Automated opportunistic screening for osteoporosis using deep learning-based automatic segmentation and radiomics on proximal femur images from low-dose abdominal CT
A three-classification predictive model combining a deep learning-based automatic segmentation of the proximal femur and a radiomics-based bone status classification on LDCT images can be used for the opportunistic detection of osteoporosis.
Authors
- Mengting Hu (ORCID: https://orcid.org/0009-0002-2897-5996)
- Shigeng Wang (ORCID: https://orcid.org/0000-0002-5643-9026)
- Wei Wei (ORCID: https://orcid.org/0000-0002-3120-8176)
- Jingyi Zhang (ORCID: https://orcid.org/0000-0003-0622-8279)
- Jian He (ORCID: https://orcid.org/0000-0001-8140-4610)
- Jianying Li (ORCID: https://orcid.org/0000-0002-9825-672X)
- Yijun Liu (ORCID: https://orcid.org/0000-0001-7780-8866)
- Chunling Du (ORCID: https://orcid.org/0000-0002-4252-932X)
- Jiageng Shen
- Qiye Cheng
Institutions
- Dalian Medical University (CN)
- First Affiliated Hospital of Dalian Medical University (CN)
Publication Details
- Journal
- BMC Musculoskeletal Disorders
- Published
- 2025-04-17
- DOI
- https://doi.org/10.1186/s12891-025-08631-x
- Citations
- 6
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 8.16