Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study
Our proposed algorithm demonstrates a clinically acceptable level of accuracy and robustness for automatic detection of CBCT soft- and hard-tissue landmarks across all types of malformations. The potential for artificial intelligence to assist in identifying three dimensional-CT landmarks in routine clinical practice and analysing large datasets for future research is promising.
Authors
- Shuli Xing
- Yan Jiang (ORCID: https://orcid.org/0000-0003-4434-3462)
- Canyang Jiang
- Jianping Huang (ORCID: https://orcid.org/0000-0003-2845-797X)
- Li Huang (ORCID: https://orcid.org/0000-0002-8454-9839)
- Lisong Lin (ORCID: https://orcid.org/0000-0003-2676-4351)
- Hao Liang (ORCID: https://orcid.org/0000-0002-9045-9717)
- Xiaohong Huang (ORCID: https://orcid.org/0000-0001-8274-5070)
- Bin Shi (ORCID: https://orcid.org/0000-0002-3643-9475)
- You Wu (ORCID: https://orcid.org/0000-0002-2160-7640)
Institutions
- Fujian Medical University (CN)
- First Affiliated Hospital of Fujian Medical University (CN)
- Fujian University of Technology (CN)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2025-04-08
- DOI
- https://doi.org/10.1186/s12903-025-05831-8
- Citations
- 2
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 3.58