Impact of Artificial Intelligence on Interventional Cardiology: From Decision-Making Aid to Advanced Interventional Procedure Assistance

The intelligent Impella: Future perspectives of artificial intelligence in the setting of Impella support

AIMS

Artificial intelligence (AI) has emerged as a potential useful tool to support clinical treatment of heart failure, including the setting of mechanical circulatory support (MCS). Modern Impella pumps are equipped with advanced technology (SmartAssist), enabling real-time acquisition and display of data related to both pump performance and the patient's haemodynamic status. These data emerge as an 'ideal' source for data-driven AI applications to predict the clinical course of an ongoing therapeutic protocol. Yet, no evidence of effective application of AI tools in the setting of Impella support is available. On this background, we aimed at identifying possible future applications of AI-based tools in the setting of temporary MCS with an Impella device.

METHODS

We explored the state of research and development at the intersection of AI and Impella support and derived future potential applications of AI in routine Impella clinical management.

RESULTS

We identified different areas where the future implementation of AI tools may contribute to addressing important clinical challenges in the setting of Impella support, including (i) early identification of the best suited pathway of care according to patients' conditions at presentation and intention to treat, (ii) prediction of therapy outcomes according to different possible therapeutic actions, (iii) optimization of device implantation procedures and evaluation of proper pump position over the whole course of support and (iv) prevention and/or rationale management of haemocompatibility-related adverse events. For each of those areas, we discuss the potential advantages, challenges and implications of harnessing AI-driven insights in the setting of MCS with an Impella device.

CONCLUSIONS

Temporary MCS with an Impella device has great potential to benefit from the integration of AI-based tools. Such tools may indeed translate into groundbreaking innovation supporting clinical decision-making and therapy regulation, in particular in complex scenarios such as the multidevice MCS strategy.

Unlocking the potential of artificial intelligence in sports cardiology: does it have a role in evaluating athlete's heart?

The integration of artificial intelligence (AI) technologies is evolving in different fields of cardiology and in particular in sports cardiology. Artificial intelligence offers significant opportunities to enhance risk assessment, diagnosis, treatment planning, and monitoring of athletes. This article explores the application of AI in various aspects of sports cardiology, including imaging techniques, genetic testing, and wearable devices. The use of machine learning and deep neural networks enables improved analysis and interpretation of complex datasets. However, ethical and legal dilemmas must be addressed, including informed consent, algorithmic fairness, data privacy, and intellectual property issues. The integration of AI technologies should complement the expertise of physicians, allowing for a balanced approach that optimizes patient care and outcomes. Ongoing research and collaborations are vital to harness the full potential of AI in sports cardiology and advance our management of cardiovascular health in athletes.

Feasibility of artificial intelligence its current status, clinical applications, and future direction in cardiovascular disease

In routine clinical practice, the diagnosis and treatment of cardiovascular disease (CVD) rely on data in a variety of formats. These formats comprise invasive angiography, laboratory data, non-invasive imaging diagnostics, and patient history. Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. In cardiovascular medicine, artificial intelligence (AI) algorithms have been used to discover novel genotypes and phenotypes in established diseases enhance patient care, enable cost effectiveness, and lower readmission and mortality rates. AI will lead to a paradigm change toward precision cardiovascular medicine in the near future. The promise application of AI in cardiovascular medicine is immense; however, failure to recognize and ignorance of the challenges may overshadow its potential clinical impact. AI can facilitate every stage in cardiology in the imaging process, from acquisition and reconstruction, to segmentation, measurement, interpretation, and subsequent clinical pathways. Along with new possibilities, new threats arise, acknowledging and understanding them is as important as understanding the machine learning (ML) methodology itself. Therefore, attention is also paid to the current opinions and guidelines regarding the validation and safety of AI. This paper provides a outline for clinicians on relevant aspects of AI and machine learning, selection of applications and methods in cardiology to date, and identifies how cardiovascular medicine could incorporate AI in the future. With progress continuing in this emerging technology, the impact for cardiovascular medicine is highlighted to provide insight for the practicing clinician and to identify potential patient benefits.

Machine learning prediction of hospital discharge disposition for inpatients with acute ischemic stroke following mechanical thrombectomy in the United States

BACKGROUND AND PURPOSE

Predicting patient recovery and discharge disposition following mechanical thrombectomy remains a challenge in patients with ischemic stroke. Machine learning offers a promising prognostication approach assisting in personalized post-thrombectomy care plans and resource allocation. As a large national database, National Inpatient Sample (NIS), contain valuable insights amenable to data-mining. The study aimed to develop and evaluate ML models predicting hospital discharge disposition with a focus on demographic, socioeconomic and hospital characteristics.

MATERIALS AND METHODS

The NIS dataset (2006-2019) was used, including 4956 patients diagnosed with ischemic stroke who underwent thrombectomy. Demographics, hospital characteristics, and Elixhauser comorbidity indices were recorded. Feature extraction, processing, and selection were performed using Python, with Maximum Relevance - Minimum Redundancy (MRMR) applied for dimensionality reduction. ML models were developed and benchmarked prior to interpretation of the best model using Shapley Additive exPlanations (SHAP).

