Results 81 to 90 of about 636,159 (270)

A review of artificial intelligence in brachytherapy

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in various aspects of brachytherapy.
Jingchu Chen, Richard L. J. Qiu, Tonghe Wang, Shadab Momin, Xiaofeng Yang   +4 more
wiley   +1 more source

Assessing HyperSight iterative CBCT for dose calculation in online adaptive radiotherapy for pelvis and breast patients compared to synthetic CT

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose/objectives Recent technological advancements have increased efficiency for clinical deliverability of online‐adaptive‐radiotherapy (oART). Previous cone‐beam‐computed‐tomography (CBCT) generations lacked the ability to provide reliable Hounsfield‐units (HU), thus requiring oART workflows to rely on synthetic‐CT (sCT) images derived ...
Jingwei Duan, Joel A. Pogue, Dennis N. Stanley, Sui Shen, Natalie N. Viscariello, Carlos E. Cardenas, Richard A. Popple, Joseph Harms   +7 more
wiley   +1 more source

Identify Learning Style Using a Fuzzy System Based on Network Behaviors

International Journal of Information and Communication Technology Research, 2020
In the e-learning environment, there are various learners with varying learning characteristics, including prior knowledge, experience, motivation, and learning objective, and each learner is responsible for their own learning.
Tahereh Sanjabi, Gholam Ali Montazer
doaj  

Evaluating the use of diagnostic CT with flattening filter free beams for palliative radiotherapy: Dosimetric impact of scanner calibration variability

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose Palliative radiotherapy comprises a significant portion of the radiation treatment workload. Volumetric‐modulated arc therapy (VMAT) improves dose conformity and, in conjunction with flattening filter free (FFF) delivery, can decrease treatment times, both of which are desirable in a population with a high probability of retreatment ...
Madeleine L. Van de Kleut, Lesley A. Buckley, Elsayed S. M. Ali   +2 more
wiley   +1 more source

Transfer Learning and Meta Learning Based Fast Downlink Beamforming Adaptation [PDF]

arXiv, 2020
This paper studies fast adaptive beamforming optimization for the signal-to-interference-plus-noise ratio balancing problem in a multiuser multiple-input single-output downlink system. Existing deep learning based approaches to predict beamforming rely on the assumption that the training and testing channels follow the same distribution which may not ...
arxiv  

Closing the gap in plan quality: Leveraging deep‐learning dose prediction for adaptive radiotherapy

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose Balancing quality and efficiency has been a challenge for online adaptive therapy. Most systems start the online re‐optimization with the original planning goals. While some systems allow planners to modify the planning goals, achieving a high‐quality plan within time constraints remains a common barrier.
Sean J. Domal, Austen Maniscalco, Justin Visak, Michael Dohopolski, Dominic Moon, Vladimir Avkshtol, Dan Nguyen, Steve Jiang, David Sher, Mu‐ Han Lin   +9 more
wiley   +1 more source

A comparative analysis of deep learning architectures with data augmentation and multichannel input for locoregional breast cancer radiotherapy

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose Studies on deep learning dose prediction increasingly focus on 3D models with multiple input channels and data augmentation, which increases the training time and thus also the environmental burden and hampers the ease of re‐training. Here we compare 2D and 3D U‐Net models with clinical accepted plans to evaluate the appropriateness of
Rosalie Klarenberg, Nienke L. M. Bakx, Coen W. Hurkmans   +2 more
wiley   +1 more source

MOBILE LEARNING: CONTEXT ADAPTATION AND SCENARIO APPROACH

Открытое образование (Москва), 2016
The paper proposes a model of an open architecture for component context-dependent systems of computer training to the needs of the software applications of intelligent learning environments and adaptive learning systems.
Vladimir V. Kureichik, Sergey I. Rodzin, Lada S. Rodzina   +2 more
doaj   +1 more source

Design of Personalized Learning Paths in Traditional LMS

Современные информационные технологии и IT-образование, 2020
Personalized learning is one of the most significant trends in education. The article discusses the individualization of learning to use traditional LMS based on adaptive learning and artificial intelligence technologies.
Julia Peryazeva, Roman Kalganov
doaj   +1 more source

Geometric and dosimetric evaluation of a commercial AI auto‐contouring tool on multiple anatomical sites in CT scans

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Current radiotherapy practices rely on manual contouring of CT scans, which is time‐consuming, prone to variability, and requires highly trained experts. There is a need for more efficient and consistent contouring methods. This study evaluated the performance of the Varian Ethos AI auto‐contouring tool to assess its potential integration into
Robert N. Finnegan, Alexandra Quinn, Patrick Horsley, Joseph Chan, Maegan Stewart, Regina Bromley, Jeremy Booth   +6 more
wiley   +1 more source