Early childhood caries (ECC) is the most common chronic childhood disease, with nearly 1.8 billion new cases per year globally. ECC afflicts approximately 55% of low-income and minority US preschool children, resulting in harmful short- and long-term effects on health and quality of life. The current biomedical approach to control the ECC pandemic has had limited success. It primarily focuses on restorative procedures rather than population-wide preventive strategies. Clinical evidence shows that caries is reversible if detected and addressed in its early stages. However, many low-income US children often have poor access to pediatric dental services. In this underserved group, dental caries is often diagnosed at a late stage when extensive restorative treatment is needed. We believe that with more than 85% of lower-income Americans owning a smartphone, mHealth tools hold great promise to achieve patient-driven early detection and risk control of ECC. Our long-term goal is to develop strategies that use mHealth tools to achieve early detection and prevention of ECC at a broad population base. Our previous innovative work has led to a novel prototype of an artificial intelligence (AI) -powered smartphone app, AICaries, to be used by children's parents/caregivers. This AICaries app prototype offers a) AI-powered caries detection using photos of children's teeth taken by the parents' smartphones, b) interactive caries risk assessment, and c) personalized education on reducing children's ECC risk. The preliminary AI- powered caries detection module demonstrated a satisfactory sensitivity and specificity for front teeth caries detection, using 6,895 annotated tooth images from 1,277 photos. We have recently built an archive of > 100,000 high-quality intra-oral photos that is ready to be used for finalizing the development of a reliable automatic detection algorithm. The immediate objectives of the study are - AIM 1: complete the development of AICaries smartphone app, maximize its caries detection performance, and achieve a caries detection sensitivity and specificity that are comparable to trained dental practitioners;
AIM 2 : employ a community-based participatory research strategy to conduct moderated testing and refinement of the app usability, and non-moderated field testing of the app feasibility/acceptability. Our multidisciplinary team is well-positioned for proposal success with needed expertise in computer science, AI imaging recognition, oral health care, mHealth, disparity research, patient education and community engagement. The AICaries app could facilitate early detection of ECC for many underserved US children, who often have poor access to pediatric dental services. Using AICaries, parents can use their regular smartphones to take photo of their children?s teeth and detect ECC aided by AICaries, so that they can actively seek treatment for their children at an early and reversible stage of ECC. Using AICaries, parents can also obtain essential knowledge on reducing their children's caries risk. Data from this R21 will support a R01 clinical trial that evaluates the real-world impact of using this innovative smartphone app on early detection and prevention of ECC among low-income children.
Although largely preventable, early childhood caries (ECC) remains the most common chronic childhood disease, disproportionately afflicts vulnerable parts of the population and has a substantial adverse impact on children, families, and healthcare systems. Our multidisciplinary team is proposing to use an Artificial Intelligence-powered mHealth tool coupled with a community engagement strategy to revolutionize the detection and monitoring of ECC at the patient level, which may pave the way for improving oral heath among low-income children.
