Cardiovascular diseases are the leading cause of global mortality. Over the past two decades, researchers have tried to provide novel solutions for end-stage heart failure to address cardiac transplantation hurdles such as donor organ shortage, chronic rejection, and life-long immunosuppression. Cardiac decellularized extracellular matrix (dECM) has been widely explored as a promising approach in tissue-regenerative medicine because of its remarkable similarity to the original tissue.
View Article and Find Full Text PDFWhole organ decellularization techniques have facilitated the fabrication of extracellular matrices (ECMs) for engineering new organs. Unfortunately, there is no objective gold standard evaluation of the scaffold without applying a destructive method such as histological analysis or DNA removal quantification of the dry tissue. Our proposal is a software application using deep convolutional neural networks (DCNN) to distinguish between different stages of decellularization, determining the exact moment of completion.
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