JMIR Publications

ABSTRACT

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 with images captured by smartphone cameras and low-cost intraoral cameras. The primary objective is to acquire standardized dental images and clinical data and to develop and validate models for detecting caries lesions, identifying sealants, and quantifying fluorosis severity.

Methods:

The proposed study population will include approximately 1,000 adolescents in Colorado, USA, living in communities with naturally elevated fluoride levels in public water systems. 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. 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 trained 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 eight permanent molars for caries and sealants, as well as the six upper anterior teeth for fluorosis severity. The study launched in May 2024, and as of December 2025, data had 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, identifying sealants, and quantifying fluorosis severity among adolescents. As mHealth technologies are increasingly incorporated into dentistry for both clinical decision support and at-home use, this protocol will help establish a structured methodological framework for acquiring, processing, and analyzing mobile imaging data for dental health surveillance and epidemiological studies. Clinical Trial: Not applicable.