Unmet Need
Heart disease is the leading cause of death worldwide with 30.3 million Americans diagnosed with heart disease in 2018, according to the CDC. The leading diagnostic method is to identify myocardial fibrosis (scarring), which is a leading indicator of sudden cardiac death (SCD). Current image segmentation involves cardiac magnetic resonance (CMR) imaging with contrast enhancement (i.e. late gadolinium LGE). Identifying scarring through image segmentation is challenging, as the process can be manual and tedious with low generalizability. There is a strong need for a more automated method of identifying scarring to limit the burden on physicians and technicians and more accurately aid preventative identification markers of SCD.
Technology Overview
Researchers at Johns Hopkins have developed a novel Anatomical Convolutional Segmentation Network or “ACSNet” technology, which is a fully automated approach that can identify myocardium and fibrosis in LGE-CMR images. This technology is capable of improving the image data leading to enhanced anatomical accuracy and proper handling of ambiguous regions such as the apex and base. The invention additionally enables clinical use by ensuring robust applicability across different CMR modalities, scar distributions arising from different heart pathologies, and different imaging centers. Furthermore, ACSNet automatically extracts clinical features that aid a physician in diagnosing diseases and decision-making factors.
Stage of Development
Experimental data is available with graphic user interface in development.
Publication
N/A
