Transfer learning can predict the presence of hibernating myocardium from rest myocardial perfusion images
This study demonstrates the feasibility of transfer learning for predicting hibernating myocardium from rest myocardial perfusion images. The integration of deep convolutional neural networks with gradient boosting models highlights the potential of machine learning-based myocardial viability assessment, contributing valuable early evidence.
Authors
- Bangkim Chandra Khangembam (ORCID: https://orcid.org/0000-0002-3746-6547)
- Chetan Patel
- Jasim Jaleel (ORCID: https://orcid.org/0000-0002-4737-2341)
- Ritwik Wakankar (ORCID: https://orcid.org/0000-0001-7904-8944)
- Arup Roy (ORCID: https://orcid.org/0000-0001-9876-4233)
- Priyanka Gupta (ORCID: https://orcid.org/0000-0002-1907-0426)
Institutions
- School of the Art Institute of Chicago (US)
- All India Institute of Medical Sciences (IN)
Publication Details
- Journal
- Nuclear Medicine Communications
- Published
- 2025-08-26
- DOI
- https://doi.org/10.1097/mnm.0000000000002043
- Primary Topic
- Infective Endocarditis Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00