Articles tagged with Image Analysis
The IceCube Neutrino Observatory has produced an image of the Milky Way using neutrinos, revealing it is a neutrino desert. The observation suggests the galaxy produces significantly fewer high-energy neutrinos than distant galaxies.
The PETAL sensor patch measures five biomarkers to assess wound inflammation and infection, providing timely medical intervention. It is a low-cost, versatile, and customizable solution for holistic wound monitoring.
Researchers developed a computational technique combining genomic and tau PET imaging data to identify four subtypes of Alzheimer's disease. The integrated approach also revealed top genes associated with each subtype. This personalized diagnostic technique has potential for broad utility across various diseases.
New analysis reveals that irony is a key medium for the spread of toxic ideas online, particularly in contemporary politics and radical online communities. Irony helps people navigate major political and economic changes, mobilize people around unclear or contradictory ideologies.
Researchers created a new method, RESORT, to image and analyze living systems in unprecedented detail. The technique combines benefits of super-resolution fluorescence and vibrational imaging, allowing for high spatial resolution and analysis of complex interactions.
A new study published in JMIR Cardio reveals the potential of AI-QCA in analyzing coronary angiography, providing accurate measurements of coronary stenotic lesions similar to IVUS. This technology has the potential to improve patient outcomes and support clinical decision-making.
Researchers at UCLA have developed a new method to bioprint miniature tumor organoids that can mimic the function and architecture of real tumors. This allows for the accurate measurement of individual organoids, enabling the identification of personalized treatments for people with rare or hard-to-treat cancers.
Researchers develop AI-based method to quantify cracking patterns in reinforced concrete structures, enabling more accurate and efficient assessments of structural damage. The approach uses graph theory and machine learning algorithms to create a unique 'fingerprint' for each set of cracks, allowing for quick and consistent evaluations.
A team of scientists has developed an automated algorithm to reconstruct the shape of each neuron inside a light microscopy image using deep learning. This breakthrough addresses the challenge of generalizing algorithms across diverse species, brain locations, developmental stages, and microscopy image sets.
Researchers have developed new methods to analyze dry-coated drug particles using 3D Raman mapping, which can effectively slow down dissolution rates. Another study introduces a novel technique for detecting complex traditional Chinese medicines, preventing adulteration and counterfeiting.
A new camera technology developed by Aarhus University and Newtec Engineering A/S aims to make it easier to recycle plastic materials. The technology uses hyperspectral imaging to analyze the chemical composition of plastic waste, allowing for the removal of unwanted additives that may be banned or harmful.
Researchers used deep learning to analyze drone images of pine trees, detecting pine processionary moth larvae nests with high accuracy. The method can be applied in various settings to inform tree health managers about potential threats.
Survivors of firearm injury experience chronic PTSD symptoms and poor health-related quality of life, emphasizing the need for comprehensive care. Screening for chronic kidney disease could be cost-effective for US adults starting at age 35.
A team from the University of Illinois has created 3D reconstructions of C4 plant cell structures, revealing new information on chloroplast size and plasmodesmata distribution. This work aims to improve modeling and production of biodiesel and biojet fuel.
Researchers developed an AI-powered tool to identify and measure coral reef halos globally, providing a new method for monitoring ecosystem health. The tool enables efficient tracking of reef ecosystems' function at large scales, improving understanding and management of coral reefs.
Dr. Miaomiao Zhang's research aims to automate late mechanical activation detection from cardiac magnetic resonance images using machine learning and AI techniques. This could lead to more accurate placement of CRT electrodes and improved patient outcomes.
Researchers developed EmbryoNet, an automated image analysis software that uses AI to detect and classify developmental defects in fish embryos. The software outperforms human experts in terms of speed and accuracy, making it a valuable tool for investigating the mechanisms of drug action and studying embryonic development.
A diagnostic study using 4,095 retinal fundus images found that biomarker-based AI algorithms can be susceptible to racial bias, even when trained on raw images. This issue highlights the need for careful evaluation of AI performance in diverse populations.
A new onsite deep-learning based FFR-CT algorithm demonstrates excellent diagnostic performance, with high interobserver and intraobserver reproducibility. The algorithm showed AUC of 0.975, sensitivity of 93.5%, and specificity of 97.7% for hemodynamically significant stenosis.
A study by University of Maryland School of Medicine found that AI algorithms used in medical imaging lack demographic data and bias evaluations. This can exacerbate existing health disparities worldwide. Researchers encourage future competitions to prioritize fairness among different groups.
