EDR cost/expense, cost-benefit ratio, electronic format conversion, and poor EDR usability. Awareness of the Federal HITECH incentive program was low. The rate of chairside computer implementation was 72%. Adoption of EDR in dental offices in the United States was higher in 2012 than electronic health record adoption rates in medical offices and was not driven by the HITECH program. Patient portal adoption among dental practices in the United States remained low.

Assessing the performance of Bing Chat artificial intelligence: Dental exams, clinical guidelines, and patients' frequent questions.

OBJECTIVES: Bing Chat is a large language model artificial intelligence (AI) with online search and text generating capabilities. This study assessed its performance within the scope of dentistry in: (a) tackling exam questions for dental students, (ii) providing guidelines for dental practitioners, and (iii) answering patients' frequently asked questions. We discuss the potential of clinical tutoring, common patient communication and impact on academia. METHODS: With the aim of assessing AI's performance in dental exams, Bing Chat was presented with 532 multiple-choice questions and awarded scores based on its answers. In evaluating guidelines for clinicians, a further set of 15 questions, each with 2 follow-up questions on clinical protocols, was presented to the AI. The answers were assessed by 4 reviewers using electronic visual analog scale. In evaluating answers to patients' frequently asked questions, another list of 15 common questions was included in the session, with respective outputs assessed. RESULTS: Bing Chat correctly answered 383 out of 532 multiple-choice questions in dental exam part, achieving a score of 71.99 %. As for outlining clinical protocols for practitioners, the overall assessment score was 81.05 %. In answering patients' frequently asked questions, Bing Chat achieved an overall mean score of 83.8 %. The assessments demonstrated low inter-rater reliability. CONCLUSIONS: The overall performance of Bing Chat was above the regularly adopted passing scores, particularly in answering patient's frequently asked questions. The generated content may have biased sources. These results suggest the importance of raising clinicians' awareness of AI's benefits and risks, as well as timely adaptations of dental education curricula, and safeguarding its use in dentistry and healthcare in general. CLINICAL SIGNIFICANCE: Bing Chat AI performed above the passing threshold in three categories, and thus demonstrated potential for educational assistance, clinical tutoring, and answering patients' questions. We recommend popularizing its benefits and risks among students and clinicians, while maintaining awareness of possible false information.

Evaluating and improving the usability of a mHealth platform to assess postoperative dental pain.

Objectives: The use of interactive mobile health (mHealth) applications to monitor patient-reported postoperative pain outcomes is an emerging area in dentistry that requires further exploration. This study aimed to evaluate and improve the usability of an existing mHealth application. Materials and methods: The usability of the application was assessed iteratively using a 3-phase approach, including a rapid cognitive walkthrough (Phase I), lab-based usability testing (Phase II), and in situ pilot testing (Phase III). The study team conducted Phase I, while providers and patients participated in Phase II and III. Results: The rapid cognitive walkthrough identified 23 potential issues that could negatively impact user experience, with the majority classified as system issues. The lab-based usability testing yielded 141 usability issues.; 43% encountered by patients and 57% by dentists. Usability problems encountered during pilot testing included undelivered messages due to mobile phone carrier and service-related issues, errors in patients' phone number data entry, and problems in provider training. Discussion: Through collaborative and iterative work with the vendor, usability issues were addressed before launching a trial to assess its efficacy. Conclusion: The usability of the mHealth application for postoperative dental pain was remarkably improved by the iterative analysis and interdisciplinary collaboration.

Survival analysis of posterior composite restorations in National Dental PBRN general dentistry practices.

OBJECTIVE: Quantify the survival of posterior composite restorations (PCR) placed during the study period in permanent teeth in United States (US) general dental community practices and factors predictive of that survival. METHODS: A retrospective cohort study was conducted utilizing de-identified electronic dental record (EDR) data of patients who received a PCR in 99 general dentistry practices in the National Dental Practice-Based Research Network (Network). The final analyzed data set included 700,885 PCRs from 200,988 patients. Descriptive statistics and Kaplan Meier (product limit) estimator were performed to estimate the survival rate (defined as the PCR not receiving any subsequent treatment) after the first PCR was observed in the EDR during the study time. The Cox proportional hazards model was done to account for patient- and tooth-specific covariates. RESULTS: The overall median survival time was 13.3 years. The annual failure rates were 4.5-5.8 % for years 1-5; 5.3-5.7 %, 4.9-5.5 %, and 3.3-5.2 % for years 6-10, 11-15, and 16-20, respectively. The failure descriptions recorded for < 7 % failures were mostly caries (54 %) and broken or fractured tooth/restorations (23 %). The following variables significantly predicted PCR survival: number of surfaces that comprised the PCR; having at least one interproximal surface; tooth type; type of prior treatment received on the tooth; Network region; patient age and sex. Based on the magnitude of the multivariable estimates, no single factor predominated. CONCLUSIONS: This study of Network practices geographically distributed across the US observed PCR survival rates and predictive factors comparable to studies done in academic settings and outside the US. CLINICAL SIGNIFICANCE: Specific baseline factors significantly predict the survival of PCRs done in US community dental practices.

Quantifying error introduced by iterative closest point image registration.

OBJECTIVES: The aim of this paper was to quantify the analysis error introduced by iterative closest point (ICP) image registration. We also investigated whether a subsequent subtraction process can reduce process error. METHODS: We tested metrology and two 3D inspection software using calibration standards at 0.39 µm, and 2.64 µm and mathematically perfect defects (softgauges) at 2 and 20 µm, on free form surfaces of increasing complexity and area, both with and without registration. Errors were calculated in percentage relative to the size of the defect being measured. Data were analysed in GraphPad Prism 9, normal and two-way ANOVA with post-hoc Tukey's was applied. Significance was inferred at p < 0.05. RESULTS: Using ICP registration introduced errors from 0 % to 15.63 % of the defect size depending on the surface complexity and size of the defect. Significant differences were observed in analysis measurements between metrology and 3D inspection software and within different 3D inspection software, however, one did not show clear superiority over another. Even in the absence of registration, defects at 0.39 µm, and 2.64 µm produced substantial measurement error (13.39-77.50 % of defect size) when using 3D inspection software. Adding an additional data subtraction process reduced registration error to negligible levels (<1 % independent of surface complexity or area). CONCLUSIONS: Commercial 3D inspection software introduces error during direct measurements below 3 µm. When using an ICP registration, errors over 15 % of the defect size can be introduced regardless of the accuracy of adjacent registration surfaces. Analysis output between software are not consistently repeatable or comparable and do not utilise ISO standards. Subtracting the datasets and analysing the residual difference reduced error to negligible levels. CLINICAL SIGNIFICANCE: This paper quantifies the significant errors and inconsistencies introduced during the registration process even when 3D datasets are true and precise. This may impact on research diagnostics and clinical performance. An additional data processing step of scan subtraction can reduce this error but increases computational complexity.