Automatic recognition and measurement of anatomical structures associated with the elevation of the maxillary sinus floor by deep learning on cone-beam computed tomographic scans
The enhanced YOLOv11 framework reliably and autonomously identifies critical anatomical structures for maxillary sinus elevation including the maxillary sinus, PSAA, and maxillary alveolar crest in CBCT images. This model enables the acquisition of reliable clinical parameters, demonstrating its potential for future intelligent assisted preoperative evaluation and design of maxillary posterior dental implant surgery.
Authors
- Bin Xuan
- Feifei Zuo (ORCID: https://orcid.org/0000-0002-2016-2176)
- Pan Yang (ORCID: https://orcid.org/0000-0003-2681-4507)
- Yajie Wang (ORCID: https://orcid.org/0009-0007-5948-538X)
- Zhuojue Liu
- Weili Wang
- Qiang Ding
- Jinlei Yin
Institutions
- Beijing Zhongke Science and Technology (China) (CN)
- Aerospace Center Hospital (CN)
- Stomatology Hospital (CN)
- Beijing Obstetrics and Gynecology Hospital (CN)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2026-01-05
- DOI
- https://doi.org/10.1186/s12903-025-07609-4
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00