Impact of Different Mammography Systems on Artificial Intelligence Performance in Breast Cancer Screening

Autor: de Vries, Clarisse F., Colosimo, Samantha J., Staff, Roger T., Dymiter, Jaroslaw A., Yearsley, Joseph, Dinneen, Deirdre, Boyle, Moragh, Harrison, David J., Anderson, Lesley A., Lip, Gerald

Publikováno v: Radiol Artif Intell

Artificial intelligence (AI) tools may assist breast screening mammography programs, but limited evidence supports their generalizability to new settings. This retrospective study used a 3-year dataset (April 1, 2016–March 31, 2019) from a U.K. reg

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmc_________::df3e86ec71579feb6863b2d1c133638c
https://europepmc.org/articles/PMC10245180/

Automated Classification of Intramedullary Spinal Cord Tumors and Inflammatory Demyelinating Lesions Using Deep Learning

Autor: Zhizheng Zhuo, Jie Zhang, Yunyun Duan, Liying Qu, Chenlu Feng, Xufang Huang, Dan Cheng, Xiaolu Xu, Ting Sun, Zhaohui Li, Xiaopeng Guo, Xiaodong Gong, Yongzhi Wang, Wenqing Jia, Decai Tian, Xinghu Zhang, Fudong Shi, Sven Haller, Frederik Barkhof, Chuyang Ye, Yaou Liu

Publikováno v: Zhuo, Z, Zhang, J, Duan, Y, Qu, L, Feng, C, Huang, X, Cheng, D, Xu, X, Sun, T, Li, Z, Guo, X, Gong, X, Wang, Y, Jia, W, Tian, D, Zhan, X, Shi, F, Haller, S, Barkhof, F, Ye, C & Liu, Y 2022, ' Automated Classification of Intramedullary Spinal Cord Tumors and Inflammatory Demyelinating Lesions Using Deep Learning ', Radiology. Artificial intelligence, vol. 4, no. 6, e210292 . https://doi.org/10.1148/ryai.210292
Radiol Artif Intell
Radiology. Artificial intelligence, 4(6):e210292. Radiological Society of North America Inc.

Accurate differentiation of intramedullary spinal cord tumors and inflammatory demyelinating lesions and their subtypes are warranted because of their overlapping characteristics at MRI but with different treatments and prognosis. The authors aimed t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::973b6adbd87b56bee5e36ea5da4bf26b
https://research.vumc.nl/en/publications/301a5cfa-218b-4647-a6c0-02259626b35e

Longitudinal Assessment of Posttreatment Diffuse Glioma Tissue Volumes with Three-dimensional Convolutional Neural Networks

Autor: Jeffrey D. Rudie, Evan Calabrese, Rachit Saluja, David Weiss, John B. Colby, Soonmee Cha, Christopher P. Hess, Andreas M. Rauschecker, Leo P. Sugrue, Javier E. Villanueva-Meyer

Publikováno v: Radiology. Artificial intelligence, vol 4, iss 5
Radiol Artif Intell

Neural networks were trained for segmentation and longitudinal assessment of posttreatment diffuse glioma. A retrospective cohort (from January 2018 to December 2019) of 298 patients with diffuse glioma (mean age, 52 years ± 14 [SD]; 177 men; 152 pa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::563261cc8a41399b9ed0a5a9a13220b3
https://escholarship.org/uc/item/8h01j5xb

Deep Learning-based Detection of Intravenous Contrast Enhancement on CT Scans

Autor: Ye, Zezhong, Qian, Jack M., Hosny, Ahmed, Zeleznik, Roman, Plana, Deborah, Likitlersuang, Jirapat, Zhang, Zhongyi, Mak, Raymond H., Aerts, Hugo J. W. L., Kann, Benjamin H.

Publikováno v: Radiology: Artificial Intelligence, 4(3):210285. Radiological Society of North America, Inc.
Radiology: Artificial Intelligence
Radiol Artif Intell

Identifying the presence of intravenous contrast material on CT scans is an important component of data curation for medical imaging-based artificial intelligence model development and deployment. Use of intravenous contrast material is often poorly

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a0159e167e043c71c47651998c708675
https://cris.maastrichtuniversity.nl/en/publications/e1820e66-20cc-46fa-abe2-2901af98cebf

Reader Perceptions and Impact of AI on CT Assessment of Air Trapping

Autor: Albert Hsiao, Lewis D. Hahn, Tara Retson, Andrew Yen, Sharon S. Brouha, Kathleen Jacobs, Seth Kligerman, Kyle Hasenstab

Publikováno v: Radiol Artif Intell

Quantitative imaging measurements can be facilitated by artificial intelligence (AI) algorithms, but how they might impact decision-making and be perceived by radiologists remains uncertain. After creation of a dedicated inspiratory-expiratory CT exa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fdfaa1be699104ed778308b8ff2276ec
https://doi.org/10.1148/ryai.2021210160