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The formation of teeth, also known as odontogenesis, is a complex process that begins in utero, around the sixth week of embryonic development. There are three stages to this process: the bud stage, the cap stage, and the bell stage.
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Performance of a Vision-Language Model in Detecting Common Dental Conditions on Panoramic Radiographs Using Different

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¹Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR 999077, China.

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Summary

This summary is machine-generated.

GPT-4o excels at identifying dental implants and restored teeth on panoramic radiographs but struggles with caries and fractures. Diagnostic accuracy was higher at the patient level, with similar performance across tooth numbering systems.

Keywords:

artificial intelligence dentistry diagnostic accuracy large language models panoramic radiographs vision-language models

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Area of Science:

Artificial intelligence in medical imaging
Dental radiology
Machine learning for diagnostics

Background:

Panoramic radiography is crucial for diagnosing common dental conditions.
Evaluating AI performance in dental diagnostics is essential for clinical integration.
GPT-4o's potential in interpreting complex radiographic findings requires thorough assessment.

Purpose of the Study:

To assess GPT-4o's accuracy in identifying nine dental conditions on panoramic radiographs.
To compare GPT-4o's performance using FDI and Universal tooth numbering systems.
To evaluate diagnostic performance at both patient and tooth levels.

Main Methods:

Fifty panoramic radiographs with various dental conditions were analyzed by GPT-4o.
GPT-4o utilized structured prompts with FDI and Universal tooth numbering systems.
Performance metrics included balanced accuracy, sensitivity, specificity, and F1 score against a consensus standard.

Main Results:

GPT-4o showed high accuracy for dental implants, missing, and impacted teeth.
Performance was inferior for caries, periapical lesions, and fractures.
Patient-level accuracy was generally higher than tooth-level accuracy.
Similar performance was observed between FDI and Universal numbering systems.

Conclusions:

GPT-4o demonstrates strengths in detecting implants and restored teeth but limitations for caries and fractures.
Patient-level assessment yielded better diagnostic accuracy than tooth-level assessment.
Further research with larger datasets and diverse AI models is warranted.