A collaborative research team from Hong Kong and China has developed the world’s first artificial intelligence (AI) system capable of predicting early childhood caries risk for individual teeth based on microbial characteristics, achieving an accuracy rate of more than 90 per cent.
The study is published in Cell Host & Microbe.
The research team conducted the most comprehensive analysis to date of tooth-specific microbial communities in young children aged 3–5 years, using an innovative approach that combined cutting-edge 16S rRNA sequencing with shotgun metagenomics for microbial compositional and functional analysis. By tracking 2504 individual tooth plaque samples from 89 preschoolers over nearly a year, they uncovered distinct patterns that foretell dental decay.
The study found that front teeth (incisors) naturally harbor different bacterial communities than back teeth (molars), creating a predictable spatial pattern across the mouth.
This gradient, maintained by factors like saliva flow and tooth anatomy, becomes disrupted when cavities begin to form. The researchers identified specific bacterial shifts that occur well before visible decay, including the migration of incisor-associated microbes to molar sites and vice versa.
The team’s most significant achievement was developing Spatial-MiC, the world’s first AI system that predicts cavity risks in individual teeth based on complex microbial communities. The system analyses these microbial patterns to assess cavity risk.
By combining data from a tooth’s microbial community with information from its neighbours, Spatial-MiC achieved 98 per cent accuracy in detecting existing cavities and 93 per cent accuracy in predicting cavities two months before they became clinically apparent. This represents a major improvement over current whole-mouth assessment methods, which often miss early warning signs.
