Articles tagged with Image Analysis
A study by Cornell University researchers found that online retail images of models have statistically lighter skin tones than videos of the same product and model. The discrepancy was also linked to 'tokenism,' where one model with darker skin is used to represent a range of diversity.
An artificial intelligence model trained on histological images of surgical specimens accurately classified patients with and without Crohn disease recurrence. The model revealed previously unrecognized differences in adipose cells and mast cell infiltration, enabling stratification by prognosis for postoperative Crohn disease patients.
A new study maps the distinct differences among cells in the retina's retinal pigment epithelium (RPE), which nourishes and supports photoreceptors. The research identifies five subpopulations with varying levels of disease sensitivity, opening doors to more precise cell and gene therapies for specific degenerative eye diseases.
A new study indicates that artificial intelligence can accurately predict who will develop pancreatic cancer based on pre-diagnostic CT scan images. The AI tool was trained to analyze textural differences in the pancreas and achieved an accuracy rate of 86% in identifying those who would eventually develop the disease.
A multicenter study found that only 42% of patients with a visualization score C on surveillance exams maintain the same score on follow-up exams. The study suggests that alternative surveillance strategies may be necessary for patients at risk for HCC, particularly those with severe steatosis and advanced cirrhosis.
A new technique called Operational Genomic Units (OGU) allows for improved resolution and simplicity in analyzing microbiome samples. By using individual genomes as basic units, researchers can pinpoint biologically relevant characteristics such as age and sex with greater accuracy.
The Logan Science Journalism Program has selected 12 accomplished science and health journalists to participate in its 35th annual fellowship program. The fellows will spend 10 days learning contemporary research techniques at the Marine Biological Laboratory, Woods Hole.
A study identifies new biomarkers that predict the presence of subclinical atherosclerosis, providing a non-invasive way to detect cardiovascular disease. The research team analyzed blood plasma samples and validated three proteins as biomarkers, improving the prediction of cardiovascular risk.
Researchers have created the Thesan simulation, a cubic volume spanning 300 million light years across, to study cosmic reionization and galaxy formation. The simulation aligns with observations and sheds light on key processes, such as how far light can travel in the early universe.
Researchers developed an AI algorithm that can measure coronary plaque buildup in five seconds, predicting heart attack risk within five years. The tool was trained on images from 921 people and matched results with invasive tests considered highly accurate.
A new 3D matrix ultrasound method has been developed to assess cardiovascular risk in healthy individuals, offering a reliable and accurate alternative to traditional methods. The technique shows promise for personalized prevention and treatment strategies by detecting and quantifying an individual's accumulated cardiovascular damage.
The February issue of SLAS Technology features an article on biosensor detection of airborne respiratory viruses, highlighting the effectiveness of this technology in identifying pathogens. Additionally, the issue includes an article on simple assessment of viability in 2D and 3D cell microarrays using single-step digital imaging.
A new AI model combines multiple machine learning methods to accurately detect thyroid cancer and predict treatment outcomes from routine ultrasound images. The multimodal platform achieved high accuracy rates in detecting malignancies and predicting pathological stage and genomic mutations.
Researchers at NTU Singapore have created a rapid and affordable method to evaluate the microstructure of 3D-printed metal alloys, providing insights into strength and toughness. This technology can benefit industries such as aerospace, where quality assessment is critical for maintenance and repair.
Scientists use AI to analyze images of wildlife for crucial data, helping track endangered species and combat extinction. A new field called imageomics extracts biological information from images, providing insights into animal movements, population trends, and evolutionary adaptations.
Scientists have devised a method to pinpoint active ingredients from traditional Chinese medicine formulations, revealing 4 analytes with significant anti-inflammatory activity. This breakthrough could improve quality control standards and lead to better herbal remedies.
Researchers analyzed new kinds of atomic-scale microscopic images using artificial intelligence to understand why rechargeable batteries wear out. They discovered nanofractures caused by mechanical strain on materials, which could lead to the development of more indestructible batteries.
Swine waste lagoon construction dates have been established for the first time using satellite imagery, providing insights into their environmental impact. The study focused on North Carolina and found that approximately 16% of lagoons were already in place before 1987.
Researchers at Universidad Carlos III de Madrid developed a computer vision system to analyze cells in microscopy videos, allowing for automatic characterization of cell behavior. The system enables faster analysis of thousands of cells compared to traditional methods, which typically involve manual segmentation and tracking.
