Effects of cardiac growth on electrical dyssynchrony in the single ventricle patient

Tikenogullari, Oguz Ziya; Peirlinck, Mathias; Chubb, Henry; Dubin, Anne M.; Kuhl, Ellen; Marsden, Alison L.

doi:10.1080/10255842.2023.2222203

Cited by 7 publications

(11 citation statements)

References 92 publications

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“…7 This is observed commonly in the hypoplastic left heart and tricuspid atresia populations, and it is postulated that this form of VPE may be related to early excitation of a small left or right ventricle, respectively, by an otherwise intact conduction system. 8 However, Przybylski et al 1 documented a short HV in every case, and the cardiac anatomy is relatively normal.…”

Section: Pathogenesis Of Fvps In Hcmmentioning

confidence: 98%

Ventricular Preexcitation in Hypertrophic Cardiomyopathy: Dove or a Hawk?

Chubb,

Dubin

2023

Circ: Arrhythmia and Electrophysiology

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Section: Pathogenesis Of Fvps In Hcmmentioning

confidence: 98%

Ventricular Preexcitation in Hypertrophic Cardiomyopathy: Dove or a Hawk?

Chubb,

Dubin

2023

Circ: Arrhythmia and Electrophysiology

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“…Similarly, for the Purkinje conductivities, we use an interval spanning both healthy cases and different cardiovascular diseases [38]. For the stimulation time of the left Purkinje network, we employ a broad range for possible ventricular dyssynchrony in patients with single ventricle physiology [27].…”

Section: Cardiac Electrophysiology Modellingmentioning

confidence: 99%

“…We note that this choice is common to other electrophysiological studies [22,45] and facilitates the generation of the fibre field by rule-based algorithms [46]. We subsequently use TetGen [47] to create the tetrahedral volumetric mesh with a maximum edge size of 1 mm [27,32]. The torso model is created semi-automatically from the images using threshold-and region-growing-based segmentation methods.…”

Section: Pre-processingmentioning

confidence: 99%

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Digital twinning of cardiac electrophysiology for congenital heart disease

Salvador,

Kong,

Peirlinck

et al. 2024

J. R. Soc. Interface.

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In recent years, blending mechanistic knowledge with machine learning has had a major impact in digital healthcare. In this work, we introduce a computational pipeline to build certified digital replicas of cardiac electrophysiology in paediatric patients with congenital heart disease. We construct the patient-specific geometry by means of semi-automatic segmentation and meshing tools. We generate a dataset of electrophysiology simulations covering cell-to-organ level model parameters and using rigorous mathematical models based on differential equations. We previously proposed Branched Latent Neural Maps (BLNMs) as an accurate and efficient means to recapitulate complex physical processes in a neural network. Here, we employ BLNMs to encode the parametrized temporal dynamics of in silico 12-lead electrocardiograms (ECGs). BLNMs act as a geometry-specific surrogate model of cardiac function for fast and robust parameter estimation to match clinical ECGs in paediatric patients. Identifiability and trustworthiness of calibrated model parameters are assessed by sensitivity analysis and uncertainty quantification.

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“…While previous studies investigated using deep learning, speci cally neural network, to model cardiac anatomies representing the various types of Congenital Heart Diseases (CHD) and heart shape variations in cardiac disease, however, none of previous studies had in-depth evaluation about the educational value of the widely-available deep learning AI-TAG of DALL•E 3 [20][21][22][23][24][25][26]. We aimed to investigate the effectiveness and perfection of DALL•E 3 in producing educational illustrations for medical education, with a focus on CHD.…”

Section: Introductionmentioning

confidence: 99%

Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases

Temsah,

Alhuzaimi,

Almansour

et al. 2024

J Med Syst

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Arti cial Intelligence (AI), particularly AI-Generated Imagery, holds the capability to transform medical and patient education. This research explores the use of AI-generated imagery, from text-to-images, in medical education, focusing on congenital heart diseases (CHD). Utilizing ChatGPT's DALL•E 3, the research aims to assess the accuracy and educational value of AI-created images for 20 common CHDs. The study involved generating a total of 110 images for normal human heart and 20 common CHDs through DALL•E 3. Then, 33 healthcare professionals systematically assessed these AI-generated images by variable levels of healthcare professionals (HCPs) using a developed framework to individually assess each image anatomical accuracy, in-picture text usefulness, image appeal to medical professionals and the potential to use the image in medical presentations. Each item was assessed on a Likert scale of three. The assessments produced a total of 3630 images' assessments. Most AI-generated cardiac images were rated poorly as follows: 80.8% of images were rated as anatomically incorrect or fabricated, 85.2% rated to have incorrect text labels, 78.1% rated as not usable for medical education. The nurses and medical interns were found to have a more positive perception about the AI-generated cardiac images compared to the faculty members, pediatricians, and cardiology experts. Complex congenital anomalies were found to be signi cantly more predicted to anatomical fabrication compared to simple cardiac anomalies. There were signi cant challenges identi ed in image generation. These ndings suggest adopting a cautious approach in integrating AI imagery in medical education, emphasizing the need for rigorous validation and interdisciplinary collaboration. The study advocates for future AI-models to be ne-tuned with accurate medical data, enhancing their reliability and educational utility.

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Effects of cardiac growth on electrical dyssynchrony in the single ventricle patient

Cited by 7 publications

References 92 publications

Ventricular Preexcitation in Hypertrophic Cardiomyopathy: Dove or a Hawk?

Ventricular Preexcitation in Hypertrophic Cardiomyopathy: Dove or a Hawk?

Digital twinning of cardiac electrophysiology for congenital heart disease

Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases

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