External validation of an artificial intelligence-based method for the detection and classification of molar incisor hypomineralisation in dental photographs.

OBJECTIVES: This ex vivo diagnostic study aimed to externally validate an open-access artificial intelligence (AI)-based model for the detection, classification, localisation and segmentation of enamel/molar incisor hypomineralisation (EH/MIH). METHODS: An independent sample of web images showing teeth with (n = 277) and without (n = 178) EH/MIH was evaluated by a workgroup of dentists whose consensus served as the reference standard. Then, an AI-based model was used for the detection of EH/MIH, followed by automated classification and segmentation of the findings (test method). The accuracy (ACC), sensitivity (SE), specificity (SP) and area under the curve (AUC) were determined. Furthermore, the correctness of EH/MIH lesion localisation and segmentation was evaluated. RESULTS: An overall ACC of 94.3 % was achieved for image-based detection of EH/MIH. Cross-classification of the AI-based class prediction and the reference standard resulted in an agreement of 89.2 % for all diagnostic decisions (n = 594), with an ACC between 91.4 % and 97.8 %. The corresponding SE and SP values ranged from 81.7 % to 92.8 % and 91.9 % to 98.7 %, respectively. The AUC varied between 0.894 and 0.945. Image size had only a limited impact on diagnostic performance. The AI-based model correctly predicted EH/MIH localisation in 97.3 % of cases. For the detected lesions, segmentation was fully correct in 63.4 % of all cases and partially correct in 33.9 %. CONCLUSIONS: This study documented the promising diagnostic performance of an open-access AI tool in the detection and classification of EH/MIH in external images. CLINICAL SIGNIFICANCE: Externally validated AI-based diagnostic methods could facilitate the detection of EH/MIH lesions in dental photographs.

Artificial intelligence and personalized diagnostics in periodontology: A narrative review.

Periodontal diseases pose a significant global health burden, requiring early detection and personalized treatment approaches. Traditional diagnostic approaches in periodontology often rely on a "one size fits all" approach, which may overlook the unique variations in disease progression and response to treatment among individuals. This narrative review explores the role of artificial intelligence (AI) and personalized diagnostics in periodontology, emphasizing the potential for tailored diagnostic strategies to enhance precision medicine in periodontal care. The review begins by elucidating the limitations of conventional diagnostic techniques. Subsequently, it delves into the application of AI models in analyzing diverse data sets, such as clinical records, imaging, and molecular information, and its role in periodontal training. Furthermore, the review also discusses the role of research community and policymakers in integrating personalized diagnostics in periodontal care. Challenges and ethical considerations associated with adopting AI-based personalized diagnostic tools are also explored, emphasizing the need for transparent algorithms, data safety and privacy, ongoing multidisciplinary collaboration, and patient involvement. In conclusion, this narrative review underscores the transformative potential of AI in advancing periodontal diagnostics toward a personalized paradigm, and their integration into clinical practice holds the promise of ushering in a new era of precision medicine for periodontal care.

Artificial intelligence for detecting periapical radiolucencies: A systematic review and meta-analysis.

OBJECTIVES: Dentists' diagnostic accuracy in detecting periapical radiolucency varies considerably. This systematic review and meta-analysis aimed to investigate the accuracy of artificial intelligence (AI) for detecting periapical radiolucency. DATA: Studies reporting diagnostic accuracy and utilizing AI for periapical radiolucency detection, published until November 2023, were eligible for inclusion. Meta-analysis was conducted using the online MetaDTA Tool to calculate pooled sensitivity and specificity. Risk of bias was evaluated using QUADAS-2. SOURCES: A comprehensive search was conducted in PubMed/MEDLINE, ScienceDirect, and Institute of Electrical and Electronics Engineers (IEEE) Xplore databases. Studies reporting diagnostic accuracy and utilizing AI tools for periapical radiolucency detection, published until November 2023, were eligible for inclusion. STUDY SELECTION: We identified 210 articles, of which 24 met the criteria for inclusion in the review. All but one study used one type of convolutional neural network. The body of evidence comes with an overall unclear to high risk of bias and several applicability concerns. Four of the twenty-four studies were included in a meta-analysis. AI showed a pooled sensitivity and specificity of 0.94 (95 % CI = 0.90-0.96) and 0.96 (95 % CI = 0.91-0.98), respectively. CONCLUSIONS: AI demonstrated high specificity and sensitivity for detecting periapical radiolucencies. However, the current landscape suggests a need for diverse study designs beyond traditional diagnostic accuracy studies. Prospective real-life randomized controlled trials using heterogeneous data are needed to demonstrate the true value of AI. CLINICAL SIGNIFICANCE: Artificial intelligence tools seem to have the potential to support detecting periapical radiolucencies on imagery. Notably, nearly all studies did not test fully fledged software systems but measured the mere accuracy of AI models in diagnostic accuracy studies. The true value of currently available AI-based software for lesion detection on both 2D and 3D radiographs remains uncertain.

