Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Hugo J. W. L. Aerts"'
Autor:
Dennis Bontempi, Leonard Nuernberg, Suraj Pai, Deepa Krishnaswamy, Vamsi Thiriveedhi, Ahmed Hosny, Raymond H. Mak, Keyvan Farahani, Ron Kikinis, Andrey Fedorov, Hugo J. W. L. Aerts
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-9 (2024)
Abstract Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical transl
Externí odkaz:
https://doaj.org/article/2cae5f3512684eb6ada92f6f175be401
Autor:
Borek Foldyna, Ibrahim Hadzic, Roman Zeleznik, Marcel C. Langenbach, Vineet K. Raghu, Thomas Mayrhofer, Michael T. Lu, Hugo J. W. L. Aerts
Publikováno v:
Communications Medicine, Vol 4, Iss 1, Pp 1-9 (2024)
Abstract Background Heavy smokers are at increased risk for cardiovascular disease and may benefit from individualized risk quantification using routine lung cancer screening chest computed tomography. We investigated the prognostic value of deep lea
Externí odkaz:
https://doaj.org/article/af58a37e8e1246279c5ccf87ec2a166b
Autor:
Deepa Krishnaswamy, Dennis Bontempi, Vamsi Krishna Thiriveedhi, Davide Punzo, David Clunie, Christopher P. Bridge, Hugo J. W. L. Aerts, Ron Kikinis, Andrey Fedorov
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-15 (2024)
Abstract Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating downstream analysis. Artificial intelligence
Externí odkaz:
https://doaj.org/article/dcc71e61a06943b383830340b580a81d
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Artificial intelligence (AI) techniques are increasingly applied across various domains, favoured by the growing acquisition and public availability of large, complex datasets. Despite this trend, AI publications often suffer from lack of re
Externí odkaz:
https://doaj.org/article/10be48a67e44427ba72374df1179b214
Autor:
Yujie Yan, Christopher Kehayias, John He, Hugo J. W. L. Aerts, Kelly J. Fitzgerald, Benjamin H. Kann, David E. Kozono, Christian V. Guthier, Raymond H. Mak
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Manual segmentation of tumors and organs-at-risk (OAR) in 3D imaging for radiation-therapy planning is time-consuming and subject to variation between different observers. Artificial intelligence (AI) can assist with segmentation, but challe
Externí odkaz:
https://doaj.org/article/0d3314f05d744208ab8a55d3f5e81d38
Autor:
Marco Guevara, Shan Chen, Spencer Thomas, Tafadzwa L. Chaunzwa, Idalid Franco, Benjamin H. Kann, Shalini Moningi, Jack M. Qian, Madeleine Goldstein, Susan Harper, Hugo J. W. L. Aerts, Paul J. Catalano, Guergana K. Savova, Raymond H. Mak, Danielle S. Bitterman
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-14 (2024)
Abstract Social determinants of health (SDoH) play a critical role in patient outcomes, yet their documentation is often missing or incomplete in the structured data of electronic health records (EHRs). Large language models (LLMs) could enable high-
Externí odkaz:
https://doaj.org/article/a741022d7d2c4d6bbbca0bc8350120b3
Autor:
Pedro Mateus, Leroy Volmer, Leonard Wee, Hugo J. W. L. Aerts, Frank Hoebers, Andre Dekker, Inigo Bermejo
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract In the past decade, there has been a sharp increase in publications describing applications of convolutional neural networks (CNNs) in medical image analysis. However, recent reviews have warned of the lack of reproducibility of most such st
Externí odkaz:
https://doaj.org/article/4fcb2da1ef3a4d92b15df6063f226ccf
Autor:
Christopher E. Kehayias, Yujie Yan, Dennis Bontempi, Sarah Quirk, Danielle S. Bitterman, Jeremy S. Bredfeldt, Hugo J. W. L. Aerts, Raymond H. Mak, Christian V. Guthier
Publikováno v:
Frontiers in Oncology, Vol 13 (2024)
IntroductionArtificial intelligence (AI)-based technologies embody countless solutions in radiation oncology, yet translation of AI-assisted software tools to actual clinical environments remains unrealized. We present the Deep Learning On-Demand Ass
Externí odkaz:
https://doaj.org/article/621f368a4dfd488c9073e9f52498d7dc
Autor:
John S. Peterson, Deborah Plana, Danielle S. Bitterman, Skyler Bryce Johnson, Hugo J. W. L. Aerts, Benjamin Harris Kann
Publikováno v:
Cancer Medicine, Vol 12, Iss 4, Pp 4715-4724 (2023)
Abstract Background Cancer trial accrual is a national priority, yet up to 20% of trials fail to accrue. Trial eligibility criteria growth may be associated with accrual failure. We sought to quantify eligibility criteria growth within National Cance
Externí odkaz:
https://doaj.org/article/4827d6e445624f17a4bdc8202318ef6e
Autor:
Jakob Weiss, Jana Taron, Zexi Jin, Thomas Mayrhofer, Hugo J. W. L. Aerts, Michael T. Lu, Udo Hoffmann
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
Abstract Deep learning convolutional neural network (CNN) can predict mortality from chest radiographs, yet, it is unknown whether radiologists can perform the same task. Here, we investigate whether radiologists can visually assess image gestalt (de
Externí odkaz:
https://doaj.org/article/534f579e4e264f86b5feb58c8815d5f9