A primer in artificial intelligence in cardiovascular medicine

Abstract

Driven by recent developments in computational power, algorithms and web-based storage resources, machine learning (ML)-based artificial intelligence (AI) has quickly gained ground as the solution for many technological and societal challenges. AI education has become very popular and is oversubscribed at Dutch universities. Major investments were made in 2018 to develop and build the first AI-driven hospitals to improve patient care and reduce healthcare costs. AI has the potential to greatly enhance traditional statistical analyses in many domains and has been demonstrated to allow the discovery of ‘hidden’ information in highly complex datasets. As such, AI can also be of significant value in the diagnosis and treatment of cardiovascular disease, and the first applications of AI in the cardiovascular field are promising. However, many professionals in the cardiovascular field involved in patient care, education or science are unaware of the basics behind AI and the existing and expected applications in their field. In this review, we aim to introduce the broad cardiovascular community to the basics of modern ML-based AI and explain several of the commonly used algorithms. We also summarise their initial and future applications relevant to the cardiovascular field.

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

“…Computational intelligence, especially, shows its predictive power when applied to medical records [25,26], or coupled with imaging [27][28][29]. Further, deep learning and meta-analysis studies applied to this field have also recently appeared in the literature [30][31][32][33], improving on human specialists' performance [34], albeit showing lower accuracy (0.75 versus 0.59).…”

Cardiovascular Disease Prediction from Electrocardiogram by using Machine Learning Method: A Snapshot from the Subjects of the Malaysian Cohort

“…When the measurements are made on independent variables for each observation, the LDA functions are continuous quantities [43]. SVM is a binary classifier widely used for classification and regression [18]. This technique constructs an optimal hyperplane (decision boundary between classes) that separates all classes [44].…”

“…ANN is a training method that emulates the human brain and is an outstanding method for predicting the relationship between the input and target values [48]. ANN has been widely used in cardiology applications for pattern recognition and classification tasks [18]. Supplementary Files This is a list of supplementary files associated with this preprint.…”

“…Machine learning (ML)-based artificial intelligence, such as knowledge-based expert systems, differs from other methods and is extensively used in the classification and prediction of CVDs [18]. The well-known ML algorithms have four types: supervised, unsupervised, semi supervised, and reinforcement learning.…”

The history of transcatheter aortic valve implantation: The role and contribution of an early believer and adopter, the Netherlands

“…2 and 3. Beyond the analysis of outcomes and the underlying mechanisms in single, multicentre national and international initiatives and collaborations, research has been initiated to elucidate and predict the interaction between the device and host as well as the role of Artificial Intelligence in TAVI [26][27][28][29][30][31][32][33][34][35]. The clinical drive of innovation providing the best possible care to the individual patient and the scientific work (volume and content) of all Dutch medical professionals is an expression of the stimulating environment in which they have the pleasure to live and work.…”