scholarly journals Artificial Intelligence-Driven Algorithm for Drug Effect Prediction on Atrial Fibrillation: An in silico Population of Models Approach

2021 ◽  
Vol 12 ◽  
Author(s):  
Ana Maria Sanchez de la Nava ◽  
Ángel Arenal ◽  
Francisco Fernández-Avilés ◽  
Felipe Atienza
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
In Silico ◽  
Antiarrhythmic Drugs ◽  
Ionic Channel ◽  
Pattern Identification ◽  
Pore Channel ◽  
Atrial Size ◽  
And Diffusion ◽  
Fold Cross Validation

Background: Antiarrhythmic drugs are the first-line treatment for atrial fibrillation (AF), but their effect is highly dependent on the characteristics of the patient. Moreover, anatomical variability, and specifically atrial size, have also a strong influence on AF recurrence.Objective: We performed a proof-of-concept study using artificial intelligence (AI) that enabled us to identify proarrhythmic profiles based on pattern identification from in silico simulations.Methods: A population of models consisting of 127 electrophysiological profiles with a variation of nine electrophysiological variables (GNa, INaK, GK1, GCaL, GKur, IKCa, [Na]ext, and [K]ext and diffusion) was simulated using the Koivumaki atrial model on square planes corresponding to a normal (16 cm2) and dilated (22.5 cm2) atrium. The simple pore channel equation was used for drug implementation including three drugs (isoproterenol, flecainide, and verapamil). We analyzed the effect of every ionic channel combination to evaluate arrhythmia induction. A Random Forest algorithm was trained using the population of models and AF inducibility as input and output, respectively. The algorithm was trained with 80% of the data (N = 832) and 20% of the data was used for testing with a k-fold cross-validation (k = 5).Results: We found two electrophysiological patterns derived from the AI algorithm that was associated with proarrhythmic behavior in most of the profiles, where GK1 was identified as the most important current for classifying the proarrhythmicity of a given profile. Additionally, we found different effects of the drugs depending on the electrophysiological profile and a higher tendency of the dilated tissue to fibrillate (Small tissue: 80 profiles vs Dilated tissue: 87 profiles).Conclusion: Artificial intelligence algorithms appear as a novel tool for electrophysiological pattern identification and analysis of the effect of antiarrhythmic drugs on a heterogeneous population of patients with AF.

scholarly journals Computational modeling of atrial fibrillation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Oh-Seok Kwon ◽  
Inseok Hwang ◽  
Hui-Nam Pak
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
Computational Modeling ◽  
Computer Science ◽  
Precision Medicine ◽  
In Silico ◽  
Antiarrhythmic Drugs ◽  
Ablation Lesion ◽  
Aging Society

AbstractWith the aging society, the prevalence of atrial fibrillation (AF) continues to increase. Nevertheless, there are still limitations in antiarrhythmic drugs (AAD) or catheter interventions for AF. If it is possible to predict the outcome of AF management according to various AADs or ablation lesion sets through computational modeling, it will be of great clinical help. AF computational modeling has been utilized for in-silico arrhythmia research and enabled high-density entire chamber mapping, reproducible condition control, virtual intervention, not possible clinically or experimentally, in-depth mechanistic research. With the recent development of computer science and technology, more sophisticated and faster computational modeling has become available for clinical application. In particular, it can be applied to determine the extra-PV target of persistent AF catheter ablation or to select the AAD with the best effect. AF computational modeling combined with artificial intelligence is expected to contribute to precision medicine for more diverse uses in the future. Therefore, in this review, we will deal with the history, development, and various applications of computation modeling.

scholarly journals In-silico analysis of aging mechanisms of action potential remodeling in human atrial cardiomyocites

2020 ◽  
Vol 22 ◽  
pp. 01025
Author(s):  
Tatyana Nesterova ◽  
Dmitry Shmarko ◽  
Konstantin Ushenin ◽  
Olga Solovyova
Keyword(s):  
Experimental Data ◽  
Atrial Fibrillation ◽  
Action Potential ◽  
In Silico ◽  
Antiarrhythmic Drugs ◽  
Model Parameters ◽  
Experimental Conditions ◽  
Human Cardiomyocytes ◽  
Atrial Cardiomyocytes ◽  
Age Related

Electrophysiology of cardiomyocytes changes with aging. Agerelated ionic remodeling in cardiomyocytes may increase the incidence and prevalence of atrial fibrillation (AF) in the elderly and affect the efficiency of antiarrhythmic drugs. There is the deep lack of experimental data on an action potential and transmembrane currents recorded in the healthy human cardiomyocytes of different age. Experimental data in mammals is also incomplete and often contradicting depending on the experimental conditions. In this in-silico study, we used a population of ionic models of human atrial cardiomyocytes to transfer data on the age- related ionic remodeling in atrial cardiomyocytes from canines and mice to predict possible consequences for human cardiomyocyte activity. Based on experimental data, we analyzes two hypotheses on the aging effect on the ionic currents using two age-related sets of varied model parameters and evaluated corresponding changes in action potential morphology with aging. Using the two populations of aging models, we analyzed the agedependent sensitivity of atrial cardiomyocytes to Dofetilide which is one of the antiarrhythmic drugs widely used in patients with atrial fibrillation.

