Automatic diagnosis of coronary artery stenosis by deep learning based on X-ray coronary angiography
We introduce an image-based vascular analysis method that localizes and grades stenosis in X-ray coronary angiography. This method can automatically identify clinically critical grades, especially within the 71-100% range. Deep learning methods can potentially facilitate the diagnosis of CAD and patient-centric treatment planning.
Authors
- Zhang Shi-sheng (ORCID: https://orcid.org/0000-0002-2072-9961)
- Kandi Zhang (ORCID: https://orcid.org/0000-0003-3335-4596)
- Chengyu Mao (ORCID: https://orcid.org/0000-0001-9740-8835)
- Dongjiu Li
- Yuqi Fan
- Qing He (ORCID: https://orcid.org/0000-0002-3588-9466)
- Huasu Zeng (ORCID: https://orcid.org/0009-0006-2714-9229)
- Tiantian Zhang (ORCID: https://orcid.org/0000-0002-5162-0408)
- Jun Gu (ORCID: https://orcid.org/0000-0002-3056-9308)
- Junfeng Zhang (ORCID: https://orcid.org/0009-0009-2097-7250)
Institutions
- The University of Sydney (AU)
- Shanghai Jiao Tong University (CN)
- Shanghai Ninth People's Hospital (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-10-29
- DOI
- https://doi.org/10.21037/qims-24-2415
- Citations
- 1
- Primary Topic
- Coronary Interventions and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 2.01