Determining the reliability of diagnosis and treatment using artificial intelligence software with panoramic radiographs

Abstract

Purpose: The objective of this study was to evaluate the accuracy and effectiveness of an artificial intelligence (AI) program in identifying dental conditions using panoramic radiographs (PRs), as well as to assess the appropriateness of its treatment recommendations.

Material and methods: PRs from 100 patients (representing 4497 teeth) with known clinical examination findings were randomly selected from a university database. Three dentomaxillofacial radiologists and the Diagnocat AI software evaluated these PRs. The evaluations were focused on various dental conditions and treatments, including canal filling, caries, cast post and core, dental calculus, fillings, furcation lesions, implants, lack of interproximal tooth contact, open margins, overhangs, periapical lesions, periodontal bone loss, short fillings, voids in root fillings, overfillings, pontics, root fragments, impacted teeth, artificial crowns, missing teeth, and healthy teeth.

Results: The AI demonstrated almost perfect agreement (exceeding 0.81) in most of the assessments when compared to the ground truth. The sensitivity was very high (above 0.8) for the evaluation of healthy teeth, artificial crowns, dental calculus, missing teeth, fillings, lack of interproximal contact, periodontal bone loss, and implants. However, the sensitivity was low for the assessment of caries, periapical lesions, pontic voids in the root canal, and overhangs.

Conclusion: Despite the limitations of this study, the synthesized data suggest that AI-based decision support systems can serve as a valuable tool in detecting dental conditions, when used with PR for clinical dental applications.

Keywords: Artificial Intelligence; Deep Learning; Dentistry; Radiography, Panoramic.

Fig. 1. A representative panoramic image shows a diagnosis automatically generated by Diagnocat.

Fig. 2. Image illustrating the predictions made by the trained model, used to define the mouth region as the area of interest within the panoramic radiograph.

Fig. 3. Mosaic data augmentation teaches the model to recognize objects in different locations, eliminating the need for a single specific context. This method of data augmentation combines numerous training images into a single image, using random proportions.