Risk prediction of osteoporotic vertebral compression fractures in postmenopausal osteoporotic women by machine learning modelling
The development of OVCF in postmenopausal osteoporotic women is influenced by a combination of bone metabolism, inflammatory processes, and muscle health. The machine learning model developed in this study provides a reliable and accurate tool for personalized OVCF risk prediction, allowing clinicians to optimize prevention and treatment strategies. Future large-scale prospective studies are required to validate these findings and enhance the model's predictive capabilities.
Authors
- Yusen Qiao (ORCID: https://orcid.org/0000-0003-2441-6687)
- Jian Mi (ORCID: https://orcid.org/0000-0003-3218-0133)
- Xiao Sun (ORCID: https://orcid.org/0000-0001-9750-7032)
- Haifu Sun
- Huilin Yang (ORCID: https://orcid.org/0000-0003-0317-4180)
- Yuqing Yang (ORCID: https://orcid.org/0000-0003-3518-5212)
- Qi Yan (ORCID: https://orcid.org/0000-0002-3814-0733)
- Pengyu Jing
- Wenxiang Tang (ORCID: https://orcid.org/0009-0004-3964-9357)
- Pengju Zong
Institutions
- Soochow University (CN)
- Second Affiliated Hospital of Soochow University (CN)
- First Affiliated Hospital of Soochow University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-09-12
- DOI
- https://doi.org/10.3389/fmed.2025.1664219
- Citations
- 2
- Primary Topic
- Bone health and osteoporosis research
- Type
- article
- Field-Weighted Citation Impact
- 3.44