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Your search keyword '"Mangold, Kathryn E"' showing total 10 results
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10 results on '"Mangold, Kathryn E"'

1. Artificial intelligence enabled interpretation of ECG images to predict hematopoietic cell transplantation toxicity

Author

Shaffer, Brian C., Brown, Samantha, Chinapen, Stephanie, Mangold, Kathryn E., Lahoud, Oscar, Lopez-Jimenez, Francisco, Schaffer, Wendy, Liu, Jennifer, Giralt, Sergio, Devlin, Sean, Shah, Gunjan, Scordo, Michael, Papadopoulos, Esperanza, Landau, Heather, Usmani, Saad, Perales, Miguel-Angel, Friedman, Paul A., Gersh, Bernard J., Attia, Itzhak Zachi, Noseworthy, Peter A., and Kosmidou, Ioanna

Published
2024
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5. Artificial Intelligence ECG as a Novel Screening Tool to Detect a Newly Abnormal Left Ventricular Ejection Fraction After Anthracycline-Based Cancer Therapy

Author

Jacobs, Johanna E J, primary, Greason, Grace, additional, Mangold, Kathryn E, additional, Wildiers, Hans, additional, Willems, Rik, additional, Janssens, Stefan, additional, Noseworthy, Peter, additional, Lopez-Jimenez, Francisco, additional, Voigt, Jens-Uwe, additional, Friedman, Paul, additional, Van Aelst, Lucas, additional, Vandenberk, Bert, additional, Attia, Zachi Itzhak, additional, and Herrmann, Joerg, additional

Published
2023
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8. Assessing Biological Age

Author

Lopez-Jimenez, Francisco, Kapa, Suraj, Friedman, Paul A., LeBrasseur, Nathan K., Klavetter, Eric, Mangold, Kathryn E., and Attia, Zachi I.

Abstract

Biological age may be a more valuable predictor of morbidity and mortality than a person’s chronological age. Mathematical models have been used for decades to predict biological age, but recent developments in artificial intelligence (AI) have led to new capabilities in age estimation. Using deep learning methods to train AI models on hundreds of thousands of electrocardiograms (ECGs) to predict age results in a good, but imperfect, age prediction. The error predicting age using ECG, or the difference between AI-ECG–derived age and chronological age (delta age), may be a surrogate measurement of biological age, as the delta age relates to survival, even after adjusting for chronological age and other covariates associated with total and cardiovascular mortality. The relative affordability, noninvasiveness, and ubiquity of ECGs, combined with ease of access and potential to be integrated with smartphone or wearable technology, presents a potential paradigm shift in assessment of biological age.

Published
2024
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9. Creating Ion Channel Kinetic Models Using Cloud Computing

Author

Mangold, Kathryn E., Zhou, Zhuodong, Schoening, Max, Moreno, Jonathan D., and Silva, Jonathan R.

Subjects
Kinetics, Medical Laboratory Technology, General Immunology and Microbiology, General Neuroscience, Computer Simulation, Health Informatics, Cloud Computing, General Pharmacology, Toxicology and Pharmaceutics, Article, Ion Channels, Software, General Biochemistry, Genetics and Molecular Biology
Abstract

Computational modeling of ion channels provides key insight into experimental electrophysiology results and can be used to connect channel dynamics to emergent phenomena observed at the tissue and organ levels. However, creation of these models requires substantial mathematical and computational background. This tutorial seeks to lower the barrier to creating these models by providing an automated pipeline for creating Markov models of an ion channel kinetics dataset. We start by detailing how to encode sample voltage-clamp protocols and experimental data into the program and its implementation in a cloud computing environment. We guide the reader on how to build a containerized instance, push the machine image, and finally run the routine on cluster nodes. While providing open-source code has become more standard in computational studies, this tutorial provides unprecedented detail on the use of the program and the creation of channel models, starting from inputting the raw experimental data. © 2022 Wiley Periodicals LLC. Basic Protocol: Creation of ion channel kinetic models with a cloud computing environment Alternate Protocol: Instructions for use in a standard high-performance compute cluster.

Published
2022
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10. Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms.

Author

Mangold KE, Carter RE, Siontis KC, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Friedman PA, and Attia ZI

Abstract

Aims: Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record., Methods and Results: We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively., Conclusion: The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health., Competing Interests: Conflict of interest: R.E.C., K.C.S., P.A.N., F.L.-J., S.J.A., P.A.F., Z.I.A., and Mayo Clinic have licensed AI-ECG models to Anumana and might benefit from its commercialization., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Published
2024
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