Results 11 to 20 of about 119 (119)

Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence

open access: yesAdvanced Materials Interfaces, EarlyView., 2023
This review discusses the use of Surface‐Enhanced Raman Spectroscopy (SERS) combined with Artificial Intelligence (AI) for detecting antimicrobial resistance (AMR). Various SERS studies used with AI techniques, including machine learning and deep learning, are analyzed for their advantages and limitations.
Zakarya Al‐Shaebi   +4 more
wiley   +1 more source

Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine

open access: yesFEBS Open Bio, EarlyView.
This review highlights how foundation models enhance predictive healthcare by integrating advanced digital twin modeling with multiomics and biomedical data. This approach supports disease management, risk assessment, and personalized medicine, with the goal of optimizing health outcomes through adaptive, interpretable digital simulations, accessible ...
Sakhaa Alsaedi   +2 more
wiley   +1 more source

A review of artificial intelligence in brachytherapy

open access: yesJournal 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   +4 more
wiley   +1 more source

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

open access: yesJournal 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   +6 more
wiley   +1 more source

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

open access: yesJournal 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   +11 more
wiley   +1 more source

Unsupervised non‐small cell lung cancer tumor segmentation using cycled generative adversarial network with similarity‐based discriminator

open access: yesJournal of Applied Clinical Medical Physics, EarlyView.
Abstract Background Tumor segmentation is crucial for lung disease diagnosis and treatment. Most existing deep learning‐based automatic segmentation methods rely on manually annotated data for network training. Purpose This study aims to develop an unsupervised tumor segmentation network smic‐GAN by using a similarity‐driven generative adversarial ...
Chengyijue Fang   +2 more
wiley   +1 more source

UR‐cycleGAN: Denoising full‐body low‐dose PET images using cycle‐consistent Generative Adversarial Networks

open access: yesJournal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose This study aims to develop a CycleGAN based denoising model to enhance the quality of low‐dose PET (LDPET) images, making them as close as possible to standard‐dose PET (SDPET) images. Methods Using a Philips Vereos PET/CT system, whole‐body PET images of fluorine‐18 fluorodeoxyglucose (18F‐FDG) were acquired from 37 patients to ...
Yang Liu, ZhiWu Sun, HaoJia Liu
wiley   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

open access: yesAdvanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani   +4 more
wiley   +1 more source

Structurally Colored Physically Unclonable Functions with Ultra‐Rich and Stable Encoding Capacity

open access: yesAdvanced Functional Materials, Volume 35, Issue 12, March 18, 2025.
This study reports a design strategy for generating bright‐field resolvable physically unclonable functions with extremely rich encoding capacity coupled with outstanding thermal and chemical stability. The optical response emerges from thickness‐dependent structural color formation in ZnO features, which are fabricated by physical vapor deposition ...
Abidin Esidir   +8 more
wiley   +1 more source

Carbon Nanotube 3D Integrated Circuits: From Design to Applications

open access: yesAdvanced Functional Materials, EarlyView.
As Moore's law approaches its physical limits, carbon nanotube (CNT) 3D integrated circuits (ICs) emerge as a promising alternative due to the miniaturization, high mobility, and low power consumption. CNT 3D ICs in optoelectronics, memory, and monolithic ICs are reviewed while addressing challenges in fabrication, design, and integration.
Han‐Yang Liu   +3 more
wiley   +1 more source

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