[Advances in heart failure clinical research based on deep learning]

Area of Science:

• Cardiology
• Artificial Intelligence
• Medical Data Analysis

Background:

• Heart failure poses a significant global health threat, necessitating advanced diagnostic and prognostic tools.
• Traditional statistical and machine learning methods for heart failure analysis exhibit limitations in model capability, accuracy, and adaptability.
• Accurate analysis of medical imaging and clinical data is crucial for understanding heart failure progression and reducing patient mortality.

Approach:

• This review examines the application of deep learning (DL) techniques in analyzing heart failure data.
• It covers DL's progress, methods, and achievements in heart failure diagnosis, mortality prediction, and readmission risk assessment.
• The paper synthesizes current research, identifies challenges, and discusses future directions for DL in heart failure clinical research.

Key Points:

• Deep learning demonstrates significant potential in enhancing heart failure diagnosis and prognosis.
• AI-driven analysis of clinical and imaging data can improve accuracy and adaptability over traditional methods.
• Key applications include predicting heart failure mortality and readmission rates.

Conclusions:

• Deep learning presents a promising new perspective for clinical data analysis in heart failure.
• Further research is needed to overcome existing challenges and promote widespread clinical adoption of DL.
• Advancements in deep learning are crucial for improving patient outcomes in heart failure management.