-
Acta Obstetricia Et Gynecologica... Nov 2023This study aims to investigate non-invasive electrocardiography as a method for the detection of congenital heart disease (CHD) with the help of artificial intelligence.
INTRODUCTION
This study aims to investigate non-invasive electrocardiography as a method for the detection of congenital heart disease (CHD) with the help of artificial intelligence.
MATERIAL AND METHODS
An artificial neural network was trained for the identification of CHD using non-invasively obtained fetal electrocardiograms. With the help of a Bayesian updating rule, multiple electrocardiographs were used to increase the algorithm's performance.
RESULTS
Using 122 measurements containing 65 healthy and 57 CHD cases, the accuracy, sensitivity, and specificity were found to be 71%, 63%, and 77%, respectively. The sensitivity was however 75% and 69% for CHD cases requiring an intervention in the neonatal period and first year of life, respectively. Furthermore, a positive effect of measurement length on the detection performance was observed, reaching optimal performance when using 14 electrocardiography segments (37.5 min) or more. A small negative trend between gestational age and accuracy was found.
CONCLUSIONS
The proposed method combining recent advances in obtaining non-invasive fetal electrocardiography with artificial intelligence for the automatic detection of CHD achieved a detection rate of 63% for all CHD and 75% for critical CHD. This feasibility study shows that detection rates of CHD might improve by using electrocardiography-based screening complementary to the standard ultrasound-based screening. More research is required to improve performance and determine the benefits to clinical practice.
Topics: Pregnancy; Female; Infant, Newborn; Humans; Artificial Intelligence; Bayes Theorem; Ultrasonography, Prenatal; Heart Defects, Congenital; Electrocardiography; Fetal Heart
PubMed: 37563851
DOI: 10.1111/aogs.14623 -
European Journal of Medical Research Jul 2023Artificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical... (Review)
Review
Artificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical field. AI techniques have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease and so on. In clinical scenario, AI have been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome prediction. Deeply developed to capture subtle connections from massive amounts of healthcare data, recent AI algorithms are expected to handle even more complex tasks than traditional methods. The aim of this review is to introduce current applications of AI in CVDs, which may allow clinicians who have limited expertise of computer science to better understand the frontier of the subject and put AI algorithms into clinical practice.
Topics: Humans; Cardiovascular Diseases; Artificial Intelligence; Algorithms; Heart Failure; Heart Valve Diseases
PubMed: 37475050
DOI: 10.1186/s40001-023-01065-y -
Current Heart Failure Reports Aug 2023The introduction of Artificial Intelligence into the healthcare system offers enormous opportunities for biomedical research, the improvement of patient care, and cost... (Review)
Review
PURPOSE OF REVIEW
The introduction of Artificial Intelligence into the healthcare system offers enormous opportunities for biomedical research, the improvement of patient care, and cost reduction in high-end medicine. Digital concepts and workflows are already playing an increasingly important role in cardiology. The fusion of computer science and medicine offers great transformative potential and enables enormous acceleration processes in cardiovascular medicine.
RECENT FINDINGS
As medical data becomes smart, it is also becoming more valuable and vulnerable to malicious actors. In addition, the gap between what is technically possible and what is allowed by privacy legislation is growing. Principles of the General Data Protection Regulation that have been in force since May 2018, such as transparency, purpose limitation, and data minimization, seem to hinder the development and use of Artificial Intelligence. Concepts to secure data integrity and incorporate legal and ethical principles can help to avoid the potential risks of digitization and may result in an European leadership in regard to privacy protection and AI. The following review provides an overview of relevant aspects of Artificial Intelligence and Machine Learning, highlights selected applications in cardiology, and discusses central ethical and legal considerations.
Topics: Humans; Artificial Intelligence; Heart Failure; Machine Learning; Cardiology; Delivery of Health Care
PubMed: 37291432
DOI: 10.1007/s11897-023-00606-0 -
Cardiovascular Diabetology Sep 2023Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess... (Review)
Review
Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess cardiovascular risk it poses. In this comprehensive review of machine learning applications in the care of patients with diabetes at increased cardiovascular risk, we offer a broad overview of various data-driven methods and how they may be leveraged in developing predictive models for personalized care. We review existing as well as expected artificial intelligence solutions in the context of diagnosis, prognostication, phenotyping, and treatment of diabetes and its cardiovascular complications. In addition to discussing the key properties of such models that enable their successful application in complex risk prediction, we define challenges that arise from their misuse and the role of methodological standards in overcoming these limitations. We also identify key issues in equity and bias mitigation in healthcare and discuss how the current regulatory framework should ensure the efficacy and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.
Topics: Humans; Artificial Intelligence; Cardiovascular Diseases; Risk Factors; Machine Learning; Diabetes Mellitus; Heart Disease Risk Factors
PubMed: 37749579
DOI: 10.1186/s12933-023-01985-3 -
International Journal of Heart Failure Jan 2024The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with... (Review)
Review
The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.
PubMed: 38303917
DOI: 10.36628/ijhf.2023.0050 -
Turk Kardiyoloji Dernegi Arsivi : Turk... Jan 2024Heart Failure (HF) is an important public health problem in Turkey and in the world. Hospitalizations due to HF decompensation are associated with increased mortality.... (Review)
Review
Heart Failure (HF) is an important public health problem in Turkey and in the world. Hospitalizations due to HF decompensation are associated with increased mortality. The use of digital technologies, especially wearable technologies, is increasing. As physicians, with the use of these devices, patients could be closely followed up and hospitalization, mortality are tried to be prevented by increased awareness of decomposition before clinical symptoms or at the beginning of symptoms. In this review, digital biomarkers, digital technologies, remote monitoring systems and the evidence supporting their use, artificial intelligence applications and the reasons limiting their use of digital technologies in clinical practice will be discussed.
Topics: Humans; Digital Technology; Artificial Intelligence; Monitoring, Physiologic; Hospitalization; Heart Failure
PubMed: 38221836
DOI: 10.5543/tkda.2023.79776 -
Frontiers in Physiology 2023
PubMed: 37664424
DOI: 10.3389/fphys.2023.1272377 -
JACC. Cardiovascular Interventions Jul 2023Percutaneous ventricular assist devices (pVADs) are increasingly being used because of improved experience and availability. The Impella (Abiomed), a percutaneous... (Review)
Review
Percutaneous ventricular assist devices (pVADs) are increasingly being used because of improved experience and availability. The Impella (Abiomed), a percutaneous microaxial, continuous-flow, short-term ventricular assist device, requires meticulous postimplantation management to avoid the 2 most frequent complications, namely, bleeding and hemolysis. A standardized approach to the prevention, detection, and treatment of these complications is mandatory to improve outcomes. The risk for hemolysis is mostly influenced by pump instability, resulting from patient- or device-related factors. Upfront echocardiographic assessment, frequent monitoring, and prompt intervention are essential. The precarious hemostatic balance during pVAD support results from the combination of a procoagulant state, due to critical illness and contact pathway activation, together with a variety of factors aggravating bleeding risk. Preventive strategies and appropriate management, adapted to the impact of the bleeding, are crucial. This review offers a guide to physicians to tackle these device-related complications in this critically ill pVAD-supported patient population.
Topics: Humans; Treatment Outcome; Hemolysis; Percutaneous Coronary Intervention; Heart-Assist Devices; Hemorrhage; Shock, Cardiogenic
PubMed: 37495347
DOI: 10.1016/j.jcin.2023.05.043 -
Frontiers in Medical Technology 2023
PubMed: 38021438
DOI: 10.3389/fmedt.2023.1309784