<scp>ResNet</scp>‐Transformer deep learning model‐aided detection of dens evaginatus
Based on these results, the integration of deep learning for dens evaginatus detection holds significance and can augment dentists' proficiency in identifying such anomaly.
Authors
- Pengcheng He (ORCID: https://orcid.org/0000-0002-5030-0824)
- Shihao Li (ORCID: https://orcid.org/0000-0002-6418-7818)
- Zhihe Zhao (ORCID: https://orcid.org/0000-0003-2955-1706)
- Liwei Zheng
- Jialing Liu (ORCID: https://orcid.org/0000-0001-5554-0870)
- Siwei Wang (ORCID: https://orcid.org/0000-0001-9517-262X)
- Xin Zhou (ORCID: https://orcid.org/0009-0001-9321-3632)
Institutions
- Sichuan University (CN)
- West China Second University Hospital of Sichuan University (CN)
Publication Details
- Journal
- International Journal of Paediatric Dentistry
- Published
- 2024-11-15
- DOI
- https://doi.org/10.1111/ipd.13282
- Citations
- 5
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.47
Funders
- National Natural Science Foundation of China
- Sichuan University
- Natural Science Foundation of Sichuan Province
- West China Hospital, Sichuan University