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Your search keyword '"Calvachi, Paola"' showing total 19 results
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19 results on '"Calvachi, Paola"'

5. A Large-Scale Multicenter Retrospective Study on Nephrotoxicity Associated With Empiric Broad-Spectrum Antibiotics in Critically Ill Patients

Author

Chen, Alyssa Y., primary, Deng, Chih-Ying, additional, Calvachi, Paola, additional, Armengol de la Hoz, Miguel Ángel, additional, Khazi-Syed, Afeefah, additional, Chen, Christina, additional, Scurlock, Corey, additional, Becker, Christian, additional, Johnson, Alistair, additional, Celi, Leo, additional, and Dagan, Alon, additional

Published
2023
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6. LARGE-SCALE, MULTICENTER, RETROSPECTIVE STUDY ON NEPHROTOXICITY ASSOCIATED WITH EMPIRIC, BROAD-SPECTRUM ANTIBIOTICS IN CRITICALLY ILL PATIENTS

Author

CHEN, ALYSSA, primary, DENG, CHIH-YING CD, additional, CALVACHI, PAOLA, additional, HOZ, MIGUEL ÁNGEL MA ARMENGOL DE LA, additional, KHAZI-SYED, AFEEFAH F, additional, CHEN, CHRISTINA W, additional, SCURLOCK, COREY S, additional, BECKER, CHRISTIAN D, additional, JOHNSON, ALISTAIR, additional, CELI, LEO ANTHONY, additional, and DAGAN, ALON, additional

Published
2022
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12. Automating Clinical Chart Review: An Open-Source Natural Language Processing Pipeline Developed on Free-Text Radiology Reports From Patients With Glioblastoma

Author

Senders, Joeky T., primary, Cho, Logan D., additional, Calvachi, Paola, additional, McNulty, John J., additional, Ashby, Joanna L., additional, Schulte, Isabelle S., additional, Almekkawi, Ahmad Kareem, additional, Mehrtash, Alireza, additional, Gormley, William B., additional, Smith, Timothy R., additional, Broekman, Marike L.D., additional, and Arnaout, Omar, additional

Published
2020
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15. Deep learning in rare disease. Detection of tubers in tuberous sclerosis complex.

Author

Sánchez Fernández, Iván, Yang, Edward, Calvachi, Paola, Amengual-Gual, Marta, Wu, Joyce Y., Krueger, Darcy, Northrup, Hope, Bebin, Martina E., Sahin, Mustafa, Yu, Kun-Hsing, and Peters, Jurriaan M.

Subjects
TUBEROUS sclerosis, ARTIFICIAL neural networks, DEEP learning, COMPUTER operating systems, TUBERS, RARE diseases
Abstract

Objective: To develop and test a deep learning algorithm to automatically detect cortical tubers in magnetic resonance imaging (MRI), to explore the utility of deep learning in rare disorders with limited data, and to generate an open-access deep learning standalone application. Methods: T2 and FLAIR axial images with and without tubers were extracted from MRIs of patients with tuberous sclerosis complex (TSC) and controls, respectively. We trained three different convolutional neural network (CNN) architectures on a training dataset and selected the one with the lowest binary cross-entropy loss in the validation dataset, which was evaluated on the testing dataset. We visualized image regions most relevant for classification with gradient-weighted class activation maps (Grad-CAM) and saliency maps. Results: 114 patients with TSC and 114 controls were divided into a training set, a validation set, and a testing set. The InceptionV3 CNN architecture performed best in the validation set and was evaluated in the testing set with the following results: sensitivity: 0.95, specificity: 0.95, positive predictive value: 0.94, negative predictive value: 0.95, F1-score: 0.95, accuracy: 0.95, and area under the curve: 0.99. Grad-CAM and saliency maps showed that tubers resided in regions most relevant for image classification within each image. A stand-alone trained deep learning App was able to classify images using local computers with various operating systems. Conclusion: This study shows that deep learning algorithms are able to detect tubers in selected MRI images, and deep learning can be prudently applied clinically to manually selected data in a rare neurological disorder. [ABSTRACT FROM AUTHOR]

Published
2020
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