Hersh Sagreiya, MD

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Assistant Professor of Radiology at the Hospital of the University of Pennsylvania
Member, ITMAT
Department: Radiology

Contact information
Hospital of University of Pennsylvania
Department of Radiology
Division of Abdominal Imaging
3400 Spruce Street
Philadelphia, PA 19104
Office: 215-614-0351
Education:
A.B. (Magna cum laude in Biochemical Sciences )
Harvard College, 2007.
MD (Medicine)
Stanford Medical School, 2012.
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Description of Clinical Expertise

abdominal imaging
ultrasound

Description of Research Expertise

informatics
machine learning

Selected Publications

Al-Hasani, M., Sultan, L.R., Sagreiya, H., Cary, T.W., Karmacharya, M.B., Sehgal, C.M.: Machine Learning Improves Early Detection of Liver Fibrosis by Quantitative Ultrasound Radiomics. 2022 IEEE International Ultrasonics Symposium October 2022.

Sagreiya, H., Jacobs, M.A., Akhbardeh, A.: Novel Quantitative Tool for Assessing Pulmonary Disease Burden in COVID-19 Using Ultrasound. American Association of Physicists in Medicine July 2022.

Yamashita, R., Kapoor, T., Alam, M.N., Galimzianova, A., Syed, S.A., Akdogan, M.U., Alkim, E., Wentland, A.L., Madhuripan, N., Goff, D., Barbee, V., Sheybani, N.D., Sagreiya, H., Rubin, D.L., Desser, T.S. (2022). : Toward Reduction in False-Positive Thyroid Nodule Biopsies with a Deep Learning-based Risk-stratification System using Ultrasound Cine-clip Images. Radiology: Artificial Intelligence 4(3): e210174, May 2022.

Orangi-Fard, N., Akhbardeh, A., Sagreiya, H.: Predicting the Risk for ICU Readmission Using Natural Language Processing and Machine Learning. MDPI Informatics 9(1): 10, January 2022.

Sagreiya, H., Akhbardeh, A., Durot, I., and Rubin, D.L.: Machine Learning for Automated Hepatic Fat Quantification. TechConnect Briefs 2021: 105-108, October 2021 Notes: https://briefs.techconnect.org/papers/machine-learning-for-automated-hepatic-fat-quantification/

De Jesus-Rodriguez, H.J., Morgan, M.A., Sagreiya, H.: Deep Learning in Kidney Ultrasound: Overview, Frontiers, and Challenges. Advances in Chronic Kidney Disease 28(3): 262-269, May 2021.

Elahi, A., MacLean, M.T., Duda, J., Lee, J., Reid, D., Mesure, S., Jehangir, Q., Vujkovic, M., Ko, Y., Litt, H., Borthakur, A., Sagreiya, H., Rosen, M., Mankoff, D.A., Schnall, M.D., Shou, H., Chirinos, J., Damrauer, S.M., Torigian, D.A., Carr, R., Rader, D.J., Kahn Jr., C.E., Witschey, W.R. (2021).: Development of deep learning methods for large-scale opportunistic CT screening of hepatic steatosis. Society for Imaging Informatics in Medicine May 2021.

MacLean, M., Jehangir, Q., Vujkovic, M., Ko, Y., Litt, H., Borthakur, A., Sagreiya, H., Rosen, M., Mankoff, D.A., Schnall, M.D., Shou, H., Chirinos, J., Damrauer, S.M., Torigian, D.A., Carr, R., Rader, D.J., Witschey, W.R.: Quantification of abdominal fat from computed tomography using deep learning and its association with electronic health records in an academic biobank. Journal of the American Medical Informatics Association February 2021.

Cannella, R., Vernuccio, F., Sagreiya, H., Iranpour, N., Marin, D., Furlan, A. : Liver Imaging Reporting and Data System (LI-RADS) v2018: Diagnostic Value of Ancillary Features Favoring Malignancy in Observations at Intermediate (LR-3) and High Probability (LR-4) for Hepatocellular Carcinoma. European Radiology 30(7): 3770-3781, July 2020 Notes: doi: 10.1007/s00330-020-06698-9.

Dunmon, J., Ratner, A.J., Saab, K., Khandwala, N., Markert, M., Sagreiya, H., Goldman, R., Lee-Messer, C., Lungren, M.P., Rubin, D.L., Rè, C.: Cross-Modal Data Programming Enables Rapid Medical Machine Learning. Cell Patterns 1(2): 100019, May 2020 Notes: doi: 10.1016/j.patter.2020.100019.

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Last updated: 06/21/2022
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