ArticleDiagnostics (Basel, Switzerland)2023
Tooth Type Enhanced Transformer for Children Caries Diagnosis on Dental Panoramic Radiographs.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Transformer-based models in dentistry: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Artificial intelligence for binary dental caries diagnosis using intraoral images and dental radiographs: a systematic review and meta-analysis.BMC oral health · 2026Pooled it
- Evaluating vision transformers and convolutional neural networks in the context of dental image processing: a systematic review.BMC oral health · 2025Pooled it
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- AI-Based Detection of Periapical Lesions in Endodontic Radiographs: a Diagnostic Accuracy Study.Maedica · 2026Article
- Artificial Intelligence and Innovation in Oral Health Care Sciences: A Conceptual Review.Healthcare (Basel, Switzerland) · 2025Review
- Charting New Territory: AI Applications in Dental Caries Detection from Panoramic Imaging.Dentistry journal · 2025Review
- Automated detection and classification of osteolytic lesions in panoramic radiographs using CNNs and vision transformers.BMC oral health · 2025Article
- Artificial Intelligence Methods in the Detection of Oral Diseases on Pantomographic Images-A Systematic Narrative Review.Journal of clinical medicine · 2025Review
- Precision Oral Medicine: A DPR Segmentation and Transfer Learning Approach for Detecting Third Molar Compress Inferior Alveolar Nerve.IEEE journal of translational engineering in health and medicine · 2025Article
- Article
- Automatized Detection of Periodontal Bone Loss on Periapical Radiographs by Vision Transformer Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
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Authors and funding
7 authors.
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Abstract
The objective of this study was to introduce a novel deep learning technique for more accurate children caries diagnosis on dental panoramic radiographs. Specifically, a swin transformer is introduced, which is compared with the state-of-the-art convolutional neural network (CNN) methods that are widely used for caries diagnosis. A tooth type enhanced swin transformer is further proposed by considering the differences among canine, molar and incisor. Modeling the above differences in swin transformer, the proposed method was expected to mine domain knowledge for more accurate caries diagnosis. To test the proposed method, a children panoramic radiograph database was built and labeled with a total of 6028 teeth. Swin transformer shows better diagnosis performance compared with typical CNN methods, which indicates the usefulness of this new technique for children caries diagnosis on panoramic radiographs. Furthermore, the proposed tooth type enhanced swin transformer outperforms the naive swin transformer with the accuracy, precision, recall, F1 and area-under-the-curve being 0.8557, 0.8832, 0.8317, 0.8567 and 0.9223, respectively. This indicates that the transformer model can be further improved with a consideration of domain knowledge instead of a copy of previous transformer models designed for natural images. Finally, we compare the proposed tooth type enhanced swin transformer with two attending doctors. The proposed method shows higher caries diagnosis accuracy for the first and second primary molars, which may assist dentists in caries diagnosis.
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