RESULTS

The multilayer perceptron model outperformed others and achieved an AUROC of 0.81, accuracy of 77 %, F1-score of 0.48, precision of 0.64, and recall of 0.54. SHAP analysis identified the most important features for predicting discharge disposition as dysphagia and dysarthria, NIHSS, age, primary payer (Medicare), cerebral edema, fluid and electrolyte disorders, complicated hypertension, primary payer (private insurance), intracranial hemorrhage, and thrombectomy alone.

CONCLUSION

Machine learning modeling of NIS database shows potential in predicting hospital discharge disposition for inpatients with acute ischemic stroke following mechanical thrombectomy in the NIS database. Insights gained from SHAP interpretation can inform targeted interventions and care plans, ultimately enhancing patient outcomes and resource allocation.

Robotic-assisted percutaneous coronary intervention: experience in Switzerland

Aims of the study

Percutaneous coronary intervention (PCI) exposes operators to ionizing radiation. Robotic-assisted PCI (RA-PCI) is a novel technology that enables interventional cardiologists to operate coronary devices remotely from a radiation-shed cockpit. The aim of this study is to describe the experience and challenges during the initiation of a RA-PCI program and to report outcomes of the first 21 patients undergoing RA-PCI in Switzerland.

Methods

All patients undergoing RA-PCI using the CorPath GRX Vascular Robotic System between 06/2021 and 12/2021 at Inselspital, Bern University Hospital were included in this retrospective registry study. Baseline, procedural and clinical follow-up data were prospectively assessed as part of the Cardiobase Bern PCI registry (NCT02241291). The two endpoints of interest were clinical success [defined as <30% residual diameter stenosis in the absence of in-hospital major adverse cardiovascular events (MACE: composite of death, periprocedural myocardial infarction, target-vessel revascularization, and stroke)] and robotic success (defined as clinical success and completion of RA-PCI without or with partial manual assistance). Additional outcome measures include clinical long-term outcomes at one year.

Results

Twenty-five lesions in 21 patients were treated with RA-PCI (age 62.4 ± 9.1 years, 24% female). Clinical success was achieved in 100%, and robotic success in 81% (17/21 procedures, including 4 procedures requiring partial manual assistance). Manual conversion (e.g. manual completion of the procedure) occurred in 19% (4 procedures). Reasons for manual assistance or conversion were poor guiding-catheter back-up or platform limitations (4), adverse events (2x transient slow-flow that was solved manually), safety decision (1x vasovagal reaction not related to robotic approach), and software error (1). No in-hospital MACE occurred. During 12 months of follow-up, one patient suffered a non-target-vessel myocardial infarction requiring repeat PCI.

Conclusions

RA-PCI can safely be performed without clinically relevant robot-associated complications in selected patients with approximately 80% of procedures conducted without or with partial manual assistance.

The Advent of Artificial Intelligence in Diabetic Foot Medicine: A New Horizon, a New Order, or a False Dawn?

In a world where automation is becoming increasingly common, easier collection of mass of data and powerful computer processing has meant a transformation in the field of artificial intelligence (AI). The diabetic foot is a multifactorial problem; its issues render it suitable for analysis, interrogation, and development of AI. The latter has the potential to deliver many solutions to issues of delayed diagnosis, compliance, and defining preventative treatments. We describe the use of AI and the development of artificial neural networks that may supplement the failed networks in the diabetic foot. The potential of this technology, current developing applications, and their limitations for diabetic foot care are suggested.

Machine learning vs. conventional methods for prediction of 30-day readmission following percutaneous mitral edge-to-edge repair

BACKGROUND

Identifying predictors of readmissions after mitral valve transcatheter edge-to-edge repair (MV-TEER) is essential for risk stratification and optimization of clinical outcomes.

AIMS

We investigated the performance of machine learning [ML] algorithms vs. logistic regression in predicting readmissions after MV-TEER.

METHODS

We utilized the National-Readmission-Database to identify patients who underwent MV-TEER between 2015 and 2018. The database was randomly split into training (70 %) and testing (30 %) sets. Lasso regression was used to remove non-informative variables and rank informative ones. The top 50 informative predictors were tested using 4 ML models: ML-logistic regression [LR], Naive Bayes [NB], random forest [RF], and artificial neural network [ANN]/For comparison, we used a traditional statistical method (principal component analysis logistic regression PCA-LR).

RESULTS

A total of 9425 index hospitalizations for MV-TEER were included. Overall, the 30-day readmission rate was 14.6 %, and heart failure was the most common cause of readmission (32 %). The readmission cohort had a higher burden of comorbidities (median Elixhauser score 5 vs. 3) and frailty score (3.7 vs. 2.9), longer hospital stays (3 vs. 2 days), and higher rates of non-home discharges (17.4 % vs. 8.5 %). The traditional PCA-LR model yielded a modest predictive value (area under the curve [AUC] 0.615 [0.587-0.644]). Two ML algorithms demonstrated superior performance than the traditional PCA-LR model; ML-LR (AUC 0.692 [0.667-0.717]), and NB (AUC 0.724 [0.700-0.748]). RF (AUC 0.62 [0.592-0.677]) and ANN (0.65 [0.623-0.677]) had modest performance.