European Organisation for Caries Research and the European Federation of Conservative Dentistry Consensus Report on Clinical Recommendations for Caries Diagnosis: Paper III - Caries Diagnosis at the Individual Level.

OBJECTIVES: The aim of the present consensus paper was to provide recommendations for clinical practice on the individual etiological and modifying factors to be assessed in the individual diagnosis of caries, and the methods for their assessment, supporting personalized treatment decisions. MATERIAL AND METHODS: The executive councils of the European Organisation for Caries Research (ORCA) and the European Federation of Conservative Dentistry (EFCD) nominated ten experts each to join the expert panel. The steering committee formed three work groups which were asked to provide recommendations on (1) caries detection and diagnostic methods, (2) caries activity assessment, and (3) forming individualized caries diagnoses. The experts responsible for "individualised caries diagnosis" searched and evaluated the relevant literature, drafted this manuscript and made provisional consensus recommendations. These recommendations were discussed and refined during the structured process in the whole work group. Finally, the agreement for each recommendation was determined using an anonymous eDelphi survey. The threshold for approval of recommendations was determined at 70% agreement. RESULTS: Ten recommendations were approved and agreed by the whole expert panel, covering medical history, caries experience, plaque, diet, fluoride, and saliva. While the level of evidence was low, the level of agreement was typically very high, except for one recommendation on salivary flow measurement, where 70% agreed. CONCLUSION: It is recommended that all aspects of caries lesion progression and activity, recent caries experience, medical conditions and medications, plaque, diet, fluoride and saliva should be synthesized to arrive at an individual diagnosis. CLINICAL RELEVANCE: The expert panel merged evidence from existing guidelines and scientific literature with practical considerations and provided recommendations for their use in daily dental practice.

In Vitro Cytotoxic and Inflammatory Response of Gingival Fibroblasts and Oral Mucosal Keratinocytes to 3D Printed Oral Devices.

The purpose of this study was to examine the biocompatibility of 3D printed materials used for additive manufacturing of rigid and flexible oral devices. Oral splints were produced and finished from six printable resins (pairs of rigid/flexible materials: KeySplint Hard [KR], KeySplint Soft [KF], V-Print Splint [VR], V-Print Splint Comfort [VF], NextDent Ortho Rigid [NR], NextDent Ortho Flex [NF]), and two types of PMMA blocks for subtractive manufacturing (Tizian Blank PMMA [TR], Tizian Flex Splint Comfort [TF]) as controls. The specimens were eluted in a cell culture medium for 7d. Human gingival fibroblasts (hGF-1) and human oral mucosal keratinocytes (hOK) were exposed to the eluates for 24 h. Cell viability, glutathione levels, apoptosis, necrosis, the cellular inflammatory response (IL-6 and PGE2 secretion), and cell morphology were assessed. All eluates led to a slight reduction of hGF-1 viability and intracellular glutathione levels. The strongest cytotoxic response of hGF-1 was observed with KF, NF, and NR eluates (p < 0.05 compared to unexposed cells). Viability, caspase-3/7 activity, necrosis levels, and IL-6/PGE2 secretion of hOK were barely affected by the materials. All materials showed an overall acceptable biocompatibility. hOK appeared to be more resilient to noxious agents than hGF-1 in vitro. There is insufficient evidence to generalize that flexible materials are more cytotoxic than rigid materials. From a biological point of view, 3D printing seems to be a viable alternative to milling for producing oral devices.

