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Cardiology Research
Congenital Heart Artificial Intelligence (CHAI) Lab

Research Overview

Dr. Mayourian is Founder and Director of the Congenital Heart Artificial Intelligence (CHAI) Lab at Boston Children's Hospital. He has published seminal AI papers in pediatric and adult congenital heart disease, with first-author publications in top-tier journals such as Circulation, JACC, European Heart Journal, and Lancet: Digital Health. This work represents the first applications of AI-enabled electrocardiograms for pediatric cardiology. Please visit the CHAI Lab Website for more details on its mission and research objectives.

Research Background

Dr. Mayourian received his BE and ME in Chemical Engineering at Cooper Union. He then completed his MD/PhD at Mount Sinai, where his PhD was focused on developing computational models of cardiac electrophysiology, and tissue engineering to develop novel cardio-therapies. He has completed his internship and residency in Pediatric at Boston Children's Hospital, and is currently undergoing Pediatric Cardiology and Advanced Electrophysiology Training.

Publications

  1. Bedside predictors of left ventricular systolic dysfunction in children with suspected infection undergoing cardiac ultrasound. Am J Emerg Med. 2026 Sep 01; 110:395-401. View Abstract
  2. The electrocardiogram and artificial intelligence: turning signals into insights in pediatric and congenital heart disease. Curr Opin Pediatr. 2026 Oct 01; 38(5):461-468. View Abstract
  3. Single lead electrocardiographic detection of left ventricular systolic dysfunction in pediatric and congenital heart disease. NPJ Digit Med. 2026 Jul 11. View Abstract
  4. Robust AI-ECG for Predicting Left Ventricular Systolic Dysfunction in Pediatric Congenital Heart Disease. AMIA Jt Summits Transl Sci Proc. 2026; 2026:536-545. View Abstract
  5. Estimation of Left Ventricular Systolic Function in Pediatric and Congenital Heart Disease from Serial Electrocardiograms. AMIA Jt Summits Transl Sci Proc. 2026; 2026:293-302. View Abstract
  6. An ECG foundation model for generalizable cardiac function prediction across the lifespan. medRxiv. 2026 May 27. View Abstract
  7. Response to the letter: Time alignment considerations in evaluating AI-ECG guidance of pulmonary valve replacement timing in repaired tetralogy of Fallot. Am Heart J. 2026 Sep; 299:107471. View Abstract
  8. Rapid functional classification of cardiac genetic variants directly informs precision cardiology. bioRxiv. 2026 Apr 19. View Abstract
  9. Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence. Circulation. 2026 May 26; 153(21):1623-1636. View Abstract
  10. Multicenter validation of AI-enabled ECG for pediatric biological sex prediction. NPJ Digit Med. 2026 Feb 26; 9(1). View Abstract
  11. Deep Learning-Based Automated Echocardiographic Measurements in Pediatric and Congenital Heart Disease. medRxiv. 2026 Feb 09. View Abstract
  12. Development and multicentre validation of an artificial intelligence electrocardiogram model for ventricular remodeling in repaired tetralogy of Fallot. Eur Heart J Digit Health. 2026 Mar; 7(2):ztag015. View Abstract
  13. Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence. medRxiv. 2026 Jan 26. View Abstract
  14. Deep survival analysis from adult and pediatric electrocardiograms: a multi-center benchmark study. BioData Min. 2025 Dec 17; 19(1):6. View Abstract
  15. Dosage-sensitive RBFOX2 autoregulation promotes cardiomyocyte differentiation by maturing the transcriptome. bioRxiv. 2025 Nov 11. View Abstract
  16. Cardiac surveillance of childhood cancer using artificial intelligence-enabled electrocardiograms. Eur Heart J Digit Health. 2025 Nov; 6(6):1293-1296. View Abstract
  17. Infant Electrocardiogram-Based Deep Learning Predicts Critical Congenital Heart Disease. JACC Clin Electrophysiol. 2025 Dec; 11(12):2757-2759. View Abstract
  18. Seeing Is Believing: Intelligence Is Artificial, Responsibility Is Not. J Am Coll Cardiol. 2025 Sep 30; 86(13):979-981. View Abstract
  19. Artificial intelligence-enabled electrocardiogram guidance for pulmonary valve replacement timing in repaired tetralogy of Fallot. Am Heart J. 2026 Jan; 291:153-161. View Abstract
  20. Artificial Intelligence-Enabled Electrocardiograms: Do 15-Lead Studies Improve Model Performance? JACC Adv. 2025 Jul; 4(7):101866. View Abstract
  21. Artificial Intelligence-Enabled ECG to Detect Congenitally Corrected Transposition of the Great Arteries. Pediatr Cardiol. 2026 Mar; 47(3):1376-1382. View Abstract
  22. Dysregulation of N-terminal acetylation causes cardiac arrhythmia and cardiomyopathy. Nat Commun. 2025 Apr 16; 16(1):3604. View Abstract
  23. Development and Validation of a Novel Deep Learning Model to Predict Pharmacologic Closure of Patent Ductus Arteriosus in Premature Infants. J Am Soc Echocardiogr. 2025 Jul; 38(7):624-632. View Abstract
