Generating patient-specific computational models with point cloud data from human atrial electrophysiology studies
Year: 2026
Type: article
Abstract: One in five patients diagnosed with atrial fibrillation die within a year after being diagnosed according to a prospective cohort study done in the United Kingdom, making this disease the focus of many scientific studies. One approach to studying this disease has been computational models since they have demonstrated powerful capabilities in understanding and analyzing the biochemical processes underlying atrial fibrillation. To create accurate patient models for studying cardiovascular diseases, we developed a pipeline for generating patient-specific models of the left atrial posterior wall using point cloud data without image segmentation. Our goal was to evaluate the performance of these models by comparing simulated electrograms to those obtained from atrial fibrillation patients. We created models for two different paroxysmal atrial fibrillation patients under healthy tissue conditions. To validate our model, we compared simulated and measured electrograms using various metrics. Some electrograms matched well in terms of local activation time and cross-correlation peak, whereas others showed significant differences in amplitude and duration. Additionally, we explored the impact of modeling fibrotic tissue on electrogram morphology by creating four persistent atrial fibrillation patient models with varying fibrosis densities and types. Simulations indicated that increased modeled fibrosis density led to more multiphasic electrogram morphologies, with little impact from fibrosis type. The fibrosis simulations also had morphological characteristics seen in other fibrosis electrophysiology modeling studies like deflections patterns and amplitudes, strengthening the reasoning behind using this type of model generation methodology. Our findings suggest that point cloud data is sufficient for creating accurate left atrial posterior wall models, which can simulate electrograms comparable to measured waveforms. This method could be useful for patient-specific studies, potential specialized ablation procedures, and arrhythmia research.
Source: PLoS ONE
Institutions: Duke University, University of North Carolina at Chapel Hill
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,382
Funders: National Science Foundation, United States Agency for International Development, U.S. Department of Veterans Affairs, Bill and Melinda Gates Foundation, Alzheimer's Association, American Heart Association, Pfizer, Fondazione CRT, Agropolis Fondation, Juvenile Diabetes Research Foundation United States of America, U.S. President’s Emergency Plan for AIDS Relief, Queen Mary University of London, Zhengzhou Municipal Science and Technology Bureau, William and Ella Owens Medical Research Foundation, UK Research and Innovation, Health~Holland, Dutch Arthritis Society, European Commission, National Research Foundation, South African Medical Research Council, Deutsche Forschungsgemeinschaft, Volkswagen Foundation, National Natural Science Foundation of China, Soonchunhyang University, Työsuojelurahasto, Ministero della Salute, Ministerstvo Zdravotnictví Ceské Republiky, National Research Foundation of Korea, Karolinska Institutet, Sightsavers International, Vetenskapsrådet, Natural Science Foundation of Shanxi Province, China Scholarship Council, National Health Research Institutes, Universidad Nacional Autónoma de México, Ningbo Municipal Bureau of Science and Technology, Suomalainen Lääkäriseura Duodecim, The Wellcome Trust DBT India Alliance, Familjen Kamprads Stiftelse, Business Finland, Quzhou Municipal Science and Technology Bureau, Jashore University of Science and Technology, Ministero dell'Università e della Ricerca, National Institutes of Health, Agency for Healthcare Research and Quality, National Institute of Food and Agriculture, University of California, Davis, Arts and Humanities Research Council, Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council, Australian Research Council, Japan Society for the Promotion of Science, Korea Centers for Disease Control and Prevention, Instituto de Salud Carlos III, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities, Science and Technology Program of Suzhou, National Institute on Aging, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute on Drug Abuse, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke, National Center for Advancing Translational Sciences, Graduate School, University of North Carolina at Chapel Hill, Carolina Population Center, University of North Carolina at Chapel Hill, Fusion Oriented REsearch for disruptive Science and Technology, H2020 European Research Council, National Science Foundation Graduate Research Fellowship Program, Boehringer Ingelheim Animal Health
OpenAlex ID: W7140858325