Results 181 to 190 of about 1,099,650 (333)

Value Prediction for Spatiotemporal Gait Data Using Deep Learning

Human gait has been commonly used for the diagnosis and evaluation of medical conditions and for monitoring the progress during treatment and rehabilitation. The use of wearable sensors that capture pressure or motion has yielded techniques that analyze the gait data to aid recovery, identify activity performed, or identify individuals.
Cavanagh, Ryan, Trajkovic, Jelena, Zhang, Wenlu, Khoo, I-Hung, Krishnan, Vennila   +4 more
openaire   +2 more sources

Complexity at Mesoscales: A Common Challenge in Developing Artificial Intelligence

Engineering, 2019
Exploring the physical mechanisms of complex systems and making effective use of them are the keys to dealing with the complexity of the world. The emergence of big data and the enhancement of computing power, in conjunction with the improvement of ...
Li Guo, Jun Wu, Jinghai Li
doaj  

RepliChrom: Interpretable machine learning predicts cancer‐associated enhancer‐promoter interactions using DNA replication timing

iMeta, EarlyView.
RepliChrom is an interpretable machine learning model that predicts enhancer‐promoter interactions using DNA replication timing across multiple cell types. By integrating replication timing with chromatin interaction data from multiple experimental platforms, it accurately distinguishes true interactions and reveals promoter‐region signals as key ...
Fuying Dao, Benjamin Lebeau, Crystal Chia Yin Ling, Mi Yang, Xueqin Xie, Melissa Jane Fullwood, Hao Lin, Hao Lyu   +7 more
wiley   +1 more source

Deep Learning for Super-resolution Ultrasound Imaging with Spatiotemporal Data

Super-resolution ultrasound imaging (SRUS) is an active area of research as it brings up to a ten-fold improvement in the resolution of microvascular structures. The limitations to the clinical adoption of SRUS include long acquisition times and long image processing times.
Redfern, Arthur David, Brown, Katherine G.   +1 more
openaire   +2 more sources

Enhance decoding of lower limb motor imagery‐electroencephalography patterns by Riemannian clustering

Interdisciplinary Medicine, EarlyView.
Abstract Brain‐Computer Interface (BCI) based on motor imagery (MI) has attracted great interest as a new rehabilitation method for stroke. Riemannian geometry‐based classification algorithms are widely used in MI‐BCI due to their strong robustness and generalization capabilities.
Xinwei Sun, Tuo Liu, Kun Wang, Lincong Pan, Lin Meng, Xinmin Ding, Weibo Yi, Minpeng Xu, Dong Ming   +8 more
wiley   +1 more source

Generative artificial intelligence in medical imaging: Current landscape, challenges, and future directions

Interdisciplinary Medicine, EarlyView.
This review explores the applications of Generative AI (GAI) in medical imaging, with emphasis on its potential to enhance AI training and personalized medicine. The study comprehensively examines frameworks for evaluating the validity of GAI‐generated images while identifying critical challenges including model bias, data augmentation reliability, and
Wenle He, Xuewei Wu, Zhe Jin, Jie Sun, Xiaofeng Jiang, Shuixing Zhang, Bin Zhang   +6 more
wiley   +1 more source

Shining a Light on the Future of Biophotonics

Journal of Biophotonics, EarlyView.
ABSTRACT Biophotonics—the interdisciplinary fusion of light‐based technologies with biology and medicine—is rapidly transforming research, diagnostics, and therapy across various domains. This white paper, developed in conjunction with the International Congress on Biophotonics 2024, offers a comprehensive overview of the current landscape and future ...
Francesco Baldini, Kishan Dholakia, Paul French, Orlando Guntinas‐Lichius, Achim Kohler, Werner Mäntele, Laura Marcu, Ronald Sroka, Siva Umapathy, Juergen Popp   +9 more
wiley   +1 more source

Humans exhibit associative symmetry in the absence of backward training and stimulus overlap

Journal of the Experimental Analysis of Behavior, EarlyView.
Abstract A recent survey of the evidence on associative symmetry in humans revealed that nearly all the demonstrations either unintentionally trained backward stimulus pairings and/or had a temporal overlap between the stimuli being trained. We consider these criticisms and improve on our own method of “associative networks.” In this method ...
Victor M. Navarro, Edward A. Wasserman
wiley   +1 more source

Five foundational tools for managing metadata from the USDA Long‐Term Agroecosystem Research (LTAR) Network

Journal of Environmental Quality, EarlyView.
Abstract The United States Department of Agriculture Long‐Term Agroecosystem Research (LTAR) Network comprises 19 sites and has collectively produced nearly one petabyte of data. Data include time‐series measurements, remotely sensed imagery, and high‐throughput environmental data from field and laboratory instrumentation.
Nicole E. Kaplan, Gerardo Armendariz, Shefali Azad, Bryan R. Carlson, William A. White, Lori J. Abendroth, Alisa W. Coffin, Vanessa S. Gordon, Jude E. Maul, William Osterholz, Jonathan Sears   +10 more
wiley   +1 more source

Evaluation of Real‐Time Cardiovascular Flow MRI Using Compressed Sensing in a Phantom and in Patients With Valvular Disease or Arrhythmia

Journal of Magnetic Resonance Imaging, EarlyView.
Background Real‐time (RT) phase contrast (PC) flow MRI can potentially be used to measure blood flow in arrhythmic patients. Undersampled RT PC has been combined with online compressed sensing (CS) reconstruction (CS RT) enabling clinical use. However, CS RT flow has not been validated in a clinical setting.
Tania Lala, Lea Christierson, Petter Frieberg, Daniel Giese, Peter Kellman, Nina Hakacova, Pia Sjöberg, Ellen Ostenfeld, Johannes Töger   +8 more
wiley   +1 more source