Bucelli M, Zingaro A, Africa PC, Fumagalli I, Dede’ L, Quarteroni A. A mathematical model that integrates cardiac electrophysiology, mechanics, and fluid dynamics: Application to the human left heart. Int J Numer Method Biomed Eng. 2023;39(3):e3678.
Corrado C, Avezzù A, Lee AWC, Mendoca Costa C, Roney CH, Strocchi M, et al. Using cardiac ionic cell models to interpret clinical data. WIREs Mech Dis. 2021;13:e1508.
Article CAS PubMed Google Scholar
Despa S, Vigmond E. From single myocyte to whole heart: the Intricate Dance of Electrophysiology and modeling. Circ Res. 2016;118:184–6.
Article CAS PubMed PubMed Central Google Scholar
Ni H, Grandi E. Computational modeling of Cardiac Electrophysiology. Methods Mol Biol. 2024;2735:63–103.
Article CAS PubMed Google Scholar
Boyle PM, Zghaib T, Zahid S, Ali RL, Deng D, Franceschi WH et al. Computationally guided personalized targeted ablation of persistent atrial fibrillation. Nat Biomedical Eng. 2019.
Prakosa A, Arevalo HJ, Deng D, Boyle PM, Nikolov PP, Ashikaga H, et al. Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia. Nat Biomedical Eng. 2018;2:732–40.
Gaur N, Qi X-Y, Benoist D, Bernus O, Coronel R, Nattel S, et al. A computational model of pig ventricular cardiomyocyte electrophysiology and calcium handling: translation from pig to human electrophysiology. PLoS Comput Biol. 2021;17:e1009137.
Article CAS PubMed PubMed Central Google Scholar
Diprose WK, Buist N, Hua N, Thurier Q, Shand G, Robinson R. Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator. J Am Med Inf Assoc. 2020;27:592–600.
Lundberg S, Lee S-I. A Unified Approach to Interpreting Model Predictions [Internet]. arXiv; 2017 [cited 2024 Aug 28]. https://arxiv.org/abs/1705.07874
Simonyan K, Vedaldi A, Zisserman A. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps [Internet]. arXiv; 2013 [cited 2024 Aug 28]. https://arxiv.org/abs/1312.6034
Corrado C, Williams S, Roney C, Plank G, O’Neill M, Niederer S. Using machine learning to identify local cellular properties that support re-entrant activation in patient-specific models of atrial fibrillation. Europace. 2021;23:i12–20.
Article PubMed PubMed Central Google Scholar
Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18:203–11.
Article CAS PubMed Google Scholar
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.
Ruiz Herrera C, Grandits T, Plank G, Perdikaris P, Sahli Costabal F, Pezzuto S. Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps. Engineering with Computers. 2022;38:3957–73.
Gillette K, Gsell MAF, Nagel C, Bender J, Winkler B, Williams SE, et al. MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations. Sci Data. 2023;10:531.
Article PubMed PubMed Central Google Scholar
Rodero C, Strocchi M, Marciniak M, Longobardi S, Whitaker J, O’Neill MD, et al. Correction: linking statistical shape models and simulated function in the healthy adult human heart. PLoS Comput Biol. 2022;18:e1010196.
Article CAS PubMed PubMed Central Google Scholar
Gillette K, Gsell MAF, Strocchi M, Grandits T, Neic A, Manninger M, et al. A personalized real-time virtual model of whole heart electrophysiology. Front Physiol. 2022;13:907190.
Article PubMed PubMed Central Google Scholar
Turakhia MP, Guo JD, Keshishian A, Delinger R, Sun X, Ferri M, et al. Contemporary prevalence estimates of undiagnosed and diagnosed atrial fibrillation in the United States. Clin Cardiol. 2023;46:484–93.
Article PubMed PubMed Central Google Scholar
Joglar JA, Chung MK, Armbruster AL, Benjamin EJ, Chyou JY, Cronin EM et al. 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2024;149(1):e1-e156.
Chahine Y, Magoon MJ, Maidu B, Del Álamo JC, Boyle PM, Akoum N. Machine learning and the Conundrum of Stroke Risk Prediction. Arrhythm Electrophysiol Rev. 2023;12:e07.
Article PubMed PubMed Central Google Scholar
Bifulco SF, Macheret F, Scott GD, Akoum N, Boyle PM. Explainable Machine Learning to predict anchored reentry substrate created by Persistent Atrial Fibrillation ablation in computational models. J Am Heart Assoc. 2023;12:e030500.
