The effectiveness of a novel artificial intelligence (AI) model in detecting oral and dental diseases
Abstract Introduction Implementing artificial intelligence (AI) to use patient-provided intra-oral photos to detect possible pathologies represents a significant advancement in oral healthcare. AI algorithms can potentially use photographs to remotely detect issues, including caries, demineralisatio...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2025-06-01
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| Series: | BDJ Open |
| Online Access: | https://doi.org/10.1038/s41405-025-00336-6 |
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| Summary: | Abstract Introduction Implementing artificial intelligence (AI) to use patient-provided intra-oral photos to detect possible pathologies represents a significant advancement in oral healthcare. AI algorithms can potentially use photographs to remotely detect issues, including caries, demineralisation, and mucosal abnormalities such as gingivitis. Aim This study aims to assess the effectiveness of a newly developed AI model in detecting common oral pathologies from intra-oral images. Method A unique AI machine-learning model was built using a convolutional neural network (CNN) model and trained using a dataset of over five thousand images. Ninety different unseen images were selected and presented to the AI model to test the accuracy of disease detection. The AI model’s performance was compared with answers provided by fifty-one dentists who reviewed the same ninety images. Both groups identified plaque, calculus, gingivitis, and caries in the images. Results Among the 51 participating dentists, clinicians correctly diagnosed 82.09% of pathologies, while AI achieved 81.11%. Clinician diagnoses matched the AI’s results 81.02% of the time. Statistical analysis using t-tests at 95% and 99% confidence levels yielded p-values of 0.63 and 0.79 for different comparisons, with mean agreement rates of 81.55% and 95.11%, respectively. The findings support the hypothesis that the average AI answers are the same as average answers by dentists, as all p-values exceeded significance thresholds (p > 0.05). Conclusion Despite current limitations, this study highlights the potential of machine learning AI models in the early detection and diagnosis of dental pathologies. AI integration has the scope to enhance clinicians’ diagnostic workflows in dentistry, with advancements in neural networks and machine learning poised to solidify its role as a valuable diagnostic aid. |
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| ISSN: | 2056-807X |