Innovative AI System Accurately Predicts Early Childhood Dental Cavities at the Tooth Level

A groundbreaking development in pediatric dental health involves an advanced artificial intelligence (AI) system that can predict the risk of cavities in individual teeth of young children with over 90% accuracy. Researchers from the University of Hong Kong’s Faculty of Dentistry, in collaboration with the Chinese Academy of Sciences and hospitals in Qingdao, have unveiled the world's first tool capable of assessing cavity susceptibility at the precise tooth level based on microbial analysis.

Early childhood caries (ECC) remains the most common chronic disease among children globally, yet how it affects specific teeth has long been a mystery. The team’s study, published in the journal Cell Host & Microbe, analyzed microbial communities in tooth plaque samples from 89 preschoolers, aged 3 to 5, over nearly a year. Through advanced sequencing techniques, including 16S rRNA sequencing and shotgun metagenomics, they identified distinct microbial patterns that precede visible decay.

One of the key discoveries was the existence of a microbial gradient along the mouth, with different bacterial communities naturally inhabiting front teeth (incisors) compared to back teeth (molars). When cavities start to form, these microbial patterns change, with bacteria migrating from their usual locations, signaling early decay.

The researchers developed Spatial-MiC, an AI system that analyzes these microbial patterns and predicts cavity risks for specific teeth. Remarkably, Spatial-MiC can detect existing cavities with 98% accuracy and forecast potential cavities two months before they are clinically visible, enabling preventive action before significant damage occurs.

This innovative approach offers a shift toward personalized dental care, allowing for targeted prevention rather than uniform treatment across all teeth. Given that ECC affects more than 70% of children in some regions, such as China, this technology could dramatically reduce childhood tooth decay, infections, and related developmental issues.

Professor Shi Huang highlighted the significance, stating, "We’ve changed the paradigm from viewing cavities as inevitable to now being able to predict and prevent them at the microbial level, tooth by tooth." The goal is to validate this system in diverse populations and incorporate it into routine dental assessments worldwide, ultimately providing children with healthier, decay-free smiles.

According to Dr. Fang Yang, the study’s lead author, this technology enriches dental health strategies by enabling early, precise intervention, potentially transforming pediatric dentistry and early childhood health management.