Effect of Bleaching on Resin-Infiltration-Masked Artificial White Spots In Vitro.

Resin infiltration is an effective method to mask vestibular white spots. If needed, external bleaching is usually recommended before infiltration, whilst in clinical practice, this sequence may not always be feasible. This in vitro study evaluated the effect of bleaching after resin infiltration regarding surface roughness and color using bovine incisors. Unlike for the untreated specimens (control, n = 25), artificial caries lesions were created within the test group (n = 25) using a demineralization solution at 37 °C for five days (pH = 4.95). The lesions were subsequently infiltrated using a resin infiltrant (Icon, DMG, Hamburg, Germany), followed by polishing. Afterwards, all specimens were bleached with a 10% carbamide peroxide gel (Opalescence, Ultradent, South Jordan, UT, USA) for 8 h/day over a ten-day period. Between bleaching treatments, specimens were stored in an opaque container with moistened paper tissues at 37 °C. Surface roughness was measured using a profilometer, and color in the L*a*b* space was assessed spectrophotometrically before and after bleaching. Bleaching increased the L*-values of both infiltrated (mean ± SD; ΔL* = 3.52 ± 1.98) and untreated (control) specimens (ΔL* = 3.53 ± 2.30) without any significant difference between the groups (p = 0.983). Bleaching also induced a significant increase in the mean surface roughness of both infiltrated (p < 0.001) and untreated (p = 0.0134) teeth. In terms of clinical relevance; it can be concluded that bleaching resin-infiltrated enamel is as effective as bleaching sound enamel.

Conquering Class Imbalances in Deep Learning-based Segmentation of Dental Radiographs with Different Loss Functions.

OBJECTIVE: The imbalanced nature of real-world datasets is an ongoing challenge in the field of machine and deep learning. In medicine and in dentistry, most data samples represent patients not affected by pathologies, and on imagery, pathologic image areas are often smaller than healthy ones. Selecting suitable loss functions during deep learning is essential and may help to overcome the resulting imbalance. We assessed six different loss functions for one exemplary task, tooth structure segmentation on bitewing radiographs, for their performance. METHODS: Six different loss functions (Focal Loss, Dice Loss, Tversky Loss and hybrid losses of Cross-Entropy and Dice Loss, Focal and Dice Loss, Focal and Generalized Dice Loss) were compared on a tooth structure segmentation task of 1,625 bitewing radiographs. Training was performed using three different model architectures (U-Net, Linknet, DeepLavbV3+) over a 5-fold cross-validation. Tooth structures consisted of the classes (occurrence in % of samples/captures areas measured on pixel level) enamel (100%/25%), dentin (100%/50%), root canal (100%/10%), filling (81%/8%) and crown (28%/5%). RESULTS: Hybrid loss functions significantly outperformed standalone ones and provided robust results over the different architectures for the classes enamel, dentin, root canal and filling. Specifically, the Dice Focal loss reached high performance to conquer both image level and pixel level class imbalance, respectively. CLINICAL SIGNIFICANCE: In dental use cases it is often important to predict minority classes such as pathologies accurately. Using specific loss function may be an effective strategy to overcome data imbalance when training deep learning models.

ORCA-EFCD consensus report on clinical recommendations for caries diagnosis. Paper II: caries lesion activity and progression assessment.

