Steps to use artificial intelligence in echocardiography
- PDF / 862,316 Bytes
- 7 Pages / 595.276 x 790.866 pts Page_size
- 88 Downloads / 198 Views
REVIEW ARTICLE
Steps to use artificial intelligence in echocardiography Kenya Kusunose1 Received: 14 September 2020 / Revised: 29 September 2020 / Accepted: 1 October 2020 © Japanese Society of Echocardiography 2020
Abstract Artificial intelligence (AI) has influenced every field of cardiovascular imaging in all phases from acquisition to reporting. Compared with computed tomography and magnetic resonance imaging, there is an issue of high observer variation in the interpretation of echocardiograms. Therefore, AI can help minimize the observer variation and provide accurate diagnosis in the field of echocardiography. In this review, we summarize the necessity for automated diagnosis in the echocardiographic field, and discuss the results of AI application to echocardiography and future perspectives. Currently, there are two roles for AI in cardiovascular imaging. One is the automation of tasks performed by humans, such as image segmentation, measurement of cardiac structural and functional parameters. The other is the discovery of clinically important insights. Most reported applications were focused on the automation of tasks. Moreover, algorithms that can obtain cardiac measurements are also being reported. In the next stage, AI can be expected to expand and enrich existing knowledge. With the continual evolution of technology, cardiologists should become well versed in this new knowledge of AI and be able to harness it as a tool. AI can be incorporated into everyday clinical practice and become a valuable aid for many healthcare professionals dealing with cardiovascular diseases. Keywords Radiomic · Deep learning · Artificial intelligence · Echocardiography · Cardiovascular imaging
Introduction Artificial intelligence (AI) is the process of having a computational program that can perform tasks of human intelligence (e.g. pattern recognition) by mimicking human thought processes [1]. These programs have been developed since the 1970s. It’s been used in gaming, social media, and robotics for a long time. Machine learning has been developed since the 1980s. Machine learning is relevant for medicine as a precursor to AI. Conventional machine learning is a method in which a computer automatically provides appropriate judgments from inspection images by giving control rules (e.g., features). After 2012, deep learning has been developed by automated learning of features extracted through repetitive trial and error. Deep learning is a type of Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12574-020-00496-4) contains supplementary material, which is available to authorized users. * Kenya Kusunose kusunosek@tokushima‑u.ac.jp 1
machine learning, the sophistication of its internal structure and learning methods enabling more accurate results than conventional machine learning [2]. Deep learning identifies patterns progressively from large databases without being explicitly programmed. In the last 10 years, deep learning has led to new strategies of machine learnin
Data Loading...
