Non-ischemic endocardial scar geometric remodeling toward topological machine learning

Area of Science:

• Cardiology
• Medical Imaging
• Computational Biology

Background:

• Scar tissue significantly impacts myocardial disease progression and cardiac failure.
• Accurate scar segmentation is crucial for risk stratification and cardiovascular disease evaluation.

Purpose of the Study:

• To apply topology data analysis and machine learning for scar tissue geometry confirmation.
• To improve visualization and quantification of cardiac scar tissue.
• To develop an integrated architecture combining geometry and topology for machine learning.

Main Methods:

• Morphological image processing to define endocardial wall regions.
• Convolutional Neural Networks (CNNs) applied to delayed enhancement cardiac CT images for scar recognition.
• Stacking 2D segmented images to construct 3D scar geometry for visualization.
• Mathematical calculations and morphological processing for scar tissue validation.

Main Results:

• Achieved 89.23% accuracy, 91.11% sensitivity, and 87.75% specificity using CNNs with small convolution/pooling layers.
• Quantified dissimilarity distance between normal and scar endocardial tissue as 9.37.
• Successfully visualized and quantified scar tissue geometry.

Conclusions:

• The novel integration of topology and machine learning offers a better understanding of scar structure.
• This method aids in analyzing transmural variations within the left ventricular endocardial wall.
• The findings support improved clinical risk prediction and disease evaluation in cardiovascular conditions.