CONCLUSION

Machine learning algorithms may provide a useful tool for predicting readmissions after MV-TEER using administrative databases.

Barriers and Facilitators of Artificial Intelligence in Family Medicine: An Empirical Study With Physicians in Saudi Arabia

BACKGROUND

Artificial intelligence (AI) is a novel technology that has been widely acknowledged for its potential to improve the processes' efficiency across industries. However, its barriers and facilitators in healthcare are not completely understood due to its novel nature.

STUDY PURPOSE

The purpose of this study is to explore the intricate landscape of AI use in family medicine, aiming to uncover the factors that either hinder or enable its successful adoption.

METHODS

A cross-sectional survey design is adopted in this study. The questionnaire included 10 factors (performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, trust, perceived privacy risk, personal innovativeness, ethical concerns, and facilitators) affecting the acceptance of AI. A total of 157 family physicians participated in the online survey.

RESULTS

Effort expectancy (μ = 3.85) and facilitating conditions (μ = 3.77) were identified to be strong influence factors. Access to data (μ = 4.33), increased computing power (μ = 3.92), and telemedicine (μ = 3.78) were identified as major facilitators; regulatory support (μ = 2.29) and interoperability standards (μ = 2.71) were identified as barriers along with privacy and ethical concerns. Younger individuals tend to have more positive attitudes and expectations toward AI-enabled assistants compared to older participants (p < .05). Perceived privacy risk is negatively correlated with all factors.

CONCLUSION

Although there are various barriers and concerns regarding the use of AI in healthcare, the preference for AI use in healthcare, especially family medicine, is increasing.

Artificial Intelligence, Computational Simulations, and Extended Reality in Cardiovascular Interventions

Artificial intelligence, computational simulations, and extended reality, among other 21st century computational technologies, are changing the health care system. To collectively highlight the most recent advances and benefits of artificial intelligence, computational simulations, and extended reality in cardiovascular therapies, we coined the abbreviation AISER. The review particularly focuses on the following applications of AISER: 1) preprocedural planning and clinical decision making; 2) virtual clinical trials, and cardiovascular device research, development, and regulatory approval; and 3) education and training of interventional health care professionals and medical technology innovators. We also discuss the obstacles and constraints associated with the application of AISER technologies, as well as the proposed solutions. Interventional health care professionals, computer scientists, biomedical engineers, experts in bioinformatics and visualization, the device industry, ethics committees, and regulatory agencies are expected to streamline the use of AISER technologies in cardiovascular interventions and medicine in general.

TAVI-PREP: A Deep Learning-Based Tool for Automated Measurements Extraction in TAVI Planning

BACKGROUND

Transcatheter aortic valve implantation (TAVI) is a less invasive alternative to open-heart surgery for treating severe aortic stenosis. Despite its benefits, the risk of procedural complications necessitates careful preoperative planning.

METHODS

This study proposes a fully automated deep learning-based method, TAVI-PREP, for pre-TAVI planning, focusing on measurements extracted from computed tomography (CT) scans. The algorithm was trained on the public MM-WHS dataset and a small subset of private data. It uses MeshDeformNet for 3D surface mesh generation and a 3D Residual U-Net for landmark detection. TAVI-PREP is designed to extract 22 different measurements from the aortic valvular complex. A total of 200 CT-scans were analyzed, and automatic measurements were compared to the ones made manually by an expert cardiologist. A second cardiologist analyzed 115 scans to evaluate inter-operator variability.

RESULTS

High Pearson correlation coefficients between the expert and the algorithm were obtained for most parameters (0.90-0.97), except for left and right coronary height (0.8 and 0.72, respectively). Similarly, the mean absolute relative error was within 5% for most measurements, except for left and right coronary height (11.6% and 16.5%, respectively). A greater consensus was observed among experts than when compared to the automatic approach, with TAVI-PREP showing no discernable bias towards either the lower or higher ends of the measurement spectrum.

CONCLUSIONS

TAVI-PREP provides reliable and time-efficient measurements of the aortic valvular complex that could aid clinicians in the preprocedural planning of TAVI procedures.

Artificial Intelligence in Facial Plastic Surgery: A Review of Current Applications, Future Applications, and Ethical Considerations

From virtual chat assistants to self-driving cars, artificial intelligence (AI) is often heralded as the technology that has and will continue to transform this generation. Among widely adopted applications in other industries, its potential use in medicine is being increasingly explored, where the vast amounts of data present in electronic health records and need for continuous improvements in patient care and workflow efficiency present many opportunities for AI implementation. Indeed, AI has already demonstrated capabilities for assisting in tasks such as documentation, image classification, and surgical outcome prediction. More specifically, this technology can be harnessed in facial plastic surgery, where the unique characteristics of the field lends itself well to specific applications. AI is not without its limitations, however, and the further adoption of AI in medicine and facial plastic surgery must necessarily be accompanied by discussion on the ethical implications and proper usage of AI in healthcare. In this article, we review current and potential uses of AI in facial plastic surgery, as well as its ethical ramifications.

