Quality control of elbow joint radiography using a YOLOv8-based artificial intelligence technology
QC of elbow joint radiography is important for detecting diseases. Models based on YOLOv8 are proposed and perform well in image QC. Models offer objective and efficient solutions for QC in elbow joint radiographs.
Authors
- Lingjing Zhang
- Zhiming Zhou (ORCID: https://orcid.org/0000-0001-8462-4483)
- Weijuan Chen (ORCID: https://orcid.org/0009-0009-0453-5823)
- Tian‐wu Chen (ORCID: https://orcid.org/0000-0001-5776-3429)
- X. X. Ding (ORCID: https://orcid.org/0000-0002-2807-2597)
- Dajing Guo (ORCID: https://orcid.org/0000-0001-8655-6621)
- Qi Lai (ORCID: https://orcid.org/0000-0003-1966-9292)
- Jinhua Chen (ORCID: https://orcid.org/0000-0003-2270-8248)
- Xin Huang (ORCID: https://orcid.org/0000-0002-8899-9992)
- Wenli Jiang (ORCID: https://orcid.org/0009-0002-5102-5972)
Institutions
- Dalian Medical University (CN)
- Second Affiliated Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- European Radiology Experimental
- Published
- 2024-09-20
- DOI
- https://doi.org/10.1186/s41747-024-00504-7
- Citations
- 2
- Primary Topic
- Shoulder Injury and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.15