A cohort study of 196 people treated with tecovirimat found that HIV status did not impact treatment outcomes. Only 22% of participants experienced nonsevere adverse effects, and 4 serious adverse events occurred, with 3 being persons with HIV. The findings contradict previous uncertainty about HIV's role in mpox acquisition.
A team of experts identified 29 sources of bias in AI/ML models for medical imaging, including data collection, preparation, and deployment. The study provides a comprehensive roadmap for mitigating these biases and ensuring fairness, equity, and trust in AI/ML models.
Astronomers have captured new images of M87*, revealing a thicker, fluffier ring that is 50% larger than the initial image. The team detected plasma from an accretion disk and observed a relativistic jet blasting out from the black hole.
Computational modeling and simulations allow for reconstruction of 3D conformations with atomistic resolution from topographic resolution-limited AFM images. This enables automated recognition of biomolecular shape changes and feature assignment, including amino acid residue identification on the molecular surface.
Researchers have developed a non-invasive technique using laser speckle imaging to visualize microvasculature in donor hearts and detect abnormal blood flow. The method enables precise visualization of blood circulation, potentially identifying hearts suitable for transplantation.
The City University of Hong Kong has developed a novel electron microscope that combines scanning and transmission electron microscope modes in a compact format. The device can produce high-resolution images in five minutes, enabling the study of atom dynamics and beam-sensitive materials.
A study presented at the ARRS Annual Meeting reveals institutional variability in projection order and image acquisition timing for CEM protocol. Earlier-obtained recombined imaging is significantly preferred for cancer lesion characterization, with a trend towards prioritizing imaging the side of pathology.
A new study at Umeå University reveals that tick-borne encephalitis virus infects distinct brain cell types and regions depending on the immune system's activation status. The researchers mapped the virus's behavior in the brain, identifying specific areas and cells infected by TBE virus.
Researchers developed an AI-based method for virtual staining of histopathological tissue samples, producing images that closely resemble actual chemical-stained samples. This reduces chemical burden and manual work while enabling the use of tissues for other purposes.
Researchers propose URDRN to effectively recover image details and extract accurate rain information. The model outperforms state-of-the-art methods in both subjective and objective evaluation metrics for synthetic and real rain images.
A new deep learning-based model estimates breast density with high precision, correlating it to cancer risk. The model's performance is comparable to that of human experts, but it can be trained faster and on smaller datasets.
A team of researchers has developed a highly sensitive imaging method to detect heavy metals like cadmium in cocoa beans. The study found that cadmium accumulates primarily in the outer shell of the bean and can be reduced through improved processing steps, which could minimize exposure.
Researchers have developed a new microfluidic chip that enables the visualization of rare cell types and disease patterns in tissue. This method combines imaging and sequencing techniques, providing spatially resolved information about individual cells and their environment.
Researchers have developed an AI algorithm that can remove atmospheric blur from astronomical images, resulting in more accurate scientific measurements and clearer data. The tool produces faster and more realistic images than current methods, producing 38.6% less error compared to classic methods.
Researchers from the Smidt Heart Institute found that individuals with spherical hearts are 31% more likely to develop atrial fibrillation and 24% more likely to develop cardiomyopathy. The study identified four genes associated with cardiomyopathy and a greater risk of developing atrial fibrillation.
Researchers used a novel brain imaging technique to visualize subtle brain changes in pre-symptomatic Alzheimer's patients. They found that individualized brain fingerprints can detect variations in brain function associated with worsening information processing.
Researchers have developed a non-invasive method to track human aging using retinal scans, which are less expensive and more accurate than other aging clocks. The study found that changes in the eye can provide an actionable evaluation of gero-protective therapeutics, offering a new tool for tracking aging.
Researchers developed a novel design for the chip using a crossbar layout, outperforming state-of-the-art photonic counterparts in terms of scalability and technical versatility. The synergy of powerful photonics with the novel crossbar architecture enables next generation neuromorphic computing engines.
An international team developed a novel method for evaluating AI interpretability methods to decipher the basis of AI reasoning and possible biases. The approach helps users understand what influences AI results and whether they can be trusted, especially in medical applications where AI-powered decisions can impact health and lives.
Researchers developed an image analysis algorithm that can automatically measure Arabidopsis thaliana stomatal aperture with high accuracy and speed. The technology also includes a portable imaging device for non-destructive observation using intact plants, allowing for rapid measurement of subtle changes in stomatal aperture.
