Artificial Intelligence in Tissue Engineering for cardiovascular treatment

Abstract

The goal of this article is to evaluate how recent developments in fabrication techniques, genome editing, and machine learning are influencing the future of cardiac tissue engineering. AI algorithms have been used to diagnose, segment and reconstruct images, quality control, prognosis, Phen grouping, and scientific discovery in cardiology. AI is being used to automate electrocardiogram interpretation and patient categorization and prognosis. ML models can detect and compute a variety of cardiac parameters, including P and T waves, QRS complexes, heart rate, cardiac axis, ECG interval lengths, ST-changes, and common rhythm abnormalities. A 34-layer DNN has recently been developed that can recognise with more recall a human cardiologist. A variety of machine learning (ML), including SVMs, gradient boosting machines (GBMs), MLNNs, it is used to estimate patients' likelihood of experiencing an ischemic stroke. Transthoracic echocardiography provides instantaneous visualisation of the heart's structure, allowing for rapid diagnosis of structural abnormalities. An innovative method for calculating LVEF automatically from 2-D echocardiographic pictures using AI-learned pattern recognition. CRISPR/Cas9 systems used to design for cell of cardiac, with potential such as enhanced the avoidance of the body's immunological response. CRISPR/Cas9 technology can be used to improve cell homing, delete inactive genes, model cardiovascular disease, reduce immunogenicity, and protect hESC-derived allografts from immune rejection.