Utilisation of Robotic Navigation Systems in the Treatment of Atrial Fibrillation

2010 ◽  
Vol 6 (3) ◽  
pp. 60
Author(s):  
Richard Schilling ◽  
Keyword(s):  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
Navigation System ◽  
Antiarrhythmic Drugs ◽  
Rhythm Control ◽  
Navigation Systems ◽  
Robotic Navigation ◽  
Increased Risk ◽  
Common Strategy ◽  
Catheter Navigation

Atrial fibrillation (AF) is linked to an increased risk of adverse cardiovascular events. While rhythm control with antiarrhythmic drugs (AADs) is a common strategy for managing patients with AF, catheter ablation may be a more efficacious and safer alternative to AADs for sinus rhythm control. Conventional catheter ablation has been associated with challenges during the arrhythmia mapping and ablation stages; however, the introduction of two remote catheter navigation systems (a robotic and a magnetic navigation system) may potentially overcome these challenges. Initial clinical experience with the robotic navigation system suggests that it offers similar procedural times, efficacy and safety to conventional manual ablation. Furthermore, it has been associated with reduced fluoroscopy exposure to the patient and the operator as well as a shorter fluoroscopy time compared with conventional catheter ablation. In the future, the remote navigation systems may become routinely used for complex catheter ablation procedures.

Ion Channel Remodelling in Atrial Fibrillation

2011 ◽  
Vol 7 (2) ◽  
pp. 97 ◽  
Author(s):  
Niels Voigt ◽  
Dobromir Dobrev ◽  
◽  
Keyword(s):  
Atrial Fibrillation ◽  
Ion Channel ◽  
Antiarrhythmic Drugs ◽  
Current Knowledge ◽  
Bleeding Complications ◽  
Large Size ◽  
Cardiovascular Morbidity And Mortality ◽  
Number Of Patients ◽  
Life Threatening ◽  
Underlying Mechanisms

Atrial fibrillation (AF) is the most common arrhythmia and is associated with substantial cardiovascular morbidity and mortality, with stroke being the most critical complication. Present drugs used for the therapy of AF (antiarrhythmics and anticoagulants) have major limitations, including incomplete efficacy, risks of life-threatening proarrhythmic events and bleeding complications. Non-pharmacological ablation procedures are efficient and apparently safe, but the very large size of the patient population allows ablation treatment of only a small number of patients. These limitations largely result from limited knowledge about the underlying mechanisms of AF and there is a hope that a better understanding of the molecular basis of AF may lead to the discovery of safer and more effective therapeutic targets. This article reviews the current knowledge about AF-related ion-channel remodelling and discusses how these alterations might affect the efficacy of antiarrhythmic drugs.

Artificial Intelligence as a Business Partner in Cardiovascular Precision Medicine: an Emerging Approach for Disease Detection and Treatment Optimization

2020 ◽  
Vol 28 ◽  
Author(s):  
Valeria Visco ◽  
Germano Junior Ferruzzi ◽  
Federico Nicastro ◽  
Nicola Virtuoso ◽  
Albino Carrizzo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Heart Failure ◽  
Atrial Fibrillation ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Disease Detection ◽  
Physician Patient Relationship ◽  
Invasive Approach ◽  
Tailored Treatment ◽  
The Impact

Background: In the real world, medical practice is changing hand in hand with the development of new Artificial Intelligence (AI) systems and problems from different areas have been successfully solved using AI algorithms. Specifically, the use of AI techniques in setting up or building precision medicine is significant in terms of the accuracy of disease discovery and tailored treatment. Moreover, with the use of technology, clinical personnel can deliver a very much efficient healthcare service. Objective: This article reviews AI state-of-the-art in cardiovascular disease management, focusing on diagnostic and therapeutic improvements. Methods: To that end, we conducted a detailed PubMed search on AI application from distinct areas of cardiology: heart failure, arterial hypertension, atrial fibrillation, syncope and cardiovascular rehabilitation. Particularly, to assess the impact of these technologies in clinical decision-making, this research considers technical and medical aspects. Results: On one hand, some devices in heart failure, atrial fibrillation and cardiac rehabilitation represent an inexpensive, not invasive or not very invasive approach to long-term surveillance and management in these areas. On the other hand, the availability of large datasets (big data) is a useful tool to predict the development and outcome of many cardiovascular diseases. In summary, with this new guided therapy, the physician can supply prompt, individualised, and tailored treatment and the patients feel safe as they are continuously monitored, with a significant psychological effect. Conclusion: Soon, tailored patient care via telemonitoring can improve the clinical practice because AI-based systems support cardiologists in daily medical activities, improving disease detection and treatment. However, the physician-patient relationship remains a pivotal step.

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