Home-Based Rehabilitation of the Shoulder Using Auxiliary Systems and Artificial Intelligence: An Overview

Advancements in modern medicine have bolstered the usage of home-based rehabilitation services for patients, particularly those recovering from diseases or conditions that necessitate a structured rehabilitation process. Understanding the technological factors that can influence the efficacy of home-based rehabilitation is crucial for optimizing patient outcomes. As technologies continue to evolve rapidly, it is imperative to document the current state of the art and elucidate the key features of the hardware and software employed in these rehabilitation systems. This narrative review aims to provide a summary of the modern technological trends and advancements in home-based shoulder rehabilitation scenarios. It specifically focuses on wearable devices, robots, exoskeletons, machine learning, virtual and augmented reality, and serious games. Through an in-depth analysis of existing literature and research, this review presents the state of the art in home-based rehabilitation systems, highlighting their strengths and limitations. Furthermore, this review proposes hypotheses and potential directions for future upgrades and enhancements in these technologies. By exploring the integration of these technologies into home-based rehabilitation, this review aims to shed light on the current landscape and offer insights into the future possibilities for improving patient outcomes and optimizing the effectiveness of home-based rehabilitation programs.

Bibliometric analysis of 100 top cited articles of heart failure-associated diseases in combination with machine learning

Objective

The aim of this paper is to analyze the application of machine learning in heart failure-associated diseases using bibliometric methods and to provide a dynamic and longitudinal bibliometric analysis of heart failure-related machine learning publications.

Materials and methods

Web of Science was screened to gather the articles for the study. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility. Intuitive data analysis was employed to analyze the top-100 cited articles and VOSViewer was used to analyze the relevance and impact of all articles. The two analysis methods were then compared to get conclusions.

Results

The search identified 3,312 articles. In the end, 2,392 papers were included in the study, which were published between 1985 and 2023. All articles were analyzed using VOSViewer. Key points of the analysis included the co-authorship map of authors, countries and organizations, the citation map of journal and documents and a visualization of keyword co-occurrence analysis. Among these 100 top-cited papers, with a mean of 122.9 citations, the most-cited article had 1,189, and the least cited article had 47. Harvard University and the University of California topped the list among all institutes with 10 papers each. More than one-ninth of the authors of these 100 top-cited papers wrote three or more articles. The 100 articles came from 49 journals. The articles were divided into seven areas according to the type of machine learning approach employed: Support Vector Machines, Convolutional Neural Networks, Logistic Regression, Recurrent Neural Networks, Random Forest, Naive Bayes, and Decision Tree. Support Vector Machines were the most popular method.

Conclusions

This analysis provides a comprehensive overview of the artificial intelligence (AI)-related research conducted in the field of heart failure, which helps healthcare institutions and researchers better understand the prospects of AI in heart failure and formulate more scientific and effective research plans. In addition, our bibliometric evaluation can assist healthcare institutions and researchers in determining the advantages, sustainability, risks, and potential impacts of AI technology in heart failure.

Development and validation of explainable machine learning models for risk of mortality in transcatheter aortic valve implantation: TAVI risk machine scores

Aims

Identification of high-risk patients and individualized decision support based on objective criteria for rapid discharge after transcatheter aortic valve implantation (TAVI) are key requirements in the context of contemporary TAVI treatment. This study aimed to predict 30-day mortality following TAVI based on machine learning (ML) using data from the German Aortic Valve Registry.

Methods and results

Mortality risk was determined using a random forest ML model that was condensed in the newly developed TAVI Risk Machine (TRIM) scores, designed to represent clinically meaningful risk modelling before (TRIMpre) and in particular after (TRIMpost) TAVI. Algorithm was trained and cross-validated on data of 22 283 patients (729 died within 30 days post-TAVI) and generalisation was examined on data of 5864 patients (146 died). TRIMpost demonstrated significantly better performance than traditional scores [C-statistics value, 0.79; 95% confidence interval (CI)] [0.74; 0.83] compared to Society of Thoracic Surgeons (STS) with C-statistics value 0.69; 95%-CI [0.65; 0.74]). An abridged (aTRIMpost) score comprising 25 features (calculated using a web interface) exhibited significantly higher performance than traditional scores (C-statistics value, 0.74; 95%-CI [0.70; 0.78]). Validation on external data of 6693 patients (205 died within 30 days post-TAVI) of the Swiss TAVI Registry confirmed significantly better performance for the TRIMpost (C-statistics value 0.75, 95%-CI [0.72; 0.79]) compared to STS (C-statistics value 0.67, CI [0.63; 0.70]).

Conclusion

TRIM scores demonstrate good performance for risk estimation before and after TAVI. Together with clinical judgement, they may support standardised and objective decision-making before and after TAVI.

