Artificial intelligence and cardiovascular imaging: A win-win combination

Abstract

Rapid development of artificial intelligence (AI) is gaining grounds in medicine. Its huge impact and inevitable necessity are also reflected in cardiovascular imaging. Although AI would probably never replace doctors, it can significantly support and improve their productivity and diagnostic performance. Many algorithms have already proven useful at all stages of the cardiac imaging chain. Their crucial practical applications include classification, automatic quantification, notification, diagnosis, and risk prediction. Consequently, more reproducible and repeatable studies are obtained, and personalized reports may be available to any patient. Utilization of AI also increases patient safety and decreases healthcare costs. Furthermore, AI is particularly useful for beginners in the field of cardiac imaging as it provides anatomic guidance and interpretation of complex imaging results. In contrast, lack of interpretability and explainability in AI carries a risk of harmful recommendations. This review was aimed at summarizing AI principles, essential execution requirements, and challenges as well as its recent applications in cardiovascular imaging.

Figure 1

Schematic representation of the hierarchy among artificial intelligence, machine learning, and deep learning. All three are branches of the data science. Machine learning and deep learning are subfields within artificial intelligence and need big data to “learn.”

Figure 2

Main features of the cardiovascular images that can be extracted with radiomics techniques and used to build big data to train and test artificial intelligence applications [adapted from Artificial Intelligence in Cardiovascular Imaging, Dey et al. (4)]

Figure 3

Supervised learning. During the training step (left panel), the algorithm is given a large number of labeled inputs (known diagnosis, anatomical structure) along with desired outputs to teach it. During the testing step (right panel), the algorithm is given new order to check if it is able to label them correctly. If not, the algorithm needs improvement. This technique enables the algorithm to classify or predict views, cardiac strictures, or diagnosis on the basis of the labeled data entered into the machine

Figure 4

Unsupervised learning is a technique that feeds the algorithm with unlabeled data sets to detect previously unknown patterns (Left panel). It is the machine that identifies the patterns needed to classify objects (Right panel), cluster patients in homogeneous groups

Figure 5

Schematic representation of how an artificial neural network works. In the case of a Convolutional Neural Network, the input is an image (a matrix of pixels). The input is processed through a chain (called a graph) of neural layers. At the end of the chain, an output of any desired result can be returned. Although five layers are shown, in reality hundreds or thousands of layers are used

Figure 6

Artificial intelligence impact in the various steps of the cardiac imaging chain