Expanding the deep-learning model to diagnosis LVNC: Limitations and trade-offs
This work provides an improved diagnostic tool for cardiologists to detect left ventricular non-compaction across diverse patient groups, though it is incremental as it adapts an existing method.
The authors extended a deep learning model for left ventricular trabecular quantification to handle patients with various cardiomyopathies, achieving high accuracy and clinical verification of 98.9% of outputs.
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.