Zobrazeno 1 - 10 of 14 pro vyhledávání: '"Laura N Blaivas"'
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Akademický článek
Artificial intelligence versus expert: a comparison of rapid visual inferior vena cava collapsibility assessment between POCUS experts and a deep learning algorithm
Autor: Michael Blaivas, Srikar Adhikari, Eric A. Savitsky, Laura N. Blaivas, Yiju T. Liu
Publikováno v: Journal of the American College of Emergency Physicians Open, Vol 1, Iss 5, Pp 857-864 (2020)
Abstract Objectives We sought to create a deep learning algorithm to determine the degree of inferior vena cava (IVC) collapsibility in critically ill patients to enable novice point‐of‐care ultrasound (POCUS) providers. Methods We used publicly
Externí odkaz: https://doaj.org/article/6339f567fe2044d1957bdcee4206a2d7
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2
Making Artificial Intelligence Lemonade Out of Data Lemons
Autor: Sonia Shah, Yiju Teresa Liu, Joseph L. Thomas, Kabir Yadav, Kendra Campbell, Michael Blaivas, Laura N Blaivas
Publikováno v: Journal of Ultrasound in Medicine. 41:2059-2069
OBJECTIVES A paucity of point-of-care ultrasound (POCUS) databases limits machine learning (ML). Assess feasibility of training ML algorithms to visually estimate left ventricular ejection fraction (EF) from a subxiphoid (SX) window using only apical
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::244bf2a95ef8dfe3af37f4c6d5778d63
https://doi.org/10.1002/jum.15889
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3
Deep Learning Pitfall: Impact of Novel Ultrasound Equipment Introduction on Algorithm Performance and the Realities of Domain Adaptation
Autor: James W. Tsung, Laura N Blaivas, Michael Blaivas
Publikováno v: Journal of Ultrasound in Medicine. 41:855-863
Objectives To test deep learning (DL) algorithm performance repercussions by introducing novel ultrasound equipment into a clinical setting. Methods Researchers introduced prospectively obtained inferior vena cava (IVC) videos from a similar patient
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07b3398f4a434ae3be9ed20c634ccee8
https://doi.org/10.1002/jum.15765
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5
Are Convolutional Neural Networks Trained on ImageNet Images Wearing Rose‐Colored Glasses?: A Quantitative Comparison of ImageNet , Computed Tomographic, Magnetic Resonance, Chest X‐Ray , and Point‐of‐Care Ultrasound Images for Quality
Autor: Laura N Blaivas, Michael Blaivas
Publikováno v: Journal of Ultrasound in Medicine. 40:377-383
Objectives Deep learning for medical imaging analysis uses convolutional neural networks pretrained on ImageNet (Stanford Vision Lab, Stanford, CA). Little is known about how such color- and scene-rich standard training images compare quantitatively
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::257f71dcaf53fa54150b1db521b961fb
https://doi.org/10.1002/jum.15413
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6
Deep learning algorithm performance compared to experts in visual evaluation of interior vena cava collapse on ultrasound to determine intravenous fluid need in dehydration management
Autor: James W. Tsung, Laura N Blaivas, Michael Blaivas
Publikováno v: Signa Vitae.
Objectives: To create a deep learning (DL) algorithm capable of analyzing real time ultrasound video of the inferior vena cava (IVC) for complete collapse in pediatric patients being evaluated for intravenous fluid (IVF) resuscitation. Methods: Resea
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ab0f9940b72e8c53aba976dfef4eb684
https://doi.org/10.22514/sv.2021.128
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7
Development of a Deep Learning Network to Classify Inferior Vena Cava Collapse to Predict Fluid Responsiveness
Autor: Gary Philips, Michael Blaivas, Mitchell M. Levy, Carsten Eickhoff, Adeel Abbasi, Roland C. Merchant, Keith Corl, Laura N Blaivas, Nathan I. Shapiro
Publikováno v: Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in MedicineReferences. 40(8)
Objectives To create a deep learning algorithm capable of video classification, using a long short-term memory (LSTM) network, to analyze collapsibility of the inferior vena cava (IVC) to predict fluid responsiveness in critically ill patients. Metho
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a5d9601addb6d26e7eb507fc2473b7e
https://pubmed.ncbi.nlm.nih.gov/33038035
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8
Artificial intelligence versus expert: a comparison of rapid visual inferior vena cava collapsibility assessment between POCUS experts and a deep learning algorithm
Autor: Eric Savitsky, Srikar Adhikari, Yiju T. Liu, Michael Blaivas, Laura N. Blaivas
Publikováno v: Journal of the American College of Emergency Physicians Open
Journal of the American College of Emergency Physicians Open, Vol 1, Iss 5, Pp 857-864 (2020)
Objectives We sought to create a deep learning algorithm to determine the degree of inferior vena cava (IVC) collapsibility in critically ill patients to enable novice point‐of‐care ultrasound (POCUS) providers. Methods We used publicly available
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18a6b7d850bebdefe1b21728f902e3cf
https://pubmed.ncbi.nlm.nih.gov/33145532
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9
Are All Deep Learning Architectures Alike for Point-of-Care Ultrasound?: Evidence From a Cardiac Image Classification Model Suggests Otherwise
Autor: Michael Blaivas, Laura N Blaivas
Publikováno v: Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in MedicineReferences. 39(6)
OBJECTIVES Little is known about optimal deep learning (DL) approaches for point-of-care ultrasound (POCUS) applications. We compared 6 popular DL architectures for POCUS cardiac image classification to determine whether an optimal DL architecture ex
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3bd3a6eca87d2713b2abc4c6811dbff2
https://pubmed.ncbi.nlm.nih.gov/31872477
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