Expanding the deep-learning model to diagnosis LVNC: Limitations and trade-offs

TL;DR

By quantifying LV trabeculation with $VT\%$ defined as $VT\% = 100 \cdot \frac{\textnormal{Trab. volume}}{\textnormal{Trab. volume}+\textnormal{Compacted volume}}$, the study extends DL-LVTQ to heterogeneous cardiomyopathies using a multicenter MRI dataset. It adapts a U-Net-based trabeculation quantification pipeline, training on three populations (P, X, H) across multiple scanners, and demonstrates robust segmentation and diagnostic performance with ROC AUC $= 0.94$ at a $VT\%$ threshold of $27.1\%$. Medical validation shows $98.9\%$ of LVNC-relevant slices deemed clinically valid, supporting automated LVNC diagnosis across diverse patient groups. The work highlights improved generalization and potential for cross-institution LVNC assessment, while calling for more LVNC data and careful threshold tuning to sustain performance.

Abstract

Hyper-trabeculation or non-compaction in the left ventricle of the myocardium (LVNC) is a recently classified form of cardiomyopathy. Several methods have been proposed to quantify the trabeculae accurately in the left ventricle, but there is no general agreement in the medical community to use a particular approach. In previous work, we proposed DL-LVTQ, a deep learning approach for left ventricular trabecular quantification based on a U-Net CNN architecture. DL-LVTQ was an automatic diagnosis tool developed from a dataset of patients with the same cardiomyopathy (hypertrophic cardiomyopathy). In this work, we have extended and adapted DL-LVTQ to cope with patients with different cardiomyopathies. The dataset consists of up 379 patients in three groups with different particularities and cardiomyopathies. Patient images were taken from different scanners and hospitals. We have modified and adapted the U-Net convolutional neural network to account for the different particularities of a heterogeneous group of patients with various unclassifiable or mixed and inherited cardiomyopathies. The inclusion of new groups of patients has increased the accuracy, specificity and kappa values while maintaining the sensitivity of the automatic deep learning method proposed. Therefore, a better-prepared diagnosis tool is ready for various cardiomyopathies with different characteristics. Cardiologists have considered that 98.9% of the evaluated outputs are verified clinically for diagnosis. Therefore, the high precision to segment the different cardiac structures allows us to make a robust diagnostic system objective and faster, decreasing human error and time spent.

Figures (3)

• Figure 1: Segmentation of left ventricle, highlighting the external layer (green), the left ventricle cavity (blue) and the trabecular zone or non-compacted area (yellow).
• Figure 2: Architecture of the U-Net segmentation network in this research. Each blue box corresponds to a multi-channel feature map.
• Figure 3: Output slices for the patient H15. Green indicates the compacted external layer of the left ventricle cavity and yellow the trabecular zone.