Article PubMed PubMed Central Google Scholar
Feng Y, Dubois R, Hocini M, Vigmond EJ. Atrial periodic source spectrum from preoperative body surface potentials predicts long-term recurrence of Atrial Fibrillation. IEEE Trans Biomed Eng. 2023;70:2131–8.
Frerich S, Malik R, Georgakis MK, Sinner MF, Kittner SJ, Mitchell BD, et al. Cardiac risk factors for stroke: a comprehensive mendelian randomization study. Stroke. 2022;53:e130–5.
İçen YK, Koca H, Sümbül HE, Yıldırım A, Koca F, Yıldırım A, et al. Relationship between coarse F waves and thromboembolic events in patients with permanent atrial fibrillation. J Arrhythmia. 2020;36:1025–31.
Khurshid S, Friedman S, Reeder C, Di Achille P, Diamant N, Singh P, et al. ECG-Based deep learning and clinical risk factors to Predict Atrial Fibrillation. Circulation. 2022;145:122–33.
Article CAS PubMed Google Scholar
Lankveld T, Zeemering S, Scherr D, Kuklik P, Hoffmann BA, Willems S et al. Atrial Fibrillation Complexity Parameters Derived From Surface ECGs Predict Procedural Outcome and Long-Term Follow-Up of Stepwise Catheter Ablation for Atrial Fibrillation. Circ Arrhythm Electrophysiol. 2016;9(2):e003354.
Lip GYH, Tran G, Genaidy A, Marroquin P, Estes C, Landsheft J. Improving dynamic stroke risk prediction in non-anticoagulated patients with and without atrial fibrillation: comparing common clinical risk scores and machine learning algorithms. Eur Heart J - Qual Care Clin Outcomes. 2022;8:548–56.
McCann A, Vesin J-M, Pruvot E, Roten L, Sticherling C, Luca A. ECG-Based indices to characterize Persistent Atrial Fibrillation before and during stepwise catheter ablation. Front Physiol. 2021;12:654053.
Article PubMed PubMed Central Google Scholar
Serhal H, Abdallah N, Marion J-M, Chauvet P, Oueidat M, Humeau-Heurtier A. Overview on prediction, detection, and classification of atrial fibrillation using wavelets and AI on ECG. Comput Biol Med. 2022;142:105168.
Zingaro A, Ahmad Z, Kholmovski E, Sakata K, Dede’ L, Morris AK, et al. A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data. Sci Rep. 2024;14:9515.
Article CAS PubMed PubMed Central Google Scholar
Bifulco SF, Scott GD, Sarairah S, Birjandian Z, Roney CH, Niederer SA, et al. Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrate. eLife. 2021;10:e64213.
Article CAS PubMed PubMed Central Google Scholar
Telle Å, Bargellini C, Chahine Y, Del Álamo JC, Akoum N, Boyle PM. Personalized biomechanical insights in atrial fibrillation: opportunities & challenges. Expert Rev Cardiovasc Ther. 2023;1–21.
Macheret F, Bifulco SF, Scott GD, Kwan KT, Chahine Y, Afroze T, et al. Comparing inducibility of re-entrant arrhythmia in patient-specific computational models to clinical atrial fibrillation phenotypes. JACC Clin Electrophysiol. 2023;9:2149–62.
Sánchez J, Trenor B, Saiz J, Dössel O, Loewe A. Fibrotic remodeling during Persistent Atrial Fibrillation: in Silico Investigation of the role of Calcium for Human Atrial Myofibroblast Electrophysiology. Cells. 2021;10:2852.
Article PubMed PubMed Central Google Scholar
Dasí A, Roy A, Sachetto R, Camps J, Bueno-Orovio A, Rodriguez B. In-silico drug trials for precision medicine in atrial fibrillation: from ionic mechanisms to electrocardiogram-based predictions in structurally-healthy human atria. Front Physiol. 2022;13:966046.
Article PubMed PubMed Central Google Scholar
Dasí A, Nagel C, Pope MTB, Wijesurendra RS, Betts TR, Sachetto R, et al. Silico TRials guide optimal stratification of ATrIal FIbrillation patients to catheter ablation and pharmacological medicaTION: the i-STRATIFICATION study. Europace. 2024;26:euae150.
de la Sánchez AM, Gómez-Cid L, Domínguez-Sobrino A, Fernández-Avilés F, Berenfeld O, Atienza F. Artificial intelligence analysis of the impact of fibrosis in arrhythmogenesis and drug response. Front Physiol. 2022;13:1025430.
Marijon E, Narayanan K, Smith K, Barra S, Basso C, Blom MT, et al. The Lancet Commission to reduce the global burden of sudden cardiac death: a call for multidisciplinary action. Lancet. 2023;402:883–936.
留言 (0)