Utility of osteoporosis screening based on estimation of bone mineral density using bidirectional chest radiographs with deep learning models
These results suggest that bidirectional CXR contributes to improved accuracy of BMD estimation and osteoporosis screening compared with single-view CXR. This study proposes a new approach for early detection of osteoporosis using a deep learning model with frontal and lateral CXR inputs. BMD estimation using bidirectional CXR showed improved detection performance for low bone mass and osteoporosis, and has the potential to be used as a clinical decision criterion. The proposed method shows potential for more appropriate screening decisions, suggesting its usefulness in clinical practice.
Authors
- Chiharu Kai
- Ikumi Sato (ORCID: https://orcid.org/0000-0002-1417-121X)
- Yuta Hirono
- Yoichi Sato (ORCID: https://orcid.org/0000-0001-6144-3735)
- Akifumi Yoshida (ORCID: https://orcid.org/0000-0001-8488-5384)
- Satoshi Kasai
Institutions
- Nagoya University (JP)
- Niigata University of Health and Welfare (JP)
- Toto (Japan) (JP)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-03-26
- DOI
- https://doi.org/10.3389/fmed.2025.1499670
- Citations
- 2
- Primary Topic
- Bone health and osteoporosis research
- Type
- article
- Field-Weighted Citation Impact
- 3.44
Funders
- Japanese Orthopaedic Association
- Japan Society for the Promotion of Science