Results 181 to 190 of about 8,042,294 (313)

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

APPLICATION OF NEXT GENERATION TECHNOLOGIES [PDF]

Neuromuscular Disorders, 2021
Büşranur Çavdarli, G. Ceylan, E. Habiloglu, Ö. Yayıcı Köken, S. Bilen, C. Semerci Gündüz, E. Tuncez   +6 more
openaire   +3 more sources

Technology based Health care an Indian perspective : opportunities and challenges [PDF]

Healthcare technology is in use for a decade or more in India. The use of technology in healthcare especially in public health is very common due to the recent development in Information Communication Technology.
Kannan, Srinivasan
core   +1 more source

Stereotactic radiotherapy for metastatic brain tumors: A comparative analysis of dose distributions among VMAT, Helical TomoTherapy, CyberKnife, Gamma Knife, and ZAP‐X

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract This study evaluates various radiotherapy techniques for treating metastatic brain tumor (BT), focusing on non‐coplanar volumetric modulated arc radiotherapy (NC‐VMAT), coplanar VMAT (C‐VMAT), Helical TomoTherapy (HT), CyberKnife (CK), Gamma Knife (GK), and ZAP‐X.
Toshihiro Suzuki, Masahide Saito, Ryutaro Nomura, Hikaru Nemoto, Naoto Yanagisawa, Ryuma Sawada, Zennosuke Mochizuki, Naoki Sano, Hiroshi Onishi, Hiroshi Takahashi   +9 more
wiley   +1 more source

Practical challenges in data‐driven interpolation: Dealing with noise, enforcing stability, and computing realizations

International Journal of Adaptive Control and Signal Processing, EarlyView., 2023
Summary In this contribution, we propose a detailed study of interpolation‐based data‐driven methods that are of relevance in the model reduction and also in the systems and control communities. The data are given by samples of the transfer function of the underlying (unknown) model, that is, we analyze frequency‐response data.
Quirin Aumann, Ion Victor Gosea
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

Data‐driven performance metrics for neural network learning

International Journal of Adaptive Control and Signal Processing, EarlyView., 2023
Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri, Mauro Gaggero, Marcello Sanguineti   +2 more
wiley   +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

Technology-enhanced practice competencies: scoping review and novel model development. [PDF]

Front Digit Health
Perle JG, Chandran DN, Brezler E, Coleman M, Deziel J, Nahhas P, McDonald G, Jent JF.   +7 more
europepmc   +1 more source

Toward a human‐centric co‐design methodology for AI detection of differences between planned and delivered dose in radiotherapy

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Introduction Many artificial intelligence (AI) solutions have been proposed to enhance the radiotherapy (RT) workflow, but limited applications have been implemented to date, suggesting an implementation gap. One contributing factor to this gap is a misalignment between AI systems and their users.
Luca M. Heising, Frank Verhaegen, Stefan G. Scheib, Maria J. G. Jacobs, Carol X. J. Ou, Viola Mottarella, Yin‐Ho Chong, Mariangela Zamburlini, Sebastiaan M. J. J. G. Nijsten, Ans Swinnen, Michel Öllers, Cecile J. A. Wolfs   +11 more
wiley   +1 more source