  24. Electrocardiogram-based deep learning to predict left ventricular systolic dysfunction in paediatric and adult congenital heart disease in the USA: a multicentre modelling study. Lancet Digit Health. 2025 04; 7(4):e264-e274. View Abstract
  25. Expert-Level Automated Diagnosis of the Pediatric ECG Using a Deep Neural Network. JACC Clin Electrophysiol. 2025 06; 11(6):1308-1320. View Abstract
  26. Electrocardiogram-based deep learning to predict mortality in paediatric and adult congenital heart disease. Eur Heart J. 2025 Mar 03; 46(9):856-868. View Abstract
  27. Artificial Intelligence-Enabled Electrocardiogram Predicts Sudden Cardiac Death in Repaired Tetralogy of Fallot. JACC Clin Electrophysiol. 2025 Mar; 11(3):593-595. View Abstract
  28. Cardiac Applications of CRISPR/AAV-Mediated Precise Genome Editing. bioRxiv. 2024 Dec 04. View Abstract
  29. Electrocardiogram-Based Deep Learning to Predict Mortality in Repaired Tetralogy of Fallot. JACC Clin Electrophysiol. 2024 12; 10(12):2600-2612. View Abstract
  30. Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease. J Am Coll Cardiol. 2024 Aug 27; 84(9):815-828. View Abstract
  31. Dysregulation of N-terminal acetylation causes cardiac arrhythmia and cardiomyopathy. Res Sq. 2024 Jul 19. View Abstract
  32. Efficient and reproducible generation of human iPSC-derived cardiomyocytes and cardiac organoids in stirred suspension systems. Nat Commun. 2024 07 15; 15(1):5929. View Abstract
  33. Pediatric Electrocardiogram-Based Deep Learning to Predict Secundum Atrial Septal Defects. Pediatr Cardiol. 2025 Jun; 46(5):1235-1240. View Abstract
  34. Development and Validation of a Mortality Risk Score for Repaired Tetralogy of Fallot. J Am Heart Assoc. 2024 06 18; 13(12):e034871. View Abstract
  35. Child opportunity and outcomes in pediatric critical asthma: A multicenter analysis. J Allergy Clin Immunol Pract. 2024 Aug; 12(8):2187-2189. View Abstract
  36. Efficient and reproducible generation of human iPSC-derived cardiomyocytes using a stirred bioreactor. bioRxiv. 2024 Feb 28. View Abstract
  37. Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling. Circulation. 2024 03 19; 149(12):917-931. View Abstract
  38. Social Drivers of Health and Pediatric Extracorporeal Membrane Oxygenation Outcomes. Pediatrics. 2023 12 01; 152(6). View Abstract
  39. An improved reporter identifies ruxolitinib as a potent and cardioprotective CaMKII inhibitor. Sci Transl Med. 2023 06 21; 15(701):eabq7839. View Abstract
  40. Insight into the Role of the Child Opportunity Index on Surgical Outcomes in Congenital Heart Disease. J Pediatr. 2023 08; 259:113464. View Abstract
  41. Computational design of custom therapeutic cells to correct failing human cardiomyocytes. Front Syst Biol. 2023; 3. View Abstract
  42. PPARdelta activation induces metabolic and contractile maturation of human pluripotent stem cell-derived cardiomyocytes. Cell Stem Cell. 2022 04 07; 29(4):559-576.e7. View Abstract
  43. In silico Cell Therapy Model Restores Failing Human Myocyte Electrophysiology and Calcium Cycling in Fibrotic Myocardium. Front Physiol. 2021; 12:755881. View Abstract
  44. Adult human cardiac stem cell supplementation effectively increases contractile function and maturation in human engineered cardiac tissues. Stem Cell Res Ther. 2019 12 04; 10(1):373. View Abstract
  45. FTO-Dependent N6-Methyladenosine Regulates Cardiac Function During Remodeling and Repair. Circulation. 2019 01 22; 139(4):518-532. View Abstract
  46. Joshua Mayourian: Rising to the Challenge. Circ Res. 2018 05 25; 122(11):1494-1495. View Abstract
  47. Functional and transcriptomic insights into pathogenesis of R9C phospholamban mutation using human induced pluripotent stem cell-derived cardiomyocytes. J Mol Cell Cardiol. 2018 06; 119:147-154. View Abstract
  48. Exosomal microRNA-21-5p Mediates Mesenchymal Stem Cell Paracrine Effects on Human Cardiac Tissue Contractility. Circ Res. 2018 03 30; 122(7):933-944. View Abstract
  49. Physiologic, Pathologic, and Therapeutic Paracrine Modulation of Cardiac Excitation-Contraction Coupling. Circ Res. 2018 01 05; 122(1):167-183. View Abstract
  50. Cardiac Tissue Engineering Models of Inherited and Acquired Cardiomyopathies. Methods Mol Biol. 2018; 1816:145-159. View Abstract
  51. An Introduction to Computational Modeling of Cardiac Electrophysiology and Arrhythmogenicity. Methods Mol Biol. 2018; 1816:17-35. View Abstract
  52. Experimental and Computational Insight Into Human Mesenchymal Stem Cell Paracrine Signaling and Heterocellular Coupling Effects on Cardiac Contractility and Arrhythmogenicity. Circ Res. 2017 Aug 04; 121(4):411-423. View Abstract
  53. Modeling Electrophysiological Coupling and Fusion between Human Mesenchymal Stem Cells and Cardiomyocytes. PLoS Comput Biol. 2016 07; 12(7):e1005014. View Abstract

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