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Jacquemyn X, Kutty S, Manlhiot C. The Lifelong Impact of Artificial Intelligence and Clinical Prediction Models on Patients With Tetralogy of Fallot. CJC PEDIATRIC AND CONGENITAL HEART DISEASE 2023; 2:440-452. [PMID: 38161675 PMCID: PMC10755786 DOI: 10.1016/j.cjcpc.2023.08.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 08/24/2023] [Indexed: 01/03/2024]
Abstract
Medical advancements in the diagnosis, surgical techniques, perioperative care, and continued care throughout childhood have transformed the outlook for individuals with tetralogy of Fallot (TOF), improving survival and shifting the perspective towards lifelong care. However, with a growing population of survivors, longstanding challenges have been accentuated, and new challenges have surfaced, necessitating a re-evaluation of TOF care. Availability of prenatal diagnostics, insufficient information from traditional imaging techniques, previously unforeseen medical complications, and debates surrounding optimal timing and indications for reintervention are among the emerging issues. To address these challenges, the integration of artificial intelligence and machine learning holds great promise as they have the potential to revolutionize patient management and positively impact lifelong outcomes for individuals with TOF. Innovative applications of artificial intelligence and machine learning have spanned across multiple domains of TOF care, including screening and diagnosis, automated image processing and interpretation, clinical risk stratification, and planning and performing cardiac interventions. By embracing these advancements and incorporating them into routine clinical practice, personalized medicine could be delivered, leading to the best possible outcomes for patients. In this review, we provide an overview of these evolving applications and emphasize the challenges, limitations, and future potential for integrating them into clinical care.
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Affiliation(s)
- Xander Jacquemyn
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Shelby Kutty
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Cedric Manlhiot
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
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Mauger CA, Govil S, Chabiniok R, Gilbert K, Hegde S, Hussain T, McCulloch AD, Occleshaw CJ, Omens J, Perry JC, Pushparajah K, Suinesiaputra A, Zhong L, Young AA. Right-left ventricular shape variations in tetralogy of Fallot: associations with pulmonary regurgitation. J Cardiovasc Magn Reson 2021; 23:105. [PMID: 34615541 PMCID: PMC8496085 DOI: 10.1186/s12968-021-00780-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 05/26/2021] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Relationships between right ventricular (RV) and left ventricular (LV) shape and function may be useful in determining optimal timing for pulmonary valve replacement in patients with repaired tetralogy of Fallot (rTOF). However, these are multivariate and difficult to quantify. We aimed to quantify variations in biventricular shape associated with pulmonary regurgitant volume (PRV) in rTOF using a biventricular atlas. METHODS In this cross-sectional retrospective study, a biventricular shape model was customized to cardiovascular magnetic resonance (CMR) images from 88 rTOF patients (median age 16, inter-quartile range 11.8-24.3 years). Morphometric scores quantifying biventricular shape at end-diastole and end-systole were computed using principal component analysis. Multivariate linear regression was used to quantify biventricular shape associations with PRV, corrected for age, sex, height, and weight. Regional associations were confirmed by univariate correlations with distances and angles computed from the models, as well as global systolic strains computed from changes in arc length from end-diastole to end-systole. RESULTS PRV was significantly associated with 5 biventricular morphometric scores, independent of covariates, and accounted for 12.3% of total shape variation (p < 0.05). Increasing PRV was associated with RV dilation and basal bulging, in conjunction with decreased LV septal-lateral dimension (LV flattening) and systolic septal motion towards the RV (all p < 0.05). Increased global RV radial, longitudinal, circumferential and LV radial systolic strains were significantly associated with increased PRV (all p < 0.05). CONCLUSION A biventricular atlas of rTOF patients quantified multivariate relationships between left-right ventricular morphometry and wall motion with pulmonary regurgitation. Regional RV dilation, LV reduction, LV septal-lateral flattening and increased RV strain were all associated with increased pulmonary regurgitant volume. Morphometric scores provide simple metrics linking mechanisms for structural and functional alteration with important clinical indices.
