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.

The cardiac surgeon's guide to artificial intelligence

“…Medical imaging traditionally has been a semi-quantitative modality. Radiomics is an emerging field that translates medical images to quantitative data to yield biological information and enable radiological phenotyping [2 ▪▪ ]. Hand-crafted radiomics is a multistep process by which features based on shapes, pixel intensities and textures are extracted as values from radiographs that is then used with artificial intelligence to perform end tasks such as survival prediction, disease state detection and diagnostics.…”

“…Artificial intelligence refers to a machine's ability to perform intelligent tasks, such as image and pattern recognition, planning action, problem solving, understanding language and recognizing objects and sounds [1 ▪▪ ]. More broadly, artificial intelligence is the application of statistical methods to large amounts of data to learn patterns and make predictions when new data is presented [2 ▪▪ ]. Artificial intelligence can offer efficiency, accuracy and novel insights to a broad range of medical arenas, especially in the setting of finite financial resources and growing healthcare demands [1 ▪▪ ,3 ▪▪ ].…”

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

“…Another example of personalized approach to heart care is shown in Fig. 9 (Badano, 2020). The AI-based approaches can help in fourout-of-five stages i.e, segmentation, post-processing, interpretation and risk estimation and prognosis with each stage using a separate model.…”

Multi-channel deep learning model-based myocardial spatial–temporal morphology feature on cardiac MRI cine images diagnoses the cause of LVH

“…Artificial intelligence (AI) brew new life into medicine during recent years [ 8 , 9 ]. The very complex heart structure has spawned numerous innovations in artificial intelligence in this area [ 10 , 11 ]. Deep learning (DL)-based disease diagnosis algorithm has proved superb ability in diagnosis of arrhythmia, recognition of myocardial infarction lesion and prediction of coronary artery disease [ 12 – 14 ].…”