The application of deep learning in early enamel demineralization detection
Objective The study aims to develop a diagnostic model using intraoral photographs to accurately detect and classify early detection of enamel demineralization on tooth surfaces. Methods A retrospective analysis was conducted with 208 patients aged 14 to 44. A total of 624 high-quality digital image...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-01-01
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| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/18593.pdf |
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| Summary: | Objective The study aims to develop a diagnostic model using intraoral photographs to accurately detect and classify early detection of enamel demineralization on tooth surfaces. Methods A retrospective analysis was conducted with 208 patients aged 14 to 44. A total of 624 high-quality digital images captured under standardized conditions were used to construct a deep learning model based on the Mask region-based convolutional neural network (Mask R-CNN). The model was trained to automate the detection of enamel demineralization. Its performance was compared to two junior dentists’ diagnostic abilities. Results The model achieved an F1-score of 0.856 for detecting demineralized teeth on the validation set, a metric that reflects comprehensive diagnostic performance, demonstrating performance close to that of senior dentists. With the the model’s assistance, the junior dentists’ average F1-scores improved significantly—from 0.713 and 0.689 to 0.897 and 0.949, respectively (p < 0.05). The model accurately segmented tooth surfaces and detected demineralized areas, allowing for precise detection of demineralized areas and monitoring of lesion progression. Conclusion Deep learning can accurately segment tooth surfaces and lesion contours, enhancing the precision, accuracy, and efficiency of enamel demineralization diagnosis and area delineation. |
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| ISSN: | 2167-8359 |