INTRODUCTION: This consensus paper provides recommendations for oral health professionals on why and how to assess caries activity and progression with special respect to the site of a lesion. METHODS: An expert panel was nominated by the executive councils of the European Organization for Caries Research (ORCA) and the European Federation of Conservative Dentistry (EFCD). The steering committee built three working groups that were asked to provide recommendations on 1) caries detection and diagnostic methods, 2) caries activity and progression assessment and 3) obtain individualized caries diagnoses. The experts of work group 2 phrased and agreed on provisional general and specific recommendations on caries lesion activity and progression, based on a review of the current literature. These recommendations were then discussed and refined in a consensus workshop followed by an anonymous Delphi survey to determine the agreement on each recommendation. RESULTS: The expert panel agreed on general (n=7) and specific recommendations (n=6). The specific recommendations cover coronal caries on pits and fissures, smooth surfaces, proximal surfaces, as well as root caries and secondary caries/ caries adjacent to restorations and sealants (CARS). 3/13 recommendations yielded perfect agreement. CONCLUSION: The most suitable method for lesion activity assessment is the visual-tactile method. No single clinical characteristic is indicative of lesion activity; instead, lesion activity assessment is based on assessing and weighing several clinical signs. The recall intervals for visual and radiographic examination need to be adjusted to the presence of active caries lesions and recent caries progression rates. Modifications should be based on individual patient characteristics.

Artificial intelligence chatbots and large language models in dental education: Worldwide survey of educators.

INTRODUCTION: Interest is growing in the potential of artificial intelligence (AI) chatbots and large language models like OpenAI's ChatGPT and Google's Gemini, particularly in dental education. To explore dental educators' perceptions of AI chatbots and large language models, specifically their potential benefits and challenges for dental education. MATERIALS AND METHODS: A global cross-sectional survey was conducted in May-June 2023 using a 31-item online-questionnaire to assess dental educators' perceptions of AI chatbots like ChatGPT and their influence on dental education. Dental educators, representing diverse backgrounds, were asked about their use of AI, its perceived impact, barriers to using chatbots, and the future role of AI in this field. RESULTS: 428 dental educators (survey views = 1516; response rate = 28%) with a median [25/75th percentiles] age of 45 [37, 56] and 16 [8, 25] years of experience participated, with the majority from the Americas (54%), followed by Europe (26%) and Asia (10%). Thirty-one percent of respondents already use AI tools, with 64% recognising their potential in dental education. Perception of AI's potential impact on dental education varied by region, with Africa (4[4-5]), Asia (4[4-5]), and the Americas (4[3-5]) perceiving more potential than Europe (3[3-4]). Educators stated that AI chatbots could enhance knowledge acquisition (74.3%), research (68.5%), and clinical decision-making (63.6%) but expressed concern about AI's potential to reduce human interaction (53.9%). Dental educators' chief concerns centred around the absence of clear guidelines and training for using AI chatbots. CONCLUSION: A positive yet cautious view towards AI chatbot integration in dental curricula is prevalent, underscoring the need for clear implementation guidelines.

Current Applications of Artificial Intelligence for Pediatric Dentistry: A Systematic Review and Meta-Analysis.

Purpose: To systematically evaluate artificial intelligence applications for diagnostic and treatment planning possibilities in pediatric dentistry. Methods: PubMed®, EMBASE®, Scopus, Web of Science™, IEEE, medRxiv, arXiv, and Google Scholar were searched using specific search queries. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) checklist was used to assess the risk of bias assessment of the included studies. Results: Based on the initial screening, 33 eligible studies were included (among 3,542). Eleven studies appeared to have low bias risk across all QUADAS-2 domains. Most applications focused on early childhood caries diagnosis and prediction, tooth identification, oral health evaluation, and supernumerary tooth identification. Six studies evaluated AI tools for mesiodens or supernumerary tooth identification on radigraphs, four for primary tooth identification and/or numbering, seven studies to detect caries on radiographs, and 12 to predict early childhood caries. For these four tasks, the reported accuracy of AI varied from 60 percent to 99 percent, sensitivity was from 20 percent to 100 percent, specificity was from 49 percent to 100 percent, F1-score was from 60 percent to 97 percent, and the area-under-the-curve varied from 87 percent to 100 percent. Conclusions: The overall body of evidence regarding artificial intelligence applications in pediatric dentistry does not allow for firm conclusions. For a wide range of applications, AI shows promising accuracy. Future studies should focus on a comparison of AI against the standard of care and employ a set of standardized outcomes and metrics to allow comparison across studies.

