Deep learning for cardiovascular medicine: a practical primer.

Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via multi-layered neural networks. Deep learning has accelerated by recent advances in computer hardware and algorithms and is increasingly applied in e-commerce, finance, and voice and image recognition to learn and classify complex datasets. The current medical literature shows both strengths and limitations of DL. Strengths of DL include its ability to automate medical image interpretation, enhance clinical decision-making, identify novel phenotypes, and select better treatment pathways in complex diseases. Deep learning may be well-suited to cardiovascular medicine in which haemodynamic and electrophysiological indices are increasingly captured on a continuous basis by wearable devices as well as image segmentation in cardiac imaging. However, DL also has significant weaknesses including difficulties in interpreting its models (the 'black-box' criticism), its need for extensive adjudicated ('labelled') data in training, lack of standardization in design, lack of data-efficiency in training, limited applicability to clinical trials, and other factors. Thus, the optimal clinical application of DL requires careful formulation of solvable problems, selection of most appropriate DL algorithms and data, and balanced interpretation of results. This review synthesizes the current state of DL for cardiovascular clinicians and investigators, and provides technical context to appreciate the promise, pitfalls, near-term challenges, and opportunities for this exciting new area.

Pokémon Go: digital health interventions to reduce cardiovascular risk.

Physical activity is associated with a lower risk of coronary heart disease/cardiovascular disease mortality, and current guidelines recommend physical activity for primary prevention in healthy individuals and secondary prevention in patients with coronary heart disease/cardiovascular disease. Over the last decade, playing classic video games has become one of the most popular leisure activities in the world, but is associated with a sedentary lifestyle. In the new era of rapidly evolving augmented reality technology, Pokémon Go, a well-known augmented reality game, may promote physical activity and prevent cardiovascular disease risks - that is, diabetes, obesity, and hypertension. Pokémon Go makes players willing to be physically active for regular and long periods of time. We report on an assessment of regular walking and playing Pokémon Go by performing data mining in Twitter.

Artificial Intelligence-Powered Blockchains for Cardiovascular Medicine.

Clinical databases, particularly those composed of big data, face growing security challenges. Blockchain, the open, decentralized, distributed public ledger technology powering cryptocurrency, records transactions securely without the need for third-party verification. In the health care setting, decentralized blockchain networks offer a secure interoperable gateway for clinical research and practice data. Here, we discuss recent advances and potential future directions for the application of blockchain and its integration with artificial intelligence (AI) in cardiovascular medicine. We first review the basic underlying concepts of this technology and contextualise it within the spectrum of current, well known applications. We then consider specific applications for cardiovascular medicine and research in areas such as high-throughput gene sequencing, wearable technologies, and clinical trials. We then evaluate current challenges to effective implementation and future directions. We also summarise the health care applications that can be realised by combining decentralized blockchain computing platforms (for data security) and AI computing (for data analytics). By leveraging high-performance computing and AI capable of securely managing large and rapidly expanding medical databases, blockchain incorporation can provide clinically meaningful predictions, help advance research methodology (eg, via robust AI-blockchain decentralized clinical trials), and provide virtual tools in clinical practice (eg, telehealth, sensory-based technologies, wearable medical devices). Integrating AI and blockchain approaches synergistically amplifies the strengths of both technologies to create novel solutions to serve the objective of providing precision cardiovascular medicine.

Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection.

Machine learning (ML) and deep learning (DL) can successfully predict high prevalence events in very large databases (big data), but the value of this methodology for risk prediction in smaller cohorts with uncommon diseases and infrequent events is uncertain. The clinical course of spontaneous coronary artery dissection (SCAD) is variable, and no reliable methods are available to predict mortality. Based on the hypothesis that machine learning (ML) and deep learning (DL) techniques could enhance the identification of patients at risk, we applied a deep neural network to information available in electronic health records (EHR) to predict in-hospital mortality in patients with SCAD. We extracted patient data from the EHR of an extensive urban health system and applied several ML and DL models using candidate clinical variables potentially associated with mortality. We partitioned the data into training and evaluation sets with cross-validation. We estimated model performance based on the area under the receiver-operator characteristics curve (AUC) and balanced accuracy. As sensitivity analyses, we examined results limited to cases with complete clinical information available. We identified 375 SCAD patients of which mortality during the index hospitalization was 11.5%. The best-performing DL algorithm identified in-hospital mortality with AUC 0.98 (95% CI 0.97-0.99), compared to other ML models (P < 0.0001). For prediction of mortality using ML models in patients with SCAD, the AUC ranged from 0.50 with the random forest method (95% CI 0.41-0.58) to 0.95 with the AdaBoost model (95% CI 0.93-0.96), with intermediate performance using logistic regression, decision tree, support vector machine, K-nearest neighbors, and extreme gradient boosting methods. A deep neural network model was associated with higher predictive accuracy and discriminative power than logistic regression or ML models for identification of patients with ACS due to SCAD prone to early mortality.

Crowdfunding for cardiovascular research.

The competition for public cardiovascular research grants has recently increased. Independent researchers are facing increasing competition for public research grant support and ultimately may need to seek alternative funding sources. Crowdfunding, a financing method of raising funds online by pooling together small donations from the online community to support a specific initiative, seems to have significant potential. However, the feasibility of crowdfunding for cardiovascular research remains unknown. Here, we performed exploratory data analysis of the feasibility of online crowdfunding in cardiovascular research.

Artificial Intelligence in Precision Cardiovascular Medicine.

Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. Each problem requires some degree of understanding of the problem, in terms of cardiovascular medicine and statistics, to apply the optimal machine-learning algorithm. In the near future, AI will result in a paradigm shift toward precision cardiovascular medicine. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI's application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine.