YOLOv8-Seg: a deep learning approach for accurate classification of osteoporotic vertebral fractures
The results demonstrate the high proficiency of the YOLOv8-Seg model in identifying OVFs, indicating its potential as a decision-support tool to streamline the current manual diagnostic process. This work underscores the significant potential of deep learning to assist medical professionals in achieving early and precise diagnoses, thereby improving patient outcomes.
Authors
- Heting Xiao
- T Chen
- Yusen Qiao (ORCID: https://orcid.org/0000-0001-7654-8636)
- Xuewen Zhao (ORCID: https://orcid.org/0009-0004-3804-5412)
- Yu Wang (ORCID: https://orcid.org/0000-0003-1606-6795)
- Zhiheng Gao
- Yonggang Li
- Yuwei Chen (ORCID: https://orcid.org/0000-0001-6955-4704)
- Haifu Sun
- Lingjie Wang (ORCID: https://orcid.org/0000-0003-1003-3300)
- Feng Yang
- Yuchen Qian
Institutions
- Soochow University (CN)
- First Affiliated Hospital of Soochow University (CN)
- The Fifth People’s Hospital of Suzhou (CN)
Publication Details
- Journal
- Frontiers in Radiology
- Published
- 2025-10-14
- DOI
- https://doi.org/10.3389/fradi.2025.1651798
- Citations
- 1
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.67
Funders
- National Natural Science Foundation of China