Automated deep-learning quantification of nine patellofemoral instability parameters on multislice CT images
The proposed deep learning model reliably automates PI measurement, reducing the time and variability associated with manual assessment and mitigating dependence on examiner experience.
Authors
- Chengxin Zhu (ORCID: https://orcid.org/0000-0002-1062-4947)
- Bowen Zheng (ORCID: https://orcid.org/0000-0001-5922-9780)
- Qin Ye (ORCID: https://orcid.org/0000-0001-7842-4397)
- Yingying Ying
- Junfen Ye
- Jiake Hua
Institutions
- Hangzhou Normal University (CN)
- Wenzhou Medical University (CN)
- First Hospital of Jiaxing (CN)
- Wenzhou City People's Hospital (CN)
- Zhejiang Taizhou Hospital (CN)
- Hangzhou Medical College (CN)
Publication Details
- Journal
- Bone & Joint Open
- Published
- 2026-03-12
- DOI
- https://doi.org/10.1302/2633-1462.73.bjo-2025-0326.r1
- Primary Topic
- Lower Extremity Biomechanics and Pathologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00