PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging

PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging

Abstract Pulmonary embolism (PE) is a life-threatening clinical problem and computed tomography pulmonary angiography (CTPA) is the gold standard for diagnosis. Prompt diagnosis and immediate treatment are critical to avoid high morbidity and mortality rates, yet PE remains among the diagnoses most...

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Bibliographic Details
Main Authors: Shih-Cheng Huang, Tanay Kothari, Imon Banerjee, Chris Chute, Robyn L. Ball, Norah Borus, Andrew Huang, Bhavik N. Patel, Pranav Rajpurkar, Jeremy Irvin, Jared Dunnmon, Joseph Bledsoe, Katie Shpanskaya, Abhay Dhaliwal, Roham Zamanian, Andrew Y. Ng, Matthew P. Lungren
Format: article
Language:EN
Published: Nature Portfolio 2020
Subjects:
Computer applications to medicine. Medical informatics
R858-859.7
Online Access:https://doaj.org/article/8ceb4d87e32c41efa595cc53fbb017ec
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https://doaj.org/article/8ceb4d87e32c41efa595cc53fbb017ec

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