Role of artificial intelligence and machine learning in interventional cardiology

Directed by two decades of technological processes and remodeling, the dynamic quality of healthcare data combined with the progress of computational power has allowed for rapid progress in artificial intelligence (AI). In interventional cardiology, AI has shown potential in providing data interpretation and automated analysis from electrocardiogram (ECG), echocardiography, computed tomography angiography (CTA), magnetic resonance imaging (MRI), and electronic patient data. Clinical decision support has the potential to assist in improving patient safety and making prognostic and diagnostic conjectures in interventional cardiology procedures. Robot-assisted percutaneous coronary intervention (R-PCI), along with functional and quantitative assessment of coronary artery ischemia and plaque burden on intravascular ultrasound (IVUS), are the major applications of AI. Machine learning (ML) algorithms are used in these applications, and they have the potential to bring a paradigm shift in intervention. Recently, an efficient branch of ML has emerged as a deep learning algorithm for numerous cardiovascular (CV) applications. However, the impact DL on the future of cardiology practice is not clear. Predictive models based on DL have several limitations including low generalizability and decision processing in cardiac anatomy.

Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries

Artificial intelligence (AI) and machine learning (ML) is a promising field of cardiovascular medicine. Many AI tools have been shown to be efficacious with a high level of accuracy. Yet, their use in real life is not well established. In the era of health technology and data science, it is crucial to consider how these tools could improve healthcare delivery. This is particularly important in countries with limited resources, such as low- and middle-income countries (LMICs). LMICs have many barriers in the care continuum of cardiovascular diseases (CVD), and big portion of these barriers come from scarcity of resources, mainly financial and human power constraints. AI/ML could potentially improve healthcare delivery if appropriately applied in these countries. Expectedly, the current literature lacks original articles about AI/ML originating from these countries. It is important to start early with a stepwise approach to understand the obstacles these countries face in order to develop AI/ML-based solutions. This could be detrimental to many patients' lives, in addition to other expected advantages in other sectors, including the economy sector. In this report, we aim to review what is known about AI/ML in cardiovascular medicine, and to discuss how it could benefit LMICs.

Digital medicine: the next big leap advancing cardiovascular science

Solid clinical and academic leadership remains necessary to ensure that healthcare based on digital technologies is relevant, meaningful, and stands on the best possible evidence. This compendium accompanying the "Digital Technologies in Cardiovascular Disorders" article collection in BMC Cardiovascular Disorders summarizes recent knowledge about robust and advanced digital tools for preventing, monitoring, diagnosing, and treating cardiovascular diseases.

Prediction of Foot Ulcers Using Artificial Intelligence for Diabetic Patients at Cairo University Hospital, Egypt

Introduction

In Egypt, diabetic foot ulcers markedly contribute to the morbidity and mortality of diabetic patients. Accurately predicting the risk of diabetic foot ulcers could dramatically reduce the enormous burden of amputation.

Objective

The aim of this study is to design an artificial intelligence-based artificial neural network and decision tree algorithms for the prediction of diabetic foot ulcers.

Methods

A case-control study design was utilized to fulfill the aim of this study. The study was conducted at the National Institute of Diabetes and Endocrine Glands, Cairo University Hospital, Egypt. A purposive sample of 200 patients was included. The tool developed and used by the researchers was a structured interview questionnaire including three parts: Part I: demographic characteristics; Part II: medical data; and Part III: in vivo measurements. Artificial intelligence methods were used to achieve the aim of this study.

Results

The researchers used 19 significant attributes based on medical history and foot images that affect diabetic foot ulcers and then proposed two classifiers to predict the foot ulcer: a feedforward neural network and a decision tree. Finally, the researchers compared the results between the two classifiers, and the experimental results showed that the proposed artificial neural network outperformed a decision tree, achieving an accuracy of 97% in the automated prediction of diabetic foot ulcers.

Conclusion

Artificial intelligence methods can be used to predict diabetic foot ulcers with high accuracy. The proposed technique utilizes two methods to predict the foot ulcer; after evaluating the two methods, the artificial neural network showed a higher improvement in performance than the decision tree algorithm. It is recommended that diabetic outpatient clinics develop health education and follow-up programs to prevent complications from diabetes.

The Ongoing Saga of the Evolution of Percutaneous Coronary Intervention: From Balloon Angioplasty to Recent Innovations to Future Prospects

The advances in percutaneous coronary intervention (PCI) have been, above all, dependent on the work of pioneers in surgery, radiology, and interventional cardiology. From Grüntzig's first balloon angioplasty, PCI has expanded through technology development, improved protocols, and dissemination of best-practice techniques. We can nowadays treat more complex lesions in higher-risk patients with favourable results. Guide wires, balloon types and profiles, debulking techniques such as atherectomy or lithotripsy, stents, and scaffolds all represent evolutions that have allowed us to tackle complex lesions such as an unprotected left main coronary artery, complex bifurcations, or chronic total occlusions. Best-practice PCI, including physiology assessment, imaging, and optimal lesion preparation are now the gold standard when performing PCI for sound indications, and new technologies such as intravascular lithotripsy for lesion preparation, or artificial intelligence, are innovations in the steps of 4 decades of pioneers to improve patient care in interventional cardiology. In the present review, major innovations in PCI since the first balloon angioplasty and also uncertainties and obstacles inherent to such medical advances are described.

