This invention proposes a dynamic early warning method for childhood dental caries risk based on
multimodal data fusion, relating to the field of
image processing technology. The method includes the following steps: Step 1, acquiring
oral examination images,
saliva biochemical test indicators, and behavioral
record data of the target child; Step 2, constructing a dual-
branch Kolmogorov-Arnold network comprising
tooth position branches, global branches, and a fusion layer. The
tooth position branches receive
tooth position-level topological feature vectors, the global branches receive child-level global feature vectors, and the fusion layer outputs a predicted dental caries risk value; Step 3, dividing the tooth position samples in the calibration subset into positive and negative calibration groups according to their actual labels, and determining the conformal prediction thresholds for positive and negative groups respectively. This invention achieves a complete
closed loop from
multimodal data acquisition to dynamic hierarchical early warning, providing reliable
technical support for the early identification and precise intervention of childhood dental caries.