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Exploring advanced deep learning approaches in cardiac image analysis: A comprehensive review.
Assia Boukhamla1, Nabiha Azizi1, Samir Brahim Belhaouari2
1LabGED Laboratory, Department of Computer Science, Faculty of Technology, Badji Mokhtar - Annaba University 12, P.O.Box, 23000, Annaba, Algeria.
Computers in Biology and Medicine
|July 5, 2025
Summary
Deep learning (DL) advances cardiac image analysis for early cardiovascular disease (CVD) diagnosis. This review covers DL applications, including transformers and compression techniques, for improved heart condition detection.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Cardiac image analysis is crucial for diagnosing cardiovascular diseases (CVDs).
- Automated disease identification aids early diagnosis and management.
- Deep learning (DL) and advanced imaging offer new opportunities in cardiology.
Purpose of the Study:
- To review recent deep learning applications in cardiac image analysis.
- To survey common imaging modalities and DL architectures used.
- To analyze deep model compression techniques in cardiac image processing.
Main Methods:
- Systematic review following PRISMA methodology.
- Analysis of recent research on DL architectures in cardiac imaging.
- Examination of cardiac imaging datasets and model compression contributions.
Main Results:
- Significant progress in DL for cardiac image analysis.
- Introduction of new techniques like transformers and foundation models.
- Advancements in deep model compression for cardiac image processing.
Conclusions:
- Deep learning is transforming cardiac image interpretation.
- Open challenges and future research directions are discussed.
- The field shows promise for enhanced cardiovascular diagnostics.