Accuracy and consistency of chatbots versus clinicians for answering pediatric dentistry questions: A pilot study.

OBJECTIVES: Artificial Intelligence has applications such as Large Language Models (LLMs), which simulate human-like conversations. The potential of LLMs in healthcare is not fully evaluated. This pilot study assessed the accuracy and consistency of chatbots and clinicians in answering common questions in pediatric dentistry. METHODS: Two expert pediatric dentists developed thirty true or false questions involving different aspects of pediatric dentistry. Publicly accessible chatbots (Google Bard, ChatGPT4, ChatGPT 3.5, Llama, Sage, Claude 2 100k, Claude-instant, Claude-instant-100k, and Google Palm) were employed to answer the questions (3 independent new conversations). Three groups of clinicians (general dentists, pediatric specialists, and students; n = 20/group) also answered. Responses were graded by two pediatric dentistry faculty members, along with a third independent pediatric dentist. Resulting accuracies (percentage of correct responses) were compared using analysis of variance (ANOVA), and post-hoc pairwise group comparisons were corrected using Tukey's HSD method. ACronbach's alpha was calculated to determine consistency. RESULTS: Pediatric dentists were significantly more accurate (mean±SD 96.67 %± 4.3 %) than other clinicians and chatbots (p < 0.001). General dentists (88.0 % ± 6.1 %) also demonstrated significantly higher accuracy than chatbots (p < 0.001), followed by students (80.8 %±6.9 %). ChatGPT showed the highest accuracy (78 %±3 %) among chatbots. All chatbots except ChatGPT3.5 showed acceptable consistency (Cronbach alpha>0.7). CLINICAL SIGNIFICANCE: Based on this pilot study, chatbots may be valuable adjuncts for educational purposes and for distributing information to patients. However, they are not yet ready to serve as substitutes for human clinicians in diagnostic decision-making. CONCLUSION: In this pilot study, chatbots showed lower accuracy than dentists. Chatbots may not yet be recommended for clinical pediatric dentistry.

Dental Practice Websites in Germany-How Do They Inform about Fluoridation?

Fluoridation (Fl) is effective in preventing caries; however, it is unclear to what extent its use is counteracted by misinformation on the internet. This study aimed to evaluate the information provided on professional websites of German dental practices regarding fluoridation. A systematic search was performed by two independent examiners, utilizing three search engines, from 10 September 2021 to 11 December 2021. Modified, validated questionnaires (LIDA, DISCERN) were used to evaluate technical and functional aspects, generic quality, and risk of bias. Demographic information and statements about Fl were also collected. The intra- and inter-rater reliability assessments were excellent. Of the 81 websites analyzed, 64 (79%) mentioned Fl, and 31 (38%) indicated it as a primary focus. Most websites met at least 50% of the LIDA (90%) and DISCERN criteria (99%), indicating that the general quality was good. Thirty (37%) of the websites explained the impact of Fl, and forty-five (56%) indicated an opinion (for/against) on Fl. The practice location and the clinical focus were not associated with the overall quality of websites. Only a minority of websites explained the effects of Fl. Taken together, this study highlights that there is a distinct lack of good-quality information on FL.

Comparing accuracy in guided endodontics: dynamic real-time navigation, static guides, and manual approaches for access cavity preparation - an in vitro study using 3D printed teeth.

