Articles tagged with Image Analysis
A new method developed by Osaka Metropolitan University accurately predicts housing prices in Osaka City, with neighborhood perception being a key factor. The approach achieves nearly 75% accuracy by combining existing property data with machine-learning-processed street view images.
A recent study introduces an innovative method for analyzing body composition, providing accurate assessments of body fat and muscle distribution. The approach utilizes deep, nonlinear methods to enhance estimation accuracy, surpassing previous linear models.
Researchers developed a novel microscopy technique to study metabolic changes in individual cancer cells at the single-cell level. They found that radiation treatment caused significant metabolic shifts in head and neck squamous cell carcinoma cells, particularly through the activation of HIF-1α.
Lauren Berger, a Texas A&M University doctoral student, has been awarded a prestigious FINESST grant from NASA to study Martian dunes. She aims to analyze the shapes and patterns of compound dunes on Mars using high-resolution images, comparing them to similar dunes on Earth.
A recent study published by researchers at the University of Liverpool has confirmed that Mesozoic fossils, including dinosaur bones and teeth, still preserve their original organic materials. The team used advanced mass spectrometry techniques to identify preserved collagen remnants in a well-preserved Edmontosaurus fossil.
The open-source AI model analyzes medical images, generates detailed reports, and answers clinical questions to streamline diagnostics and improve accuracy. BiomedGPT aims to democratize healthcare and reduce disparities amongst patients by providing easily accessible data to bolster underserved hospitals.
The EHT Collaboration unveils a new analysis of the supermassive black hole at the heart of galaxy M87, combining observations from 2017 and 2018. The study confirms the presence of a luminous ring with a shifted brightest region, indicating turbulent accretion disk dynamics.
Researchers found AI-based imaging technology improves disease diagnosis accuracy, particularly in cardiology, oncology, neurology, and ophthalmology. The technology also enhances diagnostic efficiency and reduces healthcare disparities by delivering high-quality diagnostics to underserved areas.
Researchers developed a precision medicine approach that uses high-tech microscopy to analyze individual immune cells and identify cellular phenotypes correlated with disease activity and therapeutic response. This study represents a major step forward in tailoring therapies for rheumatoid arthritis and other autoimmune diseases.
Professors Scott Acton and Mathews Jacob of UVA's Charles L. Brown Department of Electrical and Computer Engineering were named to the IEEE Signal Processing Society's 2025 Class of Distinguished Lecturers for their groundbreaking work in signal processing, artificial intelligence, and medical imaging.
The KAIST team developed a next-generation neuromorphic semiconductor-based integrated system that can learn and correct errors on its own. This technology will revolutionize AI use in everyday devices, making them faster, more private and energy-efficient.
A quasi-experimental study found that three-quarters of Mariupol's medical facilities were damaged, suggesting intentional targeting. The US State Department and Yale School of Public Health funded the research.
A study of British motherhood influencers found that 3 in 4 posts feature children, with half linked to sponsorship and financial gain. The researchers express strong trust in Instagram's safety features, suggesting a deliberate strategy rather than accidental sharing.
Researchers have developed a method to stretch and immobilize single DNA molecules, enabling detailed analysis. By controlling the shear force applied through liquid pressure flow and bonding the molecule to a substrate, scientists can distinguish between tiny biomolecular features with higher precision.
The BiliSG app uses machine learning to analyze skin color and predict bilirubin levels in newborns, offering a convenient alternative to traditional testing methods. With 100% sensitivity, the app has shown promising results in monitoring neonatal jaundice and reducing the risk of brain damage.
The SNU-Hyundai joint research developed an innovative analysis technique, e-LCTEM, to rapidly evaluate fuel cell catalyst durability and identify degradation mechanisms. This technology accelerates durability testing, reducing evaluation costs and paving the way for more efficient catalyst verification.
A Mount Sinai-led team has developed an automated machine learning method that analyzes video recordings of clinical sleep tests to detect REM sleep behavior disorder (RBD) with nearly 92% accuracy. RBD is a sleep condition that causes abnormal movements during REM sleep and is often an early sign of Parkinson's disease or dementia.
Lithium-sulphur batteries have high specific energy densities but are susceptible to degradation due to polysulphide and sulphur phase formation. Researchers investigated lithium-sulphur pouch cells using operando analysis, revealing new insights into cell component design and performance.
The ISS National Lab is soliciting flight concepts for technology advancement using the space-based environment of the International Space Station. The lab aims to develop and test products and processes with potential economic impact through this $650,000 funding opportunity.
