A two-stage multimodal learning framework for the automated diagnosis of obstructive coronary artery disease based on dynamic single-photon emission computed tomography
The integration of D-SPECT imaging and QGS-derived functional parameters within a unified multimodal framework significantly improves diagnostic performance for OCAD. This approach demonstrates the feasibility of serving as a one-stop, image-based diagnostic workflow for clinical practice.
Authors
- Chao Zhou (ORCID: https://orcid.org/0000-0002-9443-6707)
- Ya-Jie Zhu
- Jing Liu (ORCID: https://orcid.org/0000-0002-0493-296X)
- Dekui Chen
- Xingyu Wang (ORCID: https://orcid.org/0009-0006-0047-2499)
- Haijun Wang (ORCID: https://orcid.org/0000-0002-7542-5957)
- Tongtong Li (ORCID: https://orcid.org/0009-0001-6370-7332)
- Zhongwei Li (ORCID: https://orcid.org/0000-0002-3934-9053)
- Ping Xie (ORCID: https://orcid.org/0000-0003-0667-1377)
- Rujing Wang (ORCID: https://orcid.org/0000-0002-0115-1156)
Institutions
- Gansu Provincial Hospital (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-12-31
- DOI
- https://doi.org/10.21037/qims-2025-617
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00