The annual conference will cover important themes such as decision-making in coronary revascularization, specific treatment of complex lesion subsets, and the use of adjunctive tools. CTO Plus 2022 will feature 12 live case transmissions performed by world-class faculty members.
Researchers detect hundreds of major methane releases linked to global oil and gas extraction activities, with a one-year comparison to 20 million vehicles. Limiting these emissions could mitigate climate effects and save billions of dollars for fossil-fuel-producing countries.
Researchers analyzed data from over 35,000 adults who had no history of stroke or dementia. Those with optimal cardiovascular health showed a 2.4% higher brain volume, equivalent to a 7-year younger brain. Good blood pressure control and healthy blood sugar levels also contributed to healthier brain imaging measures.
Researchers found that Russians tend to believe more unusual drawings are creative, while Emirati participants value familiarity. Despite cultural differences, both groups evaluated Russian participants' work as more aesthetically pleasing.
The study reveals that Omicron's spike protein mutations increase its binding affinity to human cells and evade antibodies, contributing to its increased transmissibility. The researchers aim to develop more effective treatments against Omicron and related variants using this knowledge.
The new BD CellView Image Technology enables high-speed sorting of individual cells based on detailed microscopic analysis, accelerating discovery research in immunology, cell biology, and genomics. This technology has the potential to unlock new cell-based therapeutic discoveries and transform various fields of biomedical research.
A new method combines computational ghost imaging and x-ray fluorescence to create high-resolution chemical element maps. This approach eliminates lenses, reducing scanning time and improving spatial resolution, making it useful for biomedicine, materials science, art analysis, and industrial inspection.
A Swansea law expert has been awarded €1.5 million to study the impact of deepfakes on public perceptions of user-generated evidence in human rights trials. The project, TRUE, will track changes in trust over time and develop a systematic account of its role in accountability processes.
Automated brain volumetry in memory-impaired patients shows significant differences and systematic biases between conventional and ultrafast 3D T1-weighted MRI sequences. Most regions demonstrated substantial agreement but also significantly different mean values and consistent biases.
Researchers developed a new hand gesture recognition algorithm that surpasses current methods in accuracy, complexity, and applicability. The algorithm combines adaptive hand type classification and a shortcut feature for efficient real-time recognition.
A recent study published by Queen Mary University of London has found that certain risk factors for heart disease are linked to changes in the structure and appearance of the heart. The research analyzed images from over 30,000 people's heart MRI scans using a new imaging toolkit called radiomics.
Researchers at NIST developed new standards and calibrations for optical microscopes, enabling accurate measurement of microdroplet volumes smaller than 100 trillionths of a liter. They combined microscopy with gravimetry to verify results, linking their findings to fundamental constants of nature.
A new imaging technique can detect early signs of blood trauma in red blood cells, which could aid in the development of markers to prevent damage. The technique, developed by researchers at Shibaura Institute of Technology and Griffith University, uses high-speed cameras to visualize changes in RBC shape under stress.
A team of researchers from the University of Groningen developed an AI-based system that can identify individual Holstein cows in a milking station based on their coat pattern. The system achieved a recognition rate of 99.7% and has several advantages, including non-invasiveness, cost-effectiveness, and scalability.
A novel 'virtual segmentation' method enables accurate visualization of microtomography imaging of Egyptian mummies. This technique helps researchers reconstruct detailed anatomical structures of ancient animals, shedding new light on their biology and evolution.
A new database has been launched to systematically record findings on perovskite semiconductors, featuring over 42,000 individual data sets and analysis tools for interactive exploration. The FAIR principles guide the preparation of the data, enabling easy searching with modern algorithms and artificial intelligence.
A Michigan Tech-developed machine learning model uses probability to classify breast cancer shown in histopathology images and evaluate the uncertainty of its predictions. The model outperforms similar models and can measure uncertainty, promising time savings and referrals to human experts.
Researchers at Duke University developed a holographic system that can image and analyze tens of thousands of cells per minute to spot signs of disease. The technique distinguished between healthy samples and cancerous or pre-cancerous cells with nearly 100% accuracy, using just four basic cellular physical parameters.
A new 'image analysis pipeline' called TDAExplore gives scientists rapid insight into how cells are changed by disease, using a combination of microscopy, topology, and artificial intelligence. This approach can provide objective information on cell changes, such as the movement of proteins like actin, even with limited training data.
