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(SD2019-220) Spatiotemporal resolution enhancement of biomedical images

Cardiac MRI is the clinical reference standard for visual and quantitative assessment of heart function. Specifically, cine balanced steady-state free precession (SSFP) can yield cardiac images with high myocardium–blood pool contrast for evaluation of left ventricular (LV) function. However, MRI suffers from long acquisition times, often requiring averaging across multiple heartbeats, and necessitates a trade-off among spatial resolution, temporal resolution, and scan time. Clinically, radiologists are forced to balance acquisition time with resolution to fit clinical needs, and certain applications such as real-time imaging may require small acquisition matrices. Image scaling is typically performed by using conventional upscaling methods, such as Fourier domain zero padding and bicubic interpolation. These methods, however, do not readily recover spatial detail, such as the myocardium–blood pool interface or delineation of papillary muscles.

Neural Network Machine Learning Applied to Diagnose Acute Kidney Injury

Researchers at the University of California, Davis have developed machine learning models to enhance the accessibility and accuracy of acute kidney injury (AKI) testing.

Advanced Imaging By LASER-Trained Algorithms Used To Process Broad-Field Light Photography and Videography

Diagnosing retinal disease, which affects over 200 million people worldwide, requires expensive and complicated analysis of the structure and function of retinal tissue. Recently, UCI developed a training algorithm which, for the first time, is able to assess tissue health from images collected using more common and less expensive optics.

Covidseeker. Digital Contact Tracing And Hotspotting In Real-Time

UCSF PIs developed a novel software platform for COVID-19 contact tracing and hotspotting called COVIDseeker. Covidseeker looks back in time and may be able to recreate people’s movements when infection rates were rising and falling in the spring and summer of 2020, giving epidemiologists an invaluable source of data as they try to predict what is going to happen in the fall and winter.This digital health invention has applications broader than COVID-19. The software can potentially be leveraged for other infectious diseases, treating obesity, and controlling smoking or alcohol addiction by showing where and when people are when they smoke, what are the triggers and how their location contributes to the risk of developing a particular disease.

Automated Histological Image Processing tool for Identifying and Quantifying Tissue Calcification

Researchers at UCI have developed a method of identifying, quantifying, and visualizing tissue with calcification. The image processing tool can automatically characterize calcium deposits in CT images histological tissue, especially when it has accumulated in unusual places in the body.

Virtual Reality For Anhedonia Program

UCLA researchers in the Department of Psychology have developed a behavioral training program for the improvement of anhedonia.

Artificial Intelligence Enabled Control of Hemodynamics and Anesthesia in Surgery Patients

UCLAresearchers from the Department of Mechanical and Aerospace Engineering and the Department of Anesthesiology have developed a method for artificial intelligence guided control of anesthetics and other medications during surgery and in the Intensive Care Unit (ICU).

Automated Biomarker Prediction Using Optical Coherence Tomography

UCLA researchers in the Department of Computational Medicine have developed a computer program capable of automatically and accurately diagnosing optical diseases using OCT.

Mediator-Free Electroenzymatic Sensing with Enhanced Sensitivity and Selectivity for Wearable Metabolite and Nutrient Monitoring Applications

UCLA researchers in the Department of Electrical and Computer Engineering have developed a wearable electroenzymatic sensor for non-invasive monitoring of metabolites and nutrients. The sensor has been successfully tested in human subjects to be highly sensitive and selective, making it ideal for monitoring and improving individual well-being.

Wearable Voltammetric Monitoring of Electroactive Drugs

UCLA researchers in the Department of Electrical and Computer Engineering have developed a voltammetric wearable device capable of monitoring electroactive drug circulation and abundance in biofluids. This non-invasive monitoring system can be used for electroactive drug therapy management, drug compliance/abuse monitoring, drug-drug interaction studies, and personalized dosing.

IgEvolution: A Novel Tool for Clonal Analysis of Antibody Repertoires

Constructing antibody repertoires is an important error-correcting step in analyzing immunosequencing datasets that is important for reconstructing evolutionary (clonal) development of antibodies. However, the state-of-the-art repertoire construction tools typically miss low-abundance antibodies that often represent internal nodes in clonal trees and are crucially important for clonal tree reconstruction. Thus, although repertoire construction is a prerequisite for follow up clonal tree reconstruction, the existing repertoire reconstruction algorithms are not well suited for this task because they typically miss low-abundance antibodies that often represent internal nodes in clonal trees and are crucially important for clonal tree reconstruction.

A Fully‐automated Deep Learning System (software code) for the Detection, Prognosis, and Visualization of Pulmonary Disease.

