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Stain-Free, Rapid, and Quantitative Viral Plaque Assay Using Deep Learning and Holography (Case No. 2022-326)
Intro Sentence: UCLA researchers in the Department of Electrical and Computer Engineering have developed a rapid and stain-free quantitative assay using lens-free holography and deep learning to efficiently and cost-effectively determine the presence of viral plaque-forming units (PFUs) in samples. Background: A broad range of viruses have caused...
Published: 10/17/2024
|
Inventor(s):
Aydogan Ozcan
,
Yuzhu Li
,
Tairan Liu
Keywords(s):
Antiviral Drug
,
Artifical Intelligence (Machine Learning, Data Mining)
,
Assay
,
Bioassay
,
Computer Aided Learning
,
Diagnostic Markers & Platforms
,
Diagnostic Platform Technologies (E.G. Microfluidics)
,
Diagnostic Test
,
Digital Holography
,
Electrical
,
Electrical Brain Stimulation
,
Electrical Breakdown
,
Electrical Engineering
,
Electrical Impedance
,
Electrical Load
,
Electrical Load Equation Of State
,
Electrical Resistance And Conductance
,
Electrical Resistivity And Conductivity
,
Holography
,
Immunoassay
,
Immunoassay Sense (Molecular Biology)
,
Lentivirus Viral Vector
,
Machine Learning
,
Machine Learning Autonomous Car Gradient Descent
,
Machine Learning Pain Management
,
Machine Learning Particulates Global Climate Model
,
Network Analysis (Electrical Circuits)
,
Perceptual Learning
,
Plasmid Trabecular Meshwork Aqueous Humour Viral Vector
,
Targets And Assays
,
Transcutaneous Electrical Nerve Stimulation
,
Transfection Viral Vector
,
Unsupervised Learning
,
Viability Assay
,
Viral Delivery Systems
,
Viral Envelope
,
Viral Load
Category(s):
Diagnostic Markers
,
Diagnostic Markers > Targets And Assays
,
Electrical
,
Life Science Research Tools
,
Life Science Research Tools > Research Methods
,
Life Science Research Tools > Other Reagents
,
Software & Algorithms > Artificial Intelligence & Machine Learning
2019-737 Deep Learning-Based Color Holographic Microscopy
Summary: UCLA researchers in the Department of Electrical Engineering have developed a novel deep learning-based method that performs high-fidelity color image reconstruction using a single hologram. Background: Pathology slides are currently the gold standard in diagnostics for many diseases. Accurate color representations of well stained pathology...
Published: 7/19/2023
|
Inventor(s):
Aydogan Ozcan
,
Yair Rivenson
,
Tairan Liu
,
Yibo Zhang
,
Zhensong Wei
Keywords(s):
Artifical Intelligence (Machine Learning, Data Mining)
,
Artificial Neural Network
,
Computer Aided Learning
,
Confocal Microscopy
,
Cytopathology
,
Digital Holography
,
Electron Microscope
,
Fluorescence Microscope
,
Fluorescence-Lifetime Imaging Microscopy Leading Lights
,
Histology
,
Histopathology
,
Holography
,
Image Resolution
,
Imaging
,
Immunohistochemistry
,
Machine Learning
,
Magnetic Resonance Imaging Pathology
,
Medical Imaging
,
Microscope
,
Microscopy
,
Microscopy And Imaging
,
Neuropathology
,
Pathogen
,
Pathogenesis
,
Pathophysiology
,
Perceptual Learning
,
Unsupervised Learning
,
Wavelength
Category(s):
Electrical
,
Electrical > Imaging
,
Life Science Research Tools
,
Life Science Research Tools > Microscopy And Imaging
,
Software & Algorithms
,
Software & Algorithms > Artificial Intelligence & Machine Learning
,
Diagnostic Markers
,
Medical Devices > Medical Imaging
2022-140 Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue Using Deep Learning
SUMMARY: UCLA researchers in the Departments of Bioengineering and Electrical and Computer Engineering have developed a deep learning-based algorithm that can detect and quantify the breast cancer marker human epidermal growth factor receptor 2 (HER2) in microscopic images without the need for time-consuming immunohistochemical staining (IHC). BACKGROUND:...
Published: 4/9/2024
|
Inventor(s):
Aydogan Ozcan
,
Yair Rivenson
,
Bijie Bai
,
Hongda Wang
Keywords(s):
Artifical Intelligence (Machine Learning, Data Mining)
,
Biomarker
,
Bladder Cancer
,
Breast Cancer
,
Cancer
,
Cancer Immunotherapy
,
Computer Aided Learning
,
Diagnostic Markers & Platforms
,
Diagnostic Test
,
Fluorescence
,
Fluorescent Labelling
,
HER2/Neu
,
Histology
,
Immune System
,
Immunohistochemistry
,
Life Science Research Tools
,
Machine Learning
,
Optics
,
Research Methods
,
Unsupervised Learning
Category(s):
Life Science Research Tools
,
Life Science Research Tools > Research Methods
,
Diagnostic Markers
,
Diagnostic Markers > Immunology
,
Medical Devices > Medical Imaging
,
Medical Devices > Medical Imaging > Fluorescence
,
Software & Algorithms > Artificial Intelligence & Machine Learning
,
Optics & Photonics