OBJECTIVES: To assess root canal localization accuracy using a dynamic approach, surgical guides and freehand technique in vitro. MATERIALS AND METHODS: Access cavities were prepared for 4 different 3D printed tooth types by 4 operators (n = 144). Deviations from the planning in angle and bur positioning were compared and operating time as well as tooth substance loss were evaluated (Kruskal-Wallis Test, ANOVA). Operating method, tooth type, and operator effects were analyzed (partial eta-squared statistic). RESULTS: Angle deviation varied significantly between the operating methods (p < .0001): freehand (9.53 ± 6.36°), dynamic (2.82 ± 1.8°) and static navigation (1.12 ± 0.85°). The highest effect size was calculated for operating method (ηP²=0.524), followed by tooth type (0.364), and operator (0.08). Regarding deviation of bur base and tip localization no significant difference was found between the methods. Operating method mainly influenced both parameters (ηP²=0.471, 0.379) with minor effects of tooth type (0.157) and operator. Freehand technique caused most substance loss (p < .001), dynamic navigation least (p < .0001). Operating time was the shortest for freehand followed by static and dynamic navigation. CONCLUSIONS: Guided endodontic access may aid in precise root canal localization and save tooth structure. CLINICAL RELEVANCE: Although guided endodontic access preparation may require more time compared to the freehand technique, the guided navigation is more accurate and saves tooth structure.

ORCA-EFCD consensus report on clinical recommendation for caries diagnosis. Paper I: caries lesion detection and depth assessment.

OBJECTIVES: The aim of the present consensus paper was to provide recommendations for clinical practice considering the use of visual examination, dental radiography and adjunct methods for primary caries detection. MATERIALS AND METHODS: The executive councils of the European Organisation for Caries Research (ORCA) and the European Federation of Conservative Dentistry (EFCD) nominated ten experts each to join the expert panel. The steering committee formed three work groups that were asked to provide recommendations on (1) caries detection and diagnostic methods, (2) caries activity assessment and (3) forming individualised caries diagnoses. The experts responsible for "caries detection and diagnostic methods" searched and evaluated the relevant literature, drafted this manuscript and made provisional consensus recommendations. These recommendations were discussed and refined during the structured process in the whole work group. Finally, the agreement for each recommendation was determined using an anonymous Delphi survey. RESULTS: Recommendations (N = 8) were approved and agreed upon by the whole expert panel: visual examination (N = 3), dental radiography (N = 3) and additional diagnostic methods (N = 2). While the quality of evidence was found to be heterogeneous, all recommendations were agreed upon by the expert panel. CONCLUSION: Visual examination is recommended as the first-choice method for the detection and assessment of caries lesions on accessible surfaces. Intraoral radiography, preferably bitewing, is recommended as an additional method. Adjunct, non-ionising radiation methods might also be useful in certain clinical situations. CLINICAL RELEVANCE: The expert panel merged evidence from the scientific literature with practical considerations and provided recommendations for their use in daily dental practice.

Oral Health Literacy and Tooth Loss and Replacement in Older Adults at a University Dental Clinic in Colombia.

BACKGROUND: Oral health literacy (OHL) is the ability of individuals to obtain, process, and understand oral health information and services, allowing them to make appropriate oral health decisions. The association between OHL and tooth loss and replacement have not been well understood. OBJECTIVES: We aimed to determine the association between OHL and tooth loss and replacement in a Colombia population. METHODS: A cross-sectional study of 384 older adults age 65 to 89 years from Pasto, Colombia was carried out. The number of lost and replaced teeth was assessed intraorally; sociodemographic and prosthetic characteristics were collected, and the Health Literacy in Dentistry questionnaire was used to evaluate OHL. Generalized linear models were estimated to assess associations between independent variables (including OHL) and the number of lost and replaced teeth. KEY RESULTS: There were 224 (58.3%) men and 160 (41.7%) women. The mean (standard deviation [SD]) number of lost and replaced teeth was 27.78 (4.03) and 12.53 (9.89), respectively. One hundred fifty five (40.4%) individuals had full removable dental protheses, 122 (31.8%) partial removable dental protheses, 68 (17.7%) fixed prosthetics, and 36 (9.4%) dental implants. OHL was 33.29 (6.59) and significantly positively associated with the number of replaced teeth (ß = 0.65, 95% confidence interval [CI]: 0.52-0.78, p < .001), but not with lost teeth. CONCLUSIONS: OHL may foster individuals' capabilities to replace lost teeth, although we did not find it associated with reduced tooth loss, likely as tooth loss was highly common in this older population. [HLRP: Health Literacy Research and Practice. 2024;8(1):e21-e28.].

