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

Elisabeth Frenkel¹, Julia Neumayr¹, Julia Schwarzmaier¹

¹Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University of Munich, 80336 Munich, Germany.

Diagnostics (Basel, Switzerland)

|October 25, 2024

Summary

This summary is machine-generated.

This study validated an AI model for detecting and classifying dental caries using 718 images. The AI model demonstrated strong diagnostic performance, showing its potential for real-world dental applications.

Keywords:

artificial intelligence deep learning dental caries diagnosis validation study

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Area of Science:

Dentistry
Artificial Intelligence
Medical Imaging

Background:

Dental caries detection and classification are crucial for timely treatment.
Existing AI models require external validation on independent datasets.
AI offers potential for objective and efficient dental diagnostics.

Purpose of the Study:

To externally validate a freely accessible AI-based model for caries detection, classification, localization, and segmentation.
To assess the diagnostic performance of the AI model on an independent dataset.
To compare the AI model's performance against a dental team's evaluation.

Main Methods:

Utilized an independent dataset of 718 dental images (535 carious, 183 non-carious).
Evaluated images using both a dental team (reference standard) and the AI model (test method).
Calculated diagnostic performance metrics: accuracy (ACC), sensitivity (SE), specificity (SP), and area under the curve (AUC).

Main Results:

Achieved 92.0% overall accuracy for caries detection.
Classification performance showed ACC (85.5-95.6%), SE (42.9-93.3%), SP (82.1-99.4%), and AUC (0.702-0.909).
Demonstrated 97.0% accuracy in localization and successful segmentation in 97.0% of cases.

Conclusions:

The AI-based model exhibited promising diagnostic performance for caries detection and classification on an independent dataset.
External validation supports the AI model's potential utility in dental diagnostics.
Further research is recommended to explore AI model validity, reliability, and practicality across diverse data sources and patient groups.