Eugenio Beltrán-Aguilar

Background: Dental caries remains one of the most prevalent noncommunicable diseases worldwide despite decades of clinical and behavioural interventions. Persistently high prevalence suggests that current approaches are better organised for ongoing disease management than for preventing new disease occurrence. This commentary examines whether an elimination framework could provide a more coherent and outcome‐oriented direction for oral health policy and practice. Methodology: We apply the Dahlem Framework to assess whether dental caries meets established criteria for disease eradication. We then position caries within the classical continuum of control, elimination, and eradication, drawing on current evidence on caries aetiology, fluoride exposure, dietary environments, primary care integration, surveillance, and the social and commercial determinants of oral health. Results: Dental caries does not meet the criteria for eradication. It has no single causal agent, no interruptible transmission cycle, no immunising intervention, and no global surveillance architecture capable of detecting incidence. Eradication is therefore conceptually impossible. Elimination, however, defined as the stable disappearance of dental caries as a public health problem within a defined population, is biologically plausible and operationally achievable. An elimination frame shifts expectations from managing recurrence to preventing occurrence and reframes caries as a system outcome rather than solely an individual behavioural or clinical problem. Conclusion: Caries elimination is not the promise of biological absence, but a realistic and measurable population health objective. Adopting an elimination framework could strengthen policy coherence, surveillance, implementation research, professional education, and accountability, while reorienting oral health systems toward prevention, equity, and sustained population‐level impact.

Background: Assessing dental caries, sealants, and fluorosis is essential for public health surveillance, providing critical data to evaluate national prevention programs. Standard methods performed by dental professionals are often limited by affordability, accessibility, and scalability for both population-level and individualized assessments. Mobile health (mHealth) approaches to concurrently detect caries, sealants, and fluorosis have remained largely unexplored, especially at the population level. Objective: This study leverages mHealth technologies that integrate computer vision using machine learning and deep learning with images captured by smartphone cameras and low-cost intraoral cameras. The primary objective is to develop and validate models for detecting caries lesions, identifying sealants, and quantifying fluorosis severity from standardized dental images, using standardized visual clinical examinations as the reference standard. Methods: The proposed study population will include approximately 1000 adolescents in Colorado, United States, living in communities with naturally elevated fluoride levels in the public water system. Participants will undergo standardized clinical dental examinations and imaging using intraoral cameras and smartphones. Supervised learning models will incorporate reference chart–based color correction, radiomic spatial and textural features, and neural network classifiers. The reference standard will be standardized visual clinical examinations performed by trained and calibrated dental professionals. Two models will be developed and evaluated: one to detect caries lesions and sealants and another to assess fluorosis severity. Model performance will be evaluated against clinical assessments by dental professionals using stratified cross-validation and multiclass performance metrics while minimizing bias and accounting for confounders common to human examiners. Results: A standardized dental examination, an intraoral imaging protocol, and a smartphone imaging protocol are used to assess all 8 permanent molars for caries and sealants, as well as the 6 upper anterior teeth for fluorosis severity. Pilot studies were conducted to test study logistics and calibrate 3 examiners in person, supplemented by debriefings, mobile app training, and a web-based calibration module. The study was funded in September 2022 with supplemental funding awarded in June 2024. The study launched in May 2024, and as of January 2026, data have been collected from approximately 300 participants. Conclusions: The integration of computer vision and mobile device imaging will enable affordable, scalable, population-level assessments for detecting caries and sealants and quantifying fluorosis severity among adolescents. mHealth technologies have been increasingly incorporated into dentistry for both clinical decision support and at-home use. This protocol will further help establish a structured methodological framework for acquiring, processing, and analyzing mobile imaging data for dental health surveillance and epidemiological studies. International Registered Report Identifier (IRRID): DERR1-10.2196/91239

BACKGROUND Assessing dental caries, sealants, and fluorosis is essential for public health surveillance, providing critical data to evaluate national prevention programs. Standard methods performed by dental professionals are often limited by affordability, accessibility, and scalability for both population-level and individualized assessments. Mobile health (mHealth) approaches to concurrently detect caries, sealants, and fluorosis have remained largely unexplored, especially at the population level. OBJECTIVE This study leverages mHealth technologies that integrate computer vision using machine learning and deep learning with images captured by smartphone cameras and low-cost intraoral cameras. The primary objective is to develop and validate models for detecting caries lesions, identifying sealants, and quantifying fluorosis severity from standardized dental images, using standardized visual clinical examinations as the reference standard. METHODS The proposed study population will include approximately 1000 adolescents in Colorado, United States, living in communities with naturally elevated fluoride levels in the public water system. Participants will undergo standardized clinical dental examinations and imaging using intraoral cameras and smartphones. Supervised learning models will incorporate reference chart–based color correction, radiomic spatial and textural features, and neural network classifiers. The reference standard will be standardized visual clinical examinations performed by trained and calibrated dental professionals. Two models will be developed and evaluated: one to detect caries lesions and sealants and another to assess fluorosis severity. Model performance will be evaluated against clinical assessments by dental professionals using stratified cross-validation and multiclass performance metrics while minimizing bias and accounting for confounders common to human examiners. RESULTS A standardized dental examination, an intraoral imaging protocol, and a smartphone imaging protocol are used to assess all 8 permanent molars for caries and sealants, as well as the 6 upper anterior teeth for fluorosis severity. Pilot studies were conducted to test study logistics and calibrate 3 examiners in person, supplemented by debriefings, mobile app training, and a web-based calibration module. The study was funded in September 2022 with supplemental funding awarded in June 2024. The study launched in May 2024, and as of January 2026, data have been collected from approximately 300 participants. CONCLUSIONS The integration of computer vision and mobile device imaging will enable affordable, scalable, population-level assessments for detecting caries and sealants and quantifying fluorosis severity among adolescents. mHealth technologies have been increasingly incorporated into dentistry for both clinical decision support and at-home use. This protocol will further help establish a structured methodological framework for acquiring, processing, and analyzing mobile imaging data for dental health surveillance and epidemiological studies. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/91239

Background: The assessment of epidemiological information on the oral health of children younger than 6 y in the region of the Americas is challenging due to methodological differences. An International Association for Dental Research Regional Development Project supported researchers from 11 Latin American countries in Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Panama, Paraguay, Peru, Uruguay, and Venezuela to train and conduct standardized epidemiological studies of dental caries in children 2-6 prevalence in the 12- to 23-mo group varied between 13% in Venezuela and 48% in Argentina, while the d _5-6 prevalence varied between 0% in Venezuela and 18% in Argentina. In general, the occurrence of more severe clinical presentations increased with age. Conclusions: There was a considerable variation in the prevalence of dental caries in the 11 countries. The wide variation with age indicates the need to report ECC estimates by detection threshold and individual age groups. The ICDAS-adapted protocol is suitable for ECC epidemiological studies. Knowledge Transfer Statement: The results of this study show the high occurrence and variation of early childhood caries (ECC) in Latin American children, which shows the need for more detailed and comprehensive surveillance efforts in this age group. The ICDAS-adapted protocol facilitates data entry and the recording of clinical observations for epidemiological studies in young children by reducing examination time. We have contributed to the standardization of ECC data collection in Latin America by using common ICDAS-adapted criteria.