Caries Detection and Classification in Photographs Using an Artificial Intelligence-Based Model-An External Validation Study.

Objective: This ex vivo diagnostic study aimed to externally validate a freely accessible AI-based model for caries detection, classification, localisation and segmentation using an independent image dataset. It was hypothesised that there would be no difference in diagnostic performance compared to previously published internal validation data.

Methods: For the independent dataset, 718 dental images representing different stages of carious ( = 535) and noncarious teeth ( = 183) were retrieved from the internet.

Validation of an Artificial Intelligence-Based Model for Early Childhood Caries Detection in Dental Photographs.

: Early childhood caries (ECC) is a widespread and severe oral health problem that potentially affects the general health of children. Visual-tactile examination remains the diagnostic method of choice to diagnose ECC, although visual examination could be automated by artificial intelligence (AI) tools in the future. The aim of this study was the external validation of a recently published and freely accessible AI-based model for detecting ECC and classifying carious lesions in dental photographs.

External validation of an artificial intelligence-based method for the detection and classification of molar incisor hypomineralisation in dental photographs.

Objectives: This ex vivo diagnostic study aimed to externally validate an open-access artificial intelligence (AI)-based model for the detection, classification, localisation and segmentation of enamel/molar incisor hypomineralisation (EH/MIH).

Methods: An independent sample of web images showing teeth with (n = 277) and without (n = 178) EH/MIH was evaluated by a workgroup of dentists whose consensus served as the reference standard. Then, an AI-based model was used for the detection of EH/MIH, followed by automated classification and segmentation of the findings (test method).