Applications of Machine Learning in Cardiology

In this digital era, artificial intelligence (AI) is establishing a strong foothold in commercial industry and the field of technology. These effects are trickling into the healthcare industry, especially in the clinical arena of cardiology. Machine learning (ML) algorithms are making substantial progress in various subspecialties of cardiology. This will have a positive impact on patient care and move the field towards precision medicine. In this review article, we explore the progress of ML in cardiovascular imaging, electrophysiology, heart failure, and interventional cardiology.

Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography

Cardiovascular disease is a leading cause of death among cancer survivors, second only to cancer recurrence or development of new tumors. Cardio-oncology has therefore emerged as a relatively new specialty focused on prevention and management of cardiovascular consequences of cancer therapies. Yet challenges remain regarding precision and accuracy with predicting individuals at highest risk for cardiotoxicity. Barriers such as access to care also limit screening and early diagnosis to improve prognosis. Thus, developing innovative approaches for prediction and early detection of cardiovascular illness in this population is critical. In this review, we provide an overview of the present state of machine learning applications in cardio-oncology. We begin by outlining some factors that should be considered while utilizing machine learning algorithms. We then examine research in which machine learning has been applied to improve prediction of cardiac dysfunction in cancer survivors. We also highlight the use of artificial intelligence (AI) in conjunction with electrocardiogram (ECG) to predict cardiac malfunction and also atrial fibrillation (AF), and we discuss the potential role of wearables. Additionally, the article summarizes future prospects and critical takeaways for the application of machine learning in cardio-oncology. This study is the first in a series on artificial intelligence in cardio-oncology, and complements our manuscript on echocardiography and other forms of imaging relevant to cancer survivors cared for in cardiology clinical practice.

Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning

BACKGROUND

Cardiovascular disorders in general are responsible for 30% of deaths worldwide. Among them, hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease that is present in about 1 of 500 young adults and can cause sudden cardiac death (SCD).

OBJECTIVE

Although the current state-of-the-art methods model the risk of SCD for patients, to the best of our knowledge, no methods are available for modeling the patient's clinical status up to 10 years ahead. In this paper, we propose a novel machine learning (ML)-based tool for predicting disease progression for patients diagnosed with HCM in terms of adverse remodeling of the heart during a 10-year period.

METHODS

The method consisted of 6 predictive regression models that independently predict future values of 6 clinical characteristics: left atrial size, left atrial volume, left ventricular ejection fraction, New York Heart Association functional classification, left ventricular internal diastolic diameter, and left ventricular internal systolic diameter. We supplemented each prediction with the explanation that is generated using the Shapley additive explanation method.

RESULTS

The final experiments showed that predictive error is lower on 5 of the 6 constructed models in comparison to experts (on average, by 0.34) or a consortium of experts (on average, by 0.22). The experiments revealed that semisupervised learning and the artificial data from virtual patients help improve predictive accuracies. The best-performing random forest model improved R² from 0.3 to 0.6.

CONCLUSIONS

By engaging medical experts to provide interpretation and validation of the results, we determined the models' favorable performance compared to the performance of experts for 5 of 6 targets.

Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble

Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep Learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed.

Challenges of Implementing Artificial Intelligence in Interventional Radiology

Artificial intelligence (AI) and deep learning (DL) remains a hot topic in medicine. DL is a subcategory of machine learning that takes advantage of multiple layers of interconnected neurons capable of analyzing immense amounts of data and "learning" patterns and offering predictions. It appears to be poised to fundamentally transform and help advance the field of diagnostic radiology, as heralded by numerous published use cases and number of FDA-cleared products. On the other hand, while multiple publications have touched upon many great hypothetical use cases of AI in interventional radiology (IR), the actual implementation of AI in IR clinical practice has been slow compared with the diagnostic world. In this article, we set out to examine a few challenges contributing to this scarcity of AI applications in IR, including inherent specialty challenges, regulatory hurdles, intellectual property, raising capital, and ethics. Owing to the complexities involved in implementing AI in IR, it is likely that IR will be one of the late beneficiaries of AI. In the meantime, it would be worthwhile to continuously engage in defining clinically relevant use cases and focus our limited resources on those that would benefit our patients the most.

A Primer on the Present State and Future Prospects for Machine Learning and Artificial Intelligence Applications in Cardiology

The artificial intelligence (AI) revolution is well underway, including in the medical field, and has dramatically transformed our lives. An understanding of the basics of AI applications, their development, and challenges to their clinical implementation is important for clinicians to fully appreciate the possibilities of AI. Such a foundation would ensure that clinicians have a good grasp and realistic expectations for AI in medicine and prevent discrepancies between the promised and real-world impact. When quantifying the track record for AI applications in cardiology, we found that a substantial number of AI systems are never deployed in clinical practice, although there certainly are many success stories. Successful implementations shared the following: they came from clinical areas where large amount of training data was available; were deployable into a single diagnostic modality; prediction models generally had high performance on external validation; and most were developed as part of collaborations with medical device manufacturers who had substantial experience with implementation of new technology. When looking into the current processes used for developing AI-based systems, we suggest that expanding the analytic framework to address potential deployment and implementation issues at project outset will improve the rate of successful implementation, and will be a necessary next step for AI to achieve its full potential in cardiovascular medicine.

Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence

Artificial Intelligence (AI) is the simulation of human intelligence in machines so they can perform various actions and execute decision-making. Machine learning (ML), a branch of AI, can analyse information from data and discover novel patterns. AI and ML are rapidly gaining prominence in healthcare as data become increasingly complex. These algorithms can enhance the role of cardiovascular imaging by automating many tasks or calculations, find new patterns or phenotypes in data and provide alternative diagnoses. In interventional cardiology, AI can assist in intraprocedural guidance, intravascular imaging and provide additional information to the operator. AI is slowly expanding its boundaries into interventional cardiology and can fundamentally alter the field. In this review, the authors discuss how AI can enhance the role of cardiovascular imaging and imaging in interventional cardiology.

Robotics, imaging, and artificial intelligence in the catheterisation laboratory

The catheterisation laboratory today combines diagnosis and therapeutics, through various imaging modalities and a prolific list of interventional tools, led by balloons and stents. In this review, we focus primarily on advances in image-based coronary interventions. The X-ray images that are the primary modality for diagnosis and interventions are combined with novel tools for visualisation and display, including multi-imaging co-registration modalities with three- and four-dimensional presentations. Interpretation of the physiologic significance of coronary stenosis based on prior angiographic images is being explored and implemented. Major efforts to reduce X-ray exposure to the staff and the patients, using computer-based algorithms for image processing, and novel methods to limit the radiation spread are being explored. The use of artificial intelligence (AI) and machine learning for better patient care requires attention to universal methods for sharing and combining large data sets and for allowing interpretation and analysis of large cohorts of patients. Barriers to data sharing using integrated and universal protocols should be overcome to allow these methods to become widely applicable. Robotic catheterisation takes the physician away from the ionising radiation spot, enables coronary angioplasty and stenting without compromising safety, and may allow increased precision. Remote coronary procedures over the internet, that have been explored in virtual and animal studies and already applied to patients in a small pilot study, open possibilities for sharing experience across the world without travelling. Application of those technologies to neurovascular, and particularly stroke interventions, may be very timely in view of the need for expert neuro-interventionalists located mostly in central areas.

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people's lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

A simplified approach to identification of risk status in patients with atherosclerotic cardiovascular disease

OBJECTIVE

The 2018 American Heart Association/American College of Cardiology (AHA/ACC) Blood Cholesterol Guideline recommendation to classify patients with atherosclerotic cardiovascular disease (ASCVD) as very high-risk (VHR) vs not-VHR (NVHR) has important implications for escalation of medical therapy. We aimed to define the prevalence and clinical characteristics of these two groups within a large multi-state healthcare system and develop a simpler means to assist clinicians in identifying VHR patients using classification and regression tree (CART) analysis.

METHODS

We performed a retrospective analysis of all patients in a 28-hospital US healthcare system in 2018. ICD-10 codes were used to define the ASCVD population. Per the AHA/ACC Guideline, VHR status was defined by ≥2 major ASCVD events or 1 major ASCVD event and ≥2 high-risk conditions. CART analysis was performed on training and validation datasets. A random forest model was used to verify results.

RESULTS

Of 180,669 ASCVD patients identified, 58% were VHR. Among patients with a history of myocardial infarction (MI) or recent acute coronary syndrome (ACS), 99% and 96% were classified as VHR, respectively. Both CART and random forest models identified recent ACS, ischemic stroke, hypertension, peripheral artery disease, history of MI, and age as the most important predictors of VHR status. Using five rules identified by CART analysis, fewer than 50% of risk factors were required to assign VHR status.

CONCLUSION

CART analysis helped to streamline the identification of VHR patients based on a limited number of rules and risk factors. This approach may help improve clinical decision making by simplifying ASCVD risk assessment at the point of care. Further validation is needed, however, in more diverse populations.

A review of machine learning for cardiology

This paper reviews recent cardiology literature and reports how Artificial Intelligence Tools (specifically, Machine Learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine Learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in Machine Learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying Machine Learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while Neural Networks are slowly being incorporated in cardiovascular research, other important techniques such as Semi-Supervised Learning and Federated Learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.

Comparison among random forest, logistic regression, and existing clinical risk scores for predicting outcomes in patients with atrial fibrillation: A report from the J-RHYTHM registry

Background

Machine learning (ML) has emerged as a promising tool for risk stratification. However, few studies have applied ML to risk assessment of patients with atrial fibrillation (AF).

Hypothesis

We aimed to compare the performance of random forest (RF), logistic regression (LR), and conventional risk schemes in predicting the outcomes of AF.

Methods

We analyzed data from 7406 nonvalvular AF patients (median age 71 years, female 29.2%) enrolled in a nationwide AF registry (J‐RHYTHM Registry) and who were followed for 2 years. The endpoints were thromboembolisms, major bleeding, and all‐cause mortality. Models were generated from potential predictors using an RF model, stepwise LR model, and the thromboembolism (CHADS₂ and CHA₂DS₂‐VASc) and major bleeding (HAS‐BLED, ORBIT, and ATRIA) scores.

