Deep Learning for Early Detection of Cardiovascular Diseases From Medical Imaging
This study demonstrates the potential of deep learning, particularly CNN-based models, for automating the early detection of CVDs using echocardiogram videos. The high performance of the model suggests it could contribute to faster, more accurate, and cost-effective diagnosis in clinical practice. Future research should focus on improving model generalizability across diverse populations and enhancing interpretability for integration into clinical workflows.
Authors
- Amogh Verma (ORCID: https://orcid.org/0000-0003-2499-4874)
- Manesh Kumar
- FNU Venjhraj (ORCID: https://orcid.org/0009-0002-0618-4237)
- Sangeeta Davi
- Mukesh Kumar (ORCID: https://orcid.org/0000-0002-3699-3740)
- Zainab Muhammad Hanif (ORCID: https://orcid.org/0009-0006-5037-028X)
- Ashvin Kumar
- Aiman Salam Shaikh
- Muskan Kumari
- F. N. U. Ridham
- Insiya Fatima Azad
- F. N. U. Suwasi
Institutions
- Shaheed Mohtarma Benazir Bhutto Medical University (PK)
- Dow University of Health Sciences (PK)
- Rama Hospital & Research Centre (IN)
- Mountain Medicine Society of Nepal (NP)
Publication Details
- Journal
- Health Science Reports
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1002/hsr2.71334
- Citations
- 3
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 4.08