Researchers found that increasing melanin levels in human skin reduces Cherenkov emission intensity, while blood concentration affects different color channels. The study suggests using multispectral signatures to correct attenuated signals based on patient's blood volume or skin color.
A cross-disciplinary team developed a convolutional neural network to analyze microscopy images of chromosomes with cohesion defects. The algorithm achieved 73.1% accuracy in classifying new images, streamlining experiments with chromosome analysis.
A team from Chalmers University of Technology has developed a method to observe the formation of lithium microstructures in real-time using X-ray tomographic microscopy. This breakthrough aims to improve the safety and capacity of lithium metal batteries, which could replace traditional lithium-ion batteries in the future.
Researchers developed AI models based on UNet and MobileNet architectures to analyze standardized abnormalities in CT images, accurately identifying object presence and confidence. These models achieved an absolute percentage error of less than 5 percent, comparable to human professionals.
The UTSA ScooterLab will collect data on riders' mobility, context and environment to improve sustainable transportation solutions. The project aims to transform the way we think about micro-mobility.
Axillary lymphadenopathy after a COVID-19 vaccine booster dose resolves at a mean of 102 days. Findings support follow-up intervals of at least 12 weeks to avoid delaying screening mammography for suspected vaccine-related lymphadenopathy.
A novel AI architecture, relational reasoning network, accurately identifies anatomical landmarks in CT scans for orthodontic treatments. The model learns spatial relationships between landmarks without explicit image segmentation, achieving accuracy comparable to conventional methods.
A Swedish study of children's drawings during the COVID-19 pandemic found that young children drew detailed images of illness, death, and cancelled activities. The researchers also discovered a high level of health literacy among the children, showing they understood the virus's characteristics and symptoms.
A groundbreaking study used AI and high spatial resolution imagery to analyze over 300,000 satellite images and identify nearly 10 billion trees in sub-Saharan Africa. The research reveals that tree density and carbon stocks in the arid zone are underestimated, with a total carbon stock of 0.84 billion tonnes.
Astronomers have discovered a companion galaxy to z~4 starburst galaxy, rich in metals and dust. The galaxy, labeled SPT0418-SE, is remarkably mature considering its young age of 1.4 billion years.
Researchers developed MoBIE, a user-friendly tool for sharing and exploring large image data sets. The tool allows for visualization and analysis of huge amounts of data from hundreds of sources, making it easier for researchers to analyze and interpret microscopy data worldwide.
Researchers have developed a hand-held device that can rapidly and accurately identify lesions in the mouth that will develop into cancer. The Liverpool Diagnostic Infrared (LDIR) Wand employs infrared lasers to predict future cancer risk based on machine learning analysis.
A recent study published in Human Reproduction found a significant association between prenatal alcohol exposure and changes in children's facial features. The research used AI to analyze three-dimensional images of children's faces at ages nine and thirteen, revealing that mothers' alcohol consumption before and during pregnancy can h...
Researchers used MRI scans and texture analysis to differentiate between two types of benign oral bone lesions, ameloblastomas and odontogenic keratocysts. The study found that certain pixel and voxel organizational parameters in the MRI images were statistically significant, enabling more accurate diagnosis and treatment planning.
A new project aims to expand the network of European bioimage analysts and make cutting-edge techniques accessible globally. The initiative will support career development for young researchers and enhance collaboration among imaging and life science communities.
The new automated method, called HybridFlow, uses large-scale aerial images to produce precise 3D models of cityscapes and landscapes. This technology has potential applications in natural disaster risk assessment and mitigation, enabling informed decision-making and evaluation of risk-mitigating factors.
Researchers developed high-throughput Raman microscope for rapid large-area imaging hundreds of times faster than traditional approach. The new technique enables label-free molecular analysis and multiplex chemical imaging, holding promise for efficient medical diagnoses and drug development.
New research from the University of Georgia reveals that artificial intelligence can be used to find planets outside our solar system. Machine learning can analyze environments where planets are still forming, helping scientists overcome difficulties such as distance and data thickness.
A mindfulness-based intervention program significantly decreased subjective anxiety and improved emotional regulation in AN patients. The study found changes in brain regions involved in anxiety, emotion arousal, and self-referential processing.
The JIPipe software enables automated analysis of images generated in research without requiring programming skills. Users can create flowcharts and perform automatic image analyses using artificial intelligence.