PLAIN LANGUAGE SUMMARY: The association between OHL and tooth loss and replacement has not been well understood. A study of 384 older adults was designed to evaluate the number of lost and replaced teeth and the association with OHL. We found that OHL may foster tooth replacement but was not associated with tooth loss itself.

Terminology of e-Oral Health: Consensus Report of the IADR's e-Oral Health Network Terminology Task Force.

OBJECTIVE: Authors reported multiple definitions of e-oral health and related terms, and used several definitions interchangeably, like mhealth, teledentistry, teleoral medicine and telehealth. The International Association of Dental Research e-Oral Health Network (e-OHN) aimed to establish a consensus on terminology related to digital technologies used in oral healthcare. METHOD: The Crowdsourcing Delphi method used in this study comprised of four main stages. In the first stage, the task force created a list of terms and definitions around digital health technologies based on the literature and established a panel of experts. Inclusion criteria for the panellists were: to be actively involved in either research and/or working in e-oral health fields; and willing to participate in the consensus process. In the second stage, an email-based consultation was organized with the panel of experts to confirm an initial set of terms. In the third stage, consisted of: a) an online meeting where the list of terms was presented and refined; and b) a presentation at the 2022-IADR annual meeting. The fourth stage consisted of two rounds of feedback to solicit experts' opinion about the terminology and group discussion to reach consensus. A Delphi-questionnaire was sent online to all experts to independently assess a) the appropriateness of the terms, and b) the accompanying definitions, and vote on whether they agreed with them. In a second round, each expert received an individualised questionnaire, which presented the expert's own responses from the first round and the panellists' overall response (% agreement/disagreement) to each term. It was decided that 70% or higher agreement among experts on the terms and definitions would represent consensus. RESULTS: The study led to the identification of an initial set of 43 terms. The list of initial terms was refined to a core set of 37 terms. Initially, 34 experts took part in the consensus process about terms and definitions. From them, 27 experts completed the first rounds of consultations, and 15 the final round of consultations. All terms and definitions were confirmed via online voting (i.e., achieving above the agreed 70% threshold), which indicate their agreed recommendation for use in e-oral health research, dental public health, and clinical practice. CONCLUSION: This is the first study in oral health organised to achieve consensus in e-oral health terminology. This terminology is presented as a resource for interested parties. These terms were also conceptualised to suit with the new healthcare ecosystem and the place of e-oral health within it. The universal use of this terminology to label interventions in future research will increase the homogeneity of future studies including systematic reviews.

Psychometric Testing of HeLD-14 in a Colombian Geriatric Population.

Introduction: The objective of this study was to test the validity and reliability of the Colombian version of the Health Literacy in Dentistry (HeLD-14) in older adults. Materials and Methods: A translation and validation study of HeLD-14 was conducted on 384 non-institutionalized older adults attending the Dental Clinic at Universidad Cooperativa from Pasto, Colombia. A cross-cultural adaptation of a multidimensional HeLD-14 was completed, and the psychometric properties of this scale were evaluated through a cross-validation method using an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA). Internal consistency was measured with Cronbach's alpha (α) and Omega's McDonald (É·). The statistical significance was set at P < 0.05. Results: The EFA demonstrated that a single-factor structure with 11 items explained a cumulative 59.86% of the overall variance. The CFA confirmed that goodness of fit indices of this questionnaire had optimal adequateness (χ2S-B = 109.047; χ2S-B/(44) = 2.478, P=0.001; non-normed fit index = 0.901; comparative fit index = 0.908; root mean square error of approximation = 0.079 (90% CI (0.075, 0.083)); standardized root mean residual = 0.080). The coefficients indicated a high internal consistency for the total scale (α = 0.94; É· = 0.96). Conclusion: The developed adaptation of HeLD-14 for the Colombian population, HeLD-Col, is a unidimensional, reliable, and valid instrument to assess oral health literacy in older adults in Colombia.