Artificial Intelligence Will Transform Cardiac Imaging—Opportunities and Challenges

Abstract

Artificial intelligence (AI) using machine learning techniques will change healthcare as we know it. While healthcare AI applications are currently trailing behind popular AI applications, such as personalized web-based advertising, the pace of research and deployment is picking up and about to become disruptive. Overcoming challenges such as patient and public support, transparency over the legal basis for healthcare data use, privacy preservation, technical challenges related to accessing large-scale data from healthcare systems not designed for Big Data analysis, and deployment of AI in routine clinical practice will be crucial. Cardiac imaging and imaging of other body parts is likely to be at the frontier for the development of applications as pattern recognition and machine learning are a significant strength of AI with practical links to image processing. Many opportunities in cardiac imaging exist where AI will impact patients, medical staff, hospitals, commissioners and thus, the entire healthcare system. This perspective article will outline our vision for AI in cardiac imaging with examples of potential applications, challenges and some lessons learnt in recent years.

AI in Cardiac Imaging: A UK-Based Perspective on Addressing the Ethical, Social, and Political Challenges

“…Radiologists are in great demand to provide accurate and replicable labeling of radiological images, which are then used in supervised learning, for example in training convolutional neural networks. Those with expertise in cardiac imaging will be particularly sought after, given the especially timeconsuming and resource-intensive nature of interpreting cardiac imaging modalities such cardiac magnetic resonance (25), and any difficulty in recruiting such experts may well slow the development of these tools in this area of radiology. As any practicing clinician knows, labeling and classification of realworld clinical imaging is similar to all medical decision-making in that it involves many assumptions, heuristics, and potential biases (26)(27)(28).…”

Artificial Intelligence for Cardiac Imaging-Genetics Research

“…While the use of AI in cardiovascular imaging-genetics has great potential, the limitations and challenges of AI in genetics (90) and imaging (91) are further amplified by combining these very large data. To date, no methodological approaches have been able to include whole-genome and high-resolution wholeheart phenotypes, without requiring extensive dimensionality reduction, filtering and/or feature selection, possibly introducing errors or biases to the input data.…”

Artificial Intelligence and Cardiovascular Imaging. A win-win Combination

“…2 ), such as signal intensity, texture or shape from r images using data-characterization algorithms–enables the creation of large datasets so that any abnormality can be characterized by hundreds of parameters. These data combined with digitized clinical information improves the diagnostic process and lead to personalized treatments and prognostic stratification ( 4 - 6 ). AI-based software packages allowing fully automated quantitative assessment of images are now commercially available for echocardiography, CMR, CCT, and nuclear cardiac imaging.…”

“…Furthermore, we have the issue of personal medical data being used to improve healthcare. According to an online survey among over 2000 adults from the United Kingdom, most respondents were uncomfortable with that resolution ( 6 ).…”