The Validation of an Artificial Intelligence-Based Software for the Detection and Numbering of Primary Teeth on Panoramic Radiographs

Abstract

Background: Dental radiographs play a crucial role in diagnosis and treatment planning. With the rise in digital imaging, there is growing interest in leveraging artificial intelligence (AI) to support clinical decision-making. AI technologies can enhance diagnostic accuracy by automating tasks like identifying and locating dental structures. The aim of the current study was to assess and validate the accuracy of an AI-powered application in the detection and numbering of primary teeth on panoramic radiographs. Methods: This study examined 598 archived panoramic radiographs of subjects aged 4-14 years old. Images with poor diagnostic quality were excluded. Three experienced clinicians independently assessed each image to establish the ground truth for primary teeth identification. The same radiographs were then evaluated using EM2AI, an AI-based diagnostic software for the automatic detection and numbering of primary teeth. The AI's performance was assessed by comparing its output to the ground truth using sensitivity, specificity, predictive values, accuracy, and the Kappa coefficient. Results: EM2AI demonstrated high overall performance in detecting and numbering primary teeth in mixed dentition, with an accuracy of 0.98, a sensitivity of 0.97, a specificity of 0.99, and a Kappa coefficient of 0.96. Detection accuracy for individual teeth ranged from 0.96 to 0.99. The highest sensitivity (0.99) was observed in detecting upper right canines and primary molars, while the lowest sensitivity (0.79-0.85) occurred in detecting lower incisors and the upper left first molar. Conclusions: The AI module demonstrated high accuracy in the automatic detection of primary teeth presence and numbering in panoramic images, with performance metrics exceeding 90%. With further validation, such systems could support automated dental charting, improve electronic dental records, and aid clinical decision-making.

Keywords: artificial intelligence; panoramic radiography; primary teeth; sensitivity and specificity.

Figure 1

Panoramic radiograph of a 6-year-old subject showing mixed dentition analyzed using EM2AI for primary teeth detection.

Figure 2

Percentage of reasons for AI detection errors in primary teeth identification among the representative sample.

Figure 3

Poor quality image affecting AI performance in the detection of maxillary primary teeth.

Figure 4

Overlap between the lower left primary and permanent canines affecting the performance of AI in detecting the lower left primary canine.