ARTIFICIAL INTELLIGENCE-ASSISTED CARIES DETECTION WITH DIAGNOCAT ON INTRAORAL RADIOGRAPHS: IS ITS APPLICATION EQUIVALENT IN PEDIATRIC AND ADULT PATIENTS?

Diagnocat is an artificial intelligence platform that analyses dental radiographs and flags signs of caries on intraoral images such as bitewings and periapicals. This article asks whether the system is used the same way in children as in adults. The manufacturer presents a dedicated pediatric solution, and the upload, analysis and reporting workflow is the same for primary and permanent teeth. Children's radiographs differ in important ways, however: thinner enamel and dentine, faster lesion progression, wide contact areas, large pulp chambers, developing tooth germs and mixed dentition, and more motion artefacts and limited image availability because of radiation-dose concerns. The independent validation studies identified were based on general bitewing and periapical datasets and did not focus on children. They report good specificity but only moderate sensitivity, and the AI performs better at ruling caries out than ruling it in. No peer-reviewed accuracy study in primary or mixed dentition was found. The article concludes that Diagnocat is technically applicable to both age groups but is best used as a second reader alongside clinical examination and risk assessment, and that validation studies in children are needed.