AI system predicts early childhood cavities with high accuracy
At the heart of the discovery is a remarkable anterior-to-posterior microbial gradient in healthy mouths.
An 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%, has been developed by researchers.
The collaborative research team, from the Faculty of Dentistry of the University of Hong Kong (HKU), Chinese Academy of Sciences (CAS-QIBEBT), Qingdao Stomatological Hospital, and Qingdao Women and Children’s Hospital, say it could revolutionise the prevention of childhood tooth decay.
Early childhood caries (ECC) – the world’s most prevalent chronic childhood disease – disproportionately targets specific teeth; a mystery that has until now remained unresolved.
The team 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%. The study is published in Cell Host & Microbe.
The team conducted an analysis of tooth-specific microbial communities in young children aged 3–5 years, using an approach that combined 16S rRNA sequencing with shotgun metagenomics for microbial compositional and functional analysis. By tracking 2,504 individual tooth plaque samples from 89 pre-school children over nearly a year, they uncovered distinct patterns that foretell dental decay.
At the heart of the discovery is a remarkable anterior-to-posterior microbial gradient in healthy mouths. The study found that incisors naturally harbour 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 developed 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% accuracy in detecting existing cavities and 93% 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.
“These findings fundamentally change how we understand tooth decay,” said Professor Shi Huang, Assistant Professor in Microbiology from the Division of Applied Oral Sciences and Community Dental Care at the HKU Faculty of Dentistry.
“We’ve moved from seeing cavities as inevitable to being able to predict and prevent them at the microbial level, tooth by tooth.”
The team envisions a future where the system could be expanded to validate the approach in diverse populations. The ultimate goal is to develop clinical tests that bring the technology into dental offices worldwide.