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 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.

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.

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.

scholarly journals B-PO04-176 THE ARTIFICIAL INTELLIGENCE-ENABLED ENDOCARDIAL SIGNAL PREDICTION OF TERMINATION SITE DURING CATHETER ABLATION OF PERSISTENT ATRIAL FIBRILLATION

Heart Rhythm ◽  
2021 ◽  
Vol 18 (8) ◽  
pp. S350-S351
Author(s):  
Chih-Min Liu ◽  
Yenn-Jiang Lin ◽  
Men-Tzung Lo ◽  
Chia Hsin Chiang ◽  
Shih-Lin Chang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
Persistent Atrial Fibrillation ◽  
Signal Prediction

scholarly journals Clinical Outcomes of Computational Virtual Mapping-Guided Catheter Ablation in Patients With Persistent Atrial Fibrillation: A Multicenter Prospective Randomized Clinical Trial

2021 ◽  
Vol 8 ◽  
Author(s):  
Yong-Soo Baek ◽  
Oh-Seok Kwon ◽  
Byounghyun Lim ◽  
Song-Yi Yang ◽  
Je-Wook Park ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Clinical Trial ◽  
Catheter Ablation ◽  
Computational Modeling ◽  
Controlled Trial ◽  
Dominant Frequency ◽  
Complication Rates ◽  
Clinical Recurrence ◽  
Significant Difference

Background: Clinical recurrence after atrial fibrillation catheter ablation (AFCA) still remains high in patients with persistent AF (PeAF). We investigated whether an extra-pulmonary vein (PV) ablation targeting the dominant frequency (DF) extracted from electroanatomical map–integrated AF computational modeling improves the AFCA rhythm outcome in patients with PeAF.Methods: In this open-label, randomized, multi-center, controlled trial, 170 patients with PeAF were randomized at a 1:1 ratio to the computational modeling-guided virtual DF (V-DF) ablation and empirical PV isolation (E-PVI) groups. We generated a virtual dominant frequency (DF) map based on the atrial substrate map obtained during the clinical AF ablation procedure using computational modeling. This simulation was possible within the time of the PVI procedure. V-DF group underwent extra-PV V-DF ablation in addition to PVI, but DF information was not notified to the operators from the core lab in the E-PVI group.Results: After a mean follow-up period of 16.3 ± 5.3 months, the clinical recurrence rate was significantly lower in the V-DF than with E-PVI group (P = 0.018, log-rank). Recurrences appearing as atrial tachycardias (P = 0.145) and the cardioversion rates (P = 0.362) did not significantly differ between the groups. At the final follow-up, sinus rhythm was maintained without any AADs in 74.7% in the V-DF group and 48.2% in the E-PVI group (P < 0.001). No significant difference was found in the major complication rates (P = 0.489) or total procedure time (P = 0.513) between the groups. The V-DF ablation was independently associated with a reduced AF recurrence after AFCA [hazard ratio: 0.51 (95% confidence interval: 0.30–0.88); P = 0.016].Conclusions: The computational modeling-guided V-DF ablation improved the rhythm outcome of AFCA in patients with PeAF.Clinical Trial Registration: Clinical Research Information Service, CRIS identifier: KCT0003613.

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