Results

For thromboembolisms, the C‐statistic of the RF model was significantly higher than that of the LR model (0.66 vs. 0.59, p = .03) or CHA₂DS₂‐VASc score (0.61, p < .01). For major bleeding, the C‐statistic of RF was comparable to the LR (0.69 vs. 0.66, p = .07) and outperformed the HAS‐BLED (0.61, p < .01) and ATRIA (0.62, p < .01) but not the ORBIT (0.67, p = .07). The C‐statistic of RF for all‐cause mortality was comparable to the LR (0.78 vs. 0.79, p = .21). The calibration plot for the RF model was more aligned with the observed events for major bleeding and all‐cause mortality.

Conclusions

The RF model performed as well as or better than the LR model or existing clinical risk scores for predicting clinical outcomes of AF.

Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

Artificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

Artificial Intelligence and Transcatheter Interventions for Structural Heart Disease: A glance at the (near) future

With innovations in therapeutic technologies and changes in population demographics, transcatheter interventions for structural heart disease have become the preferred treatment and will keep growing. Yet, a thorough clinical selection and efficient pathway from diagnosis to treatment and follow-up are mandatory. In this review we reflect on how artificial intelligence may help to improve patient selection, pre-procedural planning, procedure execution and follow-up so to establish efficient and high quality health care in an increasing number of patients.

Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review

The role of physicians has always been to synthesize the data available to them to identify diagnostic patterns that guide treatment and follow response. Today, increasingly sophisticated machine learning algorithms may grow to support clinical experts in some of these tasks. Machine learning has the potential to benefit patients and cardiologists, but only if clinicians take an active role in bringing these new algorithms into practice. The aim of this review is to introduce clinicians who are not data science experts to key concepts in machine learning that will allow them to better understand the field and evaluate new literature and developments. The current published data in machine learning for cardiovascular disease is then summarized, using both a bibliometric survey, with code publicly available to enable similar analysis for any research topic of interest, and select case studies. Finally, several ways that clinicians can and must be involved in this emerging field are presented.

Big data and new information technology: what cardiologists need to know

Technological progress in medicine is constantly garnering pace, requiring that physicians constantly update their knowledge. The new wave of technologies breaking through into clinical practice includes the following: a) mHealth, which allows constant monitoring of biological parameters, anytime, anyplace, of hundreds of patients at the same time; b) artificial intelligence, which, powered by new deep learning techniques, are starting to beat human experts at their own game: diagnosis by imaging or electrocardiography; c) 3-dimensional printing, which may lead to patient-specific prostheses; d) systems medicine, which has arisen from big data, and which will open the way to personalized medicine by bringing together genetic, epigenetic, environmental, clinical and social data into complex integral mathematical models to design highly personalized therapies. This state-of-the-art review aims to summarize in a single document the most recent and most important technological trends that are being applied to cardiology, and to provide an overall view that will allow readers to discern at a glance the direction of cardiology in the next few years.

Artificial intelligence in cardiology

This review examines the current state and application of artificial intelligence (AI) and machine learning (ML) in cardiovascular medicine. AI is changing the clinical practice of medicine in other specialties. With progress continuing in this emerging technology, the impact for cardiovascular medicine is highlighted to provide insight for the practicing clinician and to identify potential patient benefits.

Integration of artificial intelligence into clinical patient management: focus on cardiac imaging

Cardiac imaging is a crucial component in the management of patients with heart disease, and as such it influences multiple, inter-related parts of the clinical workflow: physician-patient contact, image acquisition, image pre- and postprocessing, study reporting, diagnostics and outcome predictions, medical interventions, and, finally, knowledge-building through clinical research. With the gradual and ubiquitous infiltration of artificial intelligence into cardiology, it has become clear that, when used appropriately, it will influence and potentially improve-through automation, standardization and data integration-all components of the clinical workflow. This review aims to present a comprehensive view of full integration of artificial intelligence into the standard clinical patient management-with a focus on cardiac imaging, but applicable to all information handling-and to discuss current barriers that remain to be overcome before its widespread implementation and integration.

The application of big data to cardiovascular disease: paths to precision medicine

Advanced phenotyping of cardiovascular diseases has evolved with the application of high-resolution omics screening to populations enrolled in large-scale observational and clinical trials. This strategy has revealed that considerable heterogeneity exists at the genotype, endophenotype, and clinical phenotype levels in cardiovascular diseases, a feature of the most common diseases that has not been elucidated by conventional reductionism. In this discussion, we address genomic context and (endo)phenotypic heterogeneity, and examine commonly encountered cardiovascular diseases to illustrate the genotypic underpinnings of (endo)phenotypic diversity. We highlight the existing challenges in cardiovascular disease genotyping and phenotyping that can be addressed by the integration of big data and interpreted using novel analytical methodologies (network analysis). Precision cardiovascular medicine will only be broadly applied to cardiovascular patients once this comprehensive data set is subjected to unique, integrative analytical strategies that accommodate molecular and clinical heterogeneity rather than ignore or reduce it.