A new USC study validates a biomarker linked to vascular dementia and suggests that glymphatic damage drives cognitive decline. The findings provide a potential pathway to explain how cognitive impairment arises, offering a target for clinical researchers seeking treatments for vascular dementia.
Researchers developed a non-invasive way to assess fat composition around the heart using magnetic resonance imaging. This technique may help doctors identify patients at greatest risk for cardiac problems and predict treatment outcomes.
Researchers at Karolinska Institutet used AI to analyze brain images from 70-year-olds and found that factors like inflammation and high glucose levels are associated with an older-looking brain. A healthy lifestyle, on the other hand, was linked to brains with a younger appearance.
A new tool developed by Penn State researchers uses computer vision and artificial intelligence to analyze placenta images, detecting abnormalities and risks such as neonatal sepsis. The PlacentaCLIP+ model has the potential to transform neonatal and maternal care in low- and high-resource settings.
Researchers at Okinawa Institute of Science and Technology create a metric to quantify clarity in digital images, enabling scientists to capture changes in structure during artistic processes and physical transformations. The metric is tested on digital artworks and physical systems, showing its versatility and effectiveness.
SPACe enables researchers to analyze large datasets in an efficient way, capturing the full diversity of cellular responses. The platform provides a more nuanced understanding of how drugs interact with cells, revealing insights into mechanisms beyond cell death.
A new technique for detecting long wave infrared photons of different wavelengths has been developed by UCF researchers. This method, based on a nanopatterned graphene, offers dynamic spectral tunability and ultrafast response times, surpassing existing cooled and uncooled detectors.
Fogarty's research aims to monitor language function and recovery in post-stroke patients using DOT. She hopes to establish the feasibility of brain-computer interfaces to restore inter-personal communication for post-stroke patients.
Silvia Blemker, a University of Virginia biomedical engineer, has been elected Fellow of the National Academy of Inventors (NAI) for her work on muscle health. Her patented technology, Image-based Identification of Muscle Abnormalities, uses advanced imaging and analytics to provide detailed insights into muscle health.
Researchers found that autistic children prioritize faces in different ways, particularly when first seeing them, using an exploratory pattern characterized by larger face regions of interest. This may be associated with autism-related symptomology and decreased visual sensitivity to face information.
The BioImage.IO Chatbot uses extensive language models and Retrieval-Augmented Generation (RAG) to provide real-time access to databases and minimize errors. It enables researchers to perform complex image analysis tasks in a simple manner, working directly with microscopes and laboratory equipment.
Researchers at CAMERA have developed an open-source markerless motion capture system using computer vision and deep learning methods. The system estimates joint positions from regular 2D image data, providing unobtrusive analysis of body movements.
Recent US data analysis reveals stabilization of breast reconstruction rates after mastectomy, with a decrease in disparities among racial and insurance groups. The study analyzed over 1.5 million patients from 2005 to 2017, finding that nearly one-third underwent immediate reconstruction, with a shift towards more equitable distribution.
Researchers at Karolinska Institutet found that patients with frontotemporal dementia show reduced brain activity when witnessing pain in others, leading to a lack of empathy. This study aims to increase understanding of this specific dementia disease and its impact on caregivers.
Researchers from Brazil and Spain developed computer models to estimate crop maturity and yield using remote sensing technologies, increasing productivity and reducing CO2 emissions. They also created a system to estimate soil moisture levels with high accuracy, helping growers manage water usage more efficiently.
Researchers analyzed seismic data from southern Israel and found weak ground motions caused by Hamas forces' unusual vehicle movement. This breakthrough highlights the potential of using seismic data to provide early warnings for future terrorist activities.
Researchers developed a new image-processing method to visually clarify the internal network structure of rubber at the nanoscale. The method, which integrates knowledge of rubber material with advanced mathematical techniques, enables automatic analysis of multiple samples and confirms its reliability.
This editorial introduces persistence landscapes as a mathematical method to identify and correct biases in medical imaging. Persistence landscapes offer a way to reduce random noise while preserving important details, making it easier for clinicians to focus on meaningful image parts.
Researchers introduce a new tool called 'persistence images' that uncovers hidden biases in medical imaging data, advancing fairness in healthcare AI. The technology helps detect bias, filter out noise, and improve overall accuracy, leading to more reliable diagnoses and better patient outcomes.
Researchers introduce persistence barcodes, a tool that reduces diagnostic bias and uncovers subtle details in medical images. The method preserves key structural details, enabling clearer insights into the data.
The study combines AI with fluorescence photography to enhance skin diagnostics, allowing for earlier detection of skin conditions such as acne, photoaging, and hyperpigmentation. AI can pinpoint early indicators of skin conditions with greater accuracy and monitor changes over time for data-driven decisions.
