Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Bernardo C. Bizzo"'
Autor:
James M. Hillis, Jacob J. Visser, Edward R. Scheffer Cliff, Kelly van der Geest – Aspers, Bernardo C. Bizzo, Keith J. Dreyer, Jeremias Adams-Prassl, Katherine P. Andriole
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-5 (2024)
Externí odkaz:
https://doaj.org/article/637226df86bc41d28b53f5b4b3376158
Autor:
James M. Hillis, Bernardo C. Bizzo, Isabella Newbury‐Chaet, Sarah F. Mercaldo, John K. Chin, Ankita Ghatak, Madeleine A. Halle, Eric L'Italien, Ashley L. MacDonald, Alex S. Schultz, Karen Buch, John Conklin, Stuart Pomerantz, Sandra Rincon, Keith J. Dreyer, William A. Mehan
Publikováno v:
Stroke: Vascular and Interventional Neurology, Vol 4, Iss 4 (2024)
Background Intracranial hemorrhage is a critical finding on computed tomography (CT) of the head. This study compared the accuracy of an artificial intelligence (AI) model (Annalise Enterprise CTB Triage Trauma) to consensus neuroradiologist interpre
Externí odkaz:
https://doaj.org/article/1000eb9586fa40549a7ef12b84c6d349
Autor:
Romane Gauriau, Bernardo C. Bizzo, Donnella S. Comeau, James M. Hillis, Christopher P. Bridge, John K. Chin, Jayashri Pawar, Ali Pourvaziri, Ivana Sesic, Elshaimaa Sharaf, Jinjin Cao, Flavia T. C. Noro, Walter F. Wiggins, M. Travis Caton, Felipe Kitamura, Keith J. Dreyer, John F. Kalafut, Katherine P. Andriole, Stuart R. Pomerantz, Ramon G. Gonzalez, Michael H. Lev
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract Non-contrast head CT (NCCT) is extremely insensitive for early ( 0.98). When this 150 CT test set was expanded to include a total of 364 CT scans with a more heterogeneous distribution of infarct locations (94 stroke-negative, 270 stroke-pos
Externí odkaz:
https://doaj.org/article/854bbf674fbe487dae58da0a4762c5f1
Autor:
Christopher P. Bridge, Bernardo C. Bizzo, James M. Hillis, John K. Chin, Donnella S. Comeau, Romane Gauriau, Fabiola Macruz, Jayashri Pawar, Flavia T. C. Noro, Elshaimaa Sharaf, Marcelo Straus Takahashi, Bradley Wright, John F. Kalafut, Katherine P. Andriole, Stuart R. Pomerantz, Stefano Pedemonte, R. Gilberto González
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-11 (2022)
Abstract Stroke is a leading cause of death and disability. The ability to quickly identify the presence of acute infarct and quantify the volume on magnetic resonance imaging (MRI) has important treatment implications. We developed a machine learnin
Externí odkaz:
https://doaj.org/article/157c664d20a9403091eb001175b7373f
Autor:
Shadi Ebrahimian, Fatemeh Homayounieh, Marcio A. B. C. Rockenbach, Preetham Putha, Tarun Raj, Ittai Dayan, Bernardo C. Bizzo, Varun Buch, Dufan Wu, Kyungsang Kim, Quanzheng Li, Subba R. Digumarthy, Mannudeep K. Kalra
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
Abstract To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXRs) for predicting patient outcomes and the need for mechanical ventilation in COVID-19 pneu
Externí odkaz:
https://doaj.org/article/f8c2b5d978b64d608349f7877ae3ab70
Autor:
Giridhar Dasegowda, Bernardo C. Bizzo, Parisa Kaviani, Lina Karout, Shadi Ebrahimian, Subba R. Digumarthy, Nir Neumark, James M. Hillis, Mannudeep K. Kalra, Keith J. Dreyer
Publikováno v:
Diagnostics, Vol 13, Iss 4, p 778 (2023)
Purpose: Motion-impaired CT images can result in limited or suboptimal diagnostic interpretation (with missed or miscalled lesions) and patient recall. We trained and tested an artificial intelligence (AI) model for identifying substantial motion art
Externí odkaz:
https://doaj.org/article/8a248a13406e47d2b5934c09baff74bd
Autor:
Parisa Kaviani, Mannudeep K. Kalra, Subba R. Digumarthy, Reya V. Gupta, Giridhar Dasegowda, Ammar Jagirdar, Salil Gupta, Preetham Putha, Vidur Mahajan, Bhargava Reddy, Vasanth K. Venugopal, Manoj Tadepalli, Bernardo C. Bizzo, Keith J. Dreyer
Publikováno v:
Diagnostics, Vol 12, Iss 10, p 2382 (2022)
Background: Missed findings in chest X-ray interpretation are common and can have serious consequences. Methods: Our study included 2407 chest radiographs (CXRs) acquired at three Indian and five US sites. To identify CXRs reported as normal, we used
Externí odkaz:
https://doaj.org/article/0ecdb3ec072f41f2936863b72b0b645e
Autor:
Parisa Kaviani, Subba R. Digumarthy, Bernardo C. Bizzo, Bhargava Reddy, Manoj Tadepalli, Preetham Putha, Ammar Jagirdar, Shadi Ebrahimian, Mannudeep K. Kalra, Keith J. Dreyer
Publikováno v:
Diagnostics, Vol 12, Iss 9, p 2086 (2022)
Purpose: We assessed whether a CXR AI algorithm was able to detect missed or mislabeled chest radiograph (CXR) findings in radiology reports. Methods: We queried a multi-institutional radiology reports search database of 13 million reports to identif
Externí odkaz:
https://doaj.org/article/7c39e60788664f5f9392e61e06a35e01
Autor:
Parisa Kaviani, Bernardo C. Bizzo, Subba R. Digumarthy, Giridhar Dasegowda, Lina Karout, James Hillis, Nir Neumark, Mannudeep K. Kalra, Keith J. Dreyer
Publikováno v:
Diagnostics, Vol 12, Iss 8, p 1844 (2022)
(1) Background: Optimal anatomic coverage is important for radiation-dose optimization. We trained and tested (R2.2.4) two (R3-2) deep learning (DL) algorithms on a machine vision tool library platform (Cognex Vision Pro Deep Learning software) to re
Externí odkaz:
https://doaj.org/article/ded85e296e16431291852a37b432e62c
Autor:
Bernardo C. Bizzo, Giridhar Dasegowda, Christopher Bridge, Benjamin Miller, James M. Hillis, Mannudeep K. Kalra, Kimberly Durniak, Markus Stout, Thomas Schultz, Tarik Alkasab, Keith J. Dreyer
Publikováno v:
Journal of the American College of Radiology. 20:352-360