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Affiliation(s)
- Charlène A. Mauger
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Sachin Govil
- University of California San Diego, La Jolla, CA USA
| | - Radomir Chabiniok
- University of Texas Southwestern Medical Centre, Dallas, TX USA
- Inria, Palaiseau, France
- LMS, École Polytechnique, CNRS, Institut Polytechnique de Paris, Palaiseau, France
- Department of Mathematics, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Kathleen Gilbert
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Sanjeet Hegde
- University of California San Diego, La Jolla, CA USA
- Division of Cardiology, Rady Children’s Hospital, San Diego, CA USA
| | - Tarique Hussain
- University of Texas Southwestern Medical Centre, Dallas, TX USA
| | | | | | - Jeffrey Omens
- University of California San Diego, La Jolla, CA USA
| | - James C. Perry
- University of California San Diego, La Jolla, CA USA
- Division of Cardiology, Rady Children’s Hospital, San Diego, CA USA
| | | | | | - Liang Zhong
- National Heart Centre, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Alistair A. Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Department of Biomedical Engineering, King’s College London, London, UK
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Gilbert K, Mauger C, Young AA, Suinesiaputra A. Artificial Intelligence in Cardiac Imaging With Statistical Atlases of Cardiac Anatomy. Front Cardiovasc Med 2020; 7:102. [PMID: 32695795 PMCID: PMC7338378 DOI: 10.3389/fcvm.2020.00102] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 05/14/2020] [Indexed: 12/14/2022] Open
Abstract
In many cardiovascular pathologies, the shape and motion of the heart provide important clues to understanding the mechanisms of the disease and how it progresses over time. With the advent of large-scale cardiac data, statistical modeling of cardiac anatomy has become a powerful tool to provide automated, precise quantification of the status of patient-specific heart geometry with respect to reference populations. Powered by supervised or unsupervised machine learning algorithms, statistical cardiac shape analysis can be used to automatically identify and quantify the severity of heart diseases, to provide morphometric indices that are optimally associated with clinical factors, and to evaluate the likelihood of adverse outcomes. Recently, statistical cardiac atlases have been integrated with deep neural networks to enable anatomical consistency of cardiac segmentation, registration, and automated quality control. These combinations have already shown significant improvements in performance and avoid gross anatomical errors that could make the results unusable. This current trend is expected to grow in the near future. Here, we aim to provide a mini review highlighting recent advances in statistical atlasing of cardiac function in the context of artificial intelligence in cardiac imaging.
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Affiliation(s)
- Kathleen Gilbert
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Charlène Mauger
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.,Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.,Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.,Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of Computing, University of Leeds, Leeds, United Kingdom.,School of Medicine, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
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Atlas-Based Computational Analysis of Heart Shape and Function in Congenital Heart Disease. J Cardiovasc Transl Res 2018; 11:123-132. [PMID: 29294215 DOI: 10.1007/s12265-017-9778-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 12/18/2017] [Indexed: 12/18/2022]
Abstract
Approximately 1% of all babies are born with some form of congenital heart defect. Many serious forms of CHD can now be surgically corrected after birth, which has led to improved survival into adulthood. However, many patients require serial monitoring to evaluate progression of heart failure and determine timing of interventions. Accurate multidimensional quantification of regional heart shape and function is required for characterizing these patients. A computational atlas of single ventricle and biventricular heart shape and function enables quantification of remodeling in terms of z scores in relation to specific reference populations. Progression of disease can then be monitored effectively by longitudinal evaluation of z scores. A biomechanical analysis of cardiac function in relation to population variation enables investigation of the underlying mechanisms for developing pathology. Here, we summarize recent progress in this field, with examples in single ventricle and biventricular congenital pathologies.
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Pieterman ED, Budde RPJ, Robbers-Visser D, van Domburg RT, Helbing WA. Knowledge-based reconstruction for measurement of right ventricular volumes on cardiovascular magnetic resonance images in a mixed population. CONGENIT HEART DIS 2017; 12:561-569. [DOI: 10.1111/chd.12484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 03/27/2017] [Accepted: 04/30/2017] [Indexed: 11/27/2022]
Affiliation(s)
- Elise D. Pieterman
- Department of Pediatrics, Division of Cardiology; Erasmus Medical Center, Sophia Children's Hospital; Rotterdam The Netherlands
- Department of Radiology; Erasmus Medical Center; Rotterdam The Netherlands
| | | | - Daniëlle Robbers-Visser
- Department of Pediatrics, Division of Cardiology; Erasmus Medical Center, Sophia Children's Hospital; Rotterdam The Netherlands
- Department of Radiology; Erasmus Medical Center; Rotterdam The Netherlands
| | - Ron T. van Domburg
- Department of Cardiology-Thorax Center; Erasmus Medical Center; Rotterdam The Netherlands
| | - Willem A. Helbing
- Department of Pediatrics, Division of Cardiology; Erasmus Medical Center, Sophia Children's Hospital; Rotterdam The Netherlands
- Department of Radiology; Erasmus Medical Center; Rotterdam The Netherlands
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Nyns ECA, Dragulescu A, Yoo SJ, Grosse-Wortmann L. Evaluation of knowledge-based reconstruction for magnetic resonance volumetry of the right ventricle after arterial switch operation for dextro-transposition of the great arteries. Int J Cardiovasc Imaging 2016; 32:1415-1423. [DOI: 10.1007/s10554-016-0921-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2016] [Accepted: 05/29/2016] [Indexed: 12/01/2022]
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