Scientists have investigated the degradation pathways of layered Li-rich oxide cathodes, revealing changes in morphology and structure with each charging cycle. The study provides valuable insights into the chemical processes involved in battery aging.
Scientists have identified new ways that bacteria interact with each other in the oral microbiome, finding that certain species bind more strongly than others. This groundbreaking discovery could lead to new insights into disease and the development of novel therapies.
The Einstein Foundation Berlin presents the €500,000 award to recognize institutions and researchers who improve research quality and robustness. Researchers like Elisabeth Bik, PubPeer, and PixelQuality are honored for their work on promoting quality and accountability in research.
A new approach using topological data analysis (TDA) enhances the reliability and reduces bias in AI systems used for medical diagnosis in radiology. TDA captures intricate features and provides a holistic view of medical images, leading to more accurate diagnoses and equitable patient care.
A new study reveals that an AI algorithm can accurately detect early-stage metabolic-associated steatotic liver disease (MASLD) in patients who meet the criteria, leaving 83% undiagnosed. This highlights the need for improved screening and diagnosis methods to prevent progression to advanced liver disease.
A deep learning AI model can identify pathology in images of animal and human tissue much faster and often more accurately than people, potentially revolutionizing disease-related research and medical diagnosis. The model was trained using images from past epigenetic studies and showed accuracy comparable to human experts.
An international team discovered three ultra-massive galaxies, each nearly as massive as the Milky Way, forming at unexpected speeds. The findings indicate that star formation in the early Universe was more efficient than previously thought, posing a challenge to existing galaxy formation models.
A team of researchers has created a model that predicts landslide risk in coastal areas, using high-resolution images to identify over 1,000 landslide points in São Sebastião, Brazil. The new methodology aims to provide more precise results and will be ready by the end of 2025.
A recent study by Ruhr-University Bochum found that 0.052% of Twitter accounts use AI-generated profile pictures, which are often linked to fake profiles spreading disinformation and propaganda. The researchers also found that these accounts had fewer followers and were more likely to be blocked by Twitter.
A new study from Virginia Tech shows large language models can assess human-made environments using street-view images, similar to traditional methods. LLM-based performance offers a more accessible tool for users in small to medium-sized cities, making it easier to manage smart urban infrastructure.
Researchers at The Jackson Laboratory have developed a new combination of imaging and computational methods to study immune cell interactions. The approach reveals that interactions between immune cells in the vicinity of breast cancer or melanoma can predict immune responses and patient outcomes.
A new study found that the heart sends signals to the brain to increase sleep after a heart attack, promoting healing and reducing inflammation. This communication between the heart and brain is mediated through the immune system and can lead to improved recovery and reduced risk of further cardiovascular events.
Researchers develop a novel edge-highlighting visualization technique to improve the clarity of complex 3D structures, revealing sharp edges and intricate details. The technique, tested on real 3D point cloud data, offers interactive rendering speeds and enhances depth perception.
Researchers at the University of Minnesota developed a new technique to visualize 2D radio images in 3D, revealing distinct shapes and structures of galaxies and massive black holes. The technique uses Faraday rotation to estimate distances and analyze material interactions, potentially altering previous models.
A new study finds that AI-powered models exhibit similar levels of accuracy as ophthalmologists in identifying infectious keratitis, a leading cause of corneal blindness worldwide. The AI models displayed a sensitivity and specificity of 89.2% and 93.2%, respectively, matching the diagnostic accuracy of human experts.
A UVA professor has developed a new computational algorithm to find tightly connected clusters, or triangle-dense subgraphs, within large networks. This breakthrough can help uncover suspicious activity in fraud detection and identify community dynamics on social media with greater precision.
Researchers developed an AI model that uses machine learning and combination theory to predict suspicious skin lesions. The new C4C Risk Score has an accuracy of 69% and significantly outperformed existing methods, including 7PCL and Williams score.
A Korean research team has successfully observed living organoids in real time at a high resolution using holotomography. The technology allows for long-term observation of dynamic changes and precise analysis of organoid responses to drug treatments.
Research from Trinity College Dublin found that lower sustained attention in adolescence can predict a larger increase in cigarette and cannabis use by age 23. The study suggests using early biomarkers like sustained attention to identify vulnerable adolescents and guide targeted interventions.
Researchers developed a method to reconstruct hyperspectral images from standard RGB images using deep machine learning. The technique achieved over 70% accuracy in predicting soluble solid content and 88% accuracy in dry matter content in sweet potatoes, with potential applications for the agricultural industry.