The majority of state‐of‐the‐art lung segmentation algorithms in the literature do not simultaneously segment lungs, lung lobes, and airway in a single algorithm. Additionally, automated algorithms typically perform the segmentation task on a series of 2D slices, which can reduce segmentation accuracy of anatomical structures (i.e. lung lobes) that may require contextual information across all three spatial dimensions. Many existing algorithms also have not been validated on chest CTs across a wide variety of conditions to evaluate algorithm generalizability. Currently, quantification of respiratory measurements requires a radiologist, trained analyst, or technician to recognize, identify, and manually annotate anatomical landmarks such as the lung lobes or airway in the chest. A fully‐automated deep learning system may eliminate the need for manual analysis, thereby improving efficiency and expanding applicability to a large number of CTs.

Computational Cytometer Based On Magnetically-Modulated Coherent Imaging And Deep Learning

UCLA researchers in the Department of Electrical & Computer Engineering have designed and built a computational cytometer capable of detecting rare cells at low concentration in whole blood samples. This technique and instrumentation can be used for cancer metastasis detection, immune response characterization and many other biomedical applications.

Software - Unified algorithm for data cleaning, source separation, and imaging of electroencephalographic signals: Recursive Sparse Bayesian Learning (RSBL)

Electroencephalographic source imaging (a.k.a. magnetic/electric or M/EEG source imaging, ESI, or brain electrical tomography) usually depends upon sophisticated signal processing algorithms for data cleaning, source separation and imaging. Typically, these problems are addressed separately using a variety of heuristics, making it difficult to systematize a methodology for extracting robust brain source images on a wide range of applications.

Automatic Identification of Ophthalmic Medication for The Visually Impaired

Researchers at UCI are developing technology that allows visually impaired patients to use their smartphones to take pictures of their eye medication/eye drop bottles. The technology will recognize the eye medication and verbally communicate the medication and will audibly confirm the medication along with the instructions on use.

cBCI: Method and System for Diagnosing and Training Cognitive Fitness and Targeted Neural Network Function Underlying Cognitive Fitness in an Integrated Digital Approach

The inventors have created a brain computer interface (BCI) that serves as a diagnostic and training tool of cognitive abilities and neural network function.

Method and Apparatus for Movement Therapy Gaming System

Rehabilitation therapy, while an important tool for the long term recovery of patients affected by brain injury or disease, is expensive and requires one-on-one attention from a certified healthcare professional. UCI researchers have developed a computer-based system that provides arm movement therapy for patients. The system allows patients to independently practice hand and arm movements, improving therapeutic outcomes, while reducing hospital visits and cost for both patients and healthcare providers.

Financial Model for Informing Value-Based Payment Decisions

Researchers led by David Johnson from the Department of Urologic Oncology and the West Los Angeles Veteran’s Affairs Medical Center have developed an interactive web platform that predicts the financial outcomes for various stakeholders (physicians, hospitals, and payers) of transitioning from fee-for-service to bundled payments for robotic radical prostatectomy.

DARTS: Deep Learning Augmented RNA-seq Analysis of Transcript Splicing

Researchers led by Yi Xing have developed a novel deep learning algorithm to detect alternative splicing patterns in RNA-seq data

Combination of a drug with low level light therapy (LLT) for treatment of wounds

This is a combination of a drug and light technology for the purpose of accelerating the healing of wounds on the skin, ulcers, and elsewhere in the body. Both methods have been shown to accelerate wound healing, and combining the two will potentially result in more rapid healing than either would alone.  

Multimodal food journaling

Researchers at UCI have developed a hands-free, unobtrusive smartphone-based application for automatic food journaling. The app, which operates via voice command, is interactive and highly engaging thereby encouraging long-term user participation.  

A New Human-Monitor Interface For Interpreting Clinical Images

UCLA researchers in the Department of Radiological Sciences have invented a novel interactive tool that can rapidly focus and zoom on a large number of images using eye tracking technology.

A Method For Accurate Parametric Mapping Based On Characterization Of A Reference Tissue Or Region

UCLA researchers in the Department of Radiology have developed a novel method that addresses a common issue of MRI imaging misinterpretation due to the high field effects of B1+ inhomogeneity.

Deep-Learning-Based Computerized Prostate Cancer Classification Using A Hierarchical Classification Framework

UCLA researchers in the Department of Radiological Sciences have developed a deep-learning-based computerized algorithm for classification of prostate cancer using multi-parametric-MRI images.

Immersive Virtual Reality To Manage Pain

Researchers led by Mark Cohen from the Department of Psychiatry at UCLA have developed a virtual reality-based therapy to manage chronic pain.

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