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Development, validation of a deep learning algorithm to differentiate colon carcinoma from diverticulitis in CT images

01.27.23 | JAMA Network

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About The Study: The results of this study suggest that a deep learning model able to distinguish colon carcinoma and acute diverticulitis in computed tomography (CT) images as a support system may significantly improve the diagnostic performance of radiologists, which may improve patient care. As an artificial intelligence support system, the model significantly improved the sensitivity and specificity and reduced the number of false-negative and false-positive findings.

Authors: Sebastian Ziegelmayer, M.D., of the Technical University of Munich in Munich, Germany, is the corresponding author.

To access the embargoed study: Visit our For The Media website at this link https://media.jamanetwork.com/

(doi:10.1001/jamanetworkopen.2022.53370)

Editor’s Note: Please see the article for additional information, including other authors, author contributions and affiliations, conflict of interest and financial disclosures, and funding and support.

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How to Cite This Article

APA:
JAMA Network. (2023, January 27). Development, validation of a deep learning algorithm to differentiate colon carcinoma from diverticulitis in CT images. Brightsurf News. https://www.brightsurf.com/news/147YWXN1/development-validation-of-a-deep-learning-algorithm-to-differentiate-colon-carcinoma-from-diverticulitis-in-ct-images.html
MLA:
"Development, validation of a deep learning algorithm to differentiate colon carcinoma from diverticulitis in CT images." Brightsurf News, Jan. 27 2023, https://www.brightsurf.com/news/147YWXN1/development-validation-of-a-deep-learning-algorithm-to-differentiate-colon-carcinoma-from-diverticulitis-in-ct-images.html.