The Active Connection Matrixes (ACMs) are unsupervised artificial adaptive systems able to extract from digital images features of interest (edges, tissue differentiation, etc.) unnoticeable with conventional systems. In this proof-of-concept study, we assessed the potentiality of ACMs to increase measurement precision of morphological structures (e.g., stenosis and lumen diameter) and to grasp morphological features (arterial walls) from quantitative coronary angiography (QCA), unnoticeable on the original images.
Assessment of New Coronary Features on Quantitative Coronary Angiographic Images With Innovative Unsupervised Artificial Adaptive Systems: A Proof-of-Concept Study. Amato M, Buscema M, Massini G, Maurelli G, Grossi E, Frigerio B, Ravani AL, Sansaro D, Coggi D, Ferrari C, Bartorelli AL, Veglia F, Tremoli E, Baldassarre D. Front Cardiovasc Med. 2021 Oct 14;8:730626. doi: 10.3389/fcvm.2021.730626