A pipeline for developing deep learning prognostic prediction models in cardiac magnetic resonance image analysis

Area of Science:

• Cardiology
• Medical Imaging Analysis
• Artificial Intelligence in Medicine

Background:

• Clinical prediction models are essential for healthcare decisions.
• Traditional regression models have limitations.
• Deep learning (DL) shows promise for analyzing heterogeneous data, including medical images.

Purpose of the Study:

• To propose a framework for developing DL-based prediction models for cardiac magnetic resonance image analysis.
• To support researchers in applying DL in cardiology, focusing on arrhythmic risk prediction.
• To address challenges in assessing arrhythmic risk in cardiomyopathy.

Main Methods:

• A four-step pipeline for developing DL prediction models was proposed.
• The framework focuses on cardiac magnetic resonance image analysis.
• The study explores DL for predicting major arrhythmic events in dilated cardiomyopathy.

Main Results:

• DL models can effectively analyze heterogeneous data, including medical images.
• Initial results for DL models in predicting arrhythmic events in dilated cardiomyopathy are promising.
• Challenges in DL model development include problem conceptualization, variable selection, architecture design, and explainability.

Conclusions:

• DL offers a powerful alternative to traditional models for clinical prediction.
• The proposed framework can advance DL applications in cardiology.
• Further validation is required before clinical implementation of DL models for arrhythmic risk prediction.