A new study published in Frontiers in Psychology has identified specific facial features that can be used to distinguish children's faces from adult faces. These features include the shape of the nose and eyebrows for adult faces and the eye, jawline, and nasal bone for child faces.
Researchers from Okayama University developed an AI-powered image classifier to simplify and speed up the task of image analysis in cell biology. The system achieved high detection accuracy for mitotic cells in plant species, demonstrating its potential for non-experts to use.
Research at RMIT University uses Getty's top lists of editorial pictures to analyze daily investor sentiment, predicting stock market returns based on global mood. The algorithm produces a daily score from 10 popular photos, providing a quick snapshot of investment mood across developed and emerging economies.
Researchers at Keck School of Medicine will collect and analyze imaging data from over 1,300 children nationwide to identify biological markers predicting recovery or persistent symptoms after concussion. The study aims to develop a diagnostic protocol for clinicians caring for pediatric patients with concussion.
The Perseverance rover's first scientific analysis confirms Jezero crater was a calm lake for most of its existence, interrupted by flash floods that carried huge boulders downstream. The findings provide clues to Martian climate evolution and offer opportunities to search for signs of ancient life.
Researchers from Shibaura Institute of Technology synthesized atropisomeric N-aryl quinazoline-4-thiones, showing unprecedented isotopic atropisomerism due to rotational restriction around an N-Ar bond. The findings support the formation of diastereomers and have potential applications in pharmaceuticals.
A novel computational model predicts that articular cartilage can partially heal after injury by controlling inflammation. The study uses biomechanical and inflammatory aspects of osteoarthritis progression to develop a physics-based computational model.
The Ohio State University has received a $15 million NSF grant to create the Imageomics Institute, which will use machine learning methodologies to extract biological traits from images. This new approach, called imageomics, aims to transform biomedical, agricultural and basic biological sciences.
Researchers are reconstructing life on the ISS over two decades to understand space culture and how astronauts interact with their tools and colleagues. The project uses digital photography, crowdsourcing, and archaeological surveys to document developments and changes within the station's lifestyle and cultural makeup.
Researchers at Helmholtz-Zentrum Berlin have achieved a new world record in materials research by using X-ray microscopy to create 1000 three-dimensional images per second. This allows for the non-destructive study of fast processes in materials, enabling researchers to gain insights into material properties and behavior.
The new method uses ink-jet printers and hyperspectral imaging to create hundreds of thousands of material combinations in a single trial run. A cobalt-tantalum-tin compound was discovered that exhibits tunable transparency and acts as a good catalyst for chemical reactions.
The USC Institute is launching a $3 million global consortium study to analyze brain imaging, genetics, and clinical data from 20 countries. The study aims to understand how Parkinson's disease progresses in the brain and explore genetic factors contributing to risk.
Researchers at Nanyang Technological University and Tan Tock Seng Hospital have developed an AI-powered system to diagnose glaucoma from stereo fundus images, achieving an accuracy of 97% in diagnosing the condition. The automated method could potentially be used in less developed areas where patients lack access to ophthalmologists.
A deep learning-based method developed by Kaunas University of Technology researchers can predict the possible onset of Alzheimer's disease from brain images with an accuracy of over 99%. The algorithm was trained on functional MRI images from 138 subjects and performed better than previously developed methods.
A team of researchers found that people rate cloned faces as eerier and more improbable than those with different faces, due to the violation of the one-to-one correspondence between face and identity. The 'clone devaluation effect' was stronger when the number of clone faces increased.
The New Roots for Restoration Biology Integration Institute aims to integrate plant traits, communities, and the soil ecosphere to advance restoration of natural and agricultural ecosystems. The project seeks to understand how root traits influence plant interactions with each other and with the soil.
Scientists have developed a new live analysis system for plant stomata, allowing for rapid and affordable identification of desirable traits. This innovation has the potential to accelerate crop development for climate-resistance, addressing future food shortages.
A recent study published in Biosystems Engineering explores the potential of smartphone cameras to assess soil organic matter and evaluate soil fertility. The technique uses advanced image analysis and machine learning to predict SOM values rapidly and with high correlation to traditional soil analysis.
Researchers developed an image-based detection system using artificial intelligence models to diagnose COVID-19 from chest X-rays. The model accurately differentiated between COVID-19, pneumonia, and healthy patients, showing great promise for precision medicine.
Researchers from Nara Institute of Science and Technology developed a machine learning program that accurately predicts the location of proteins related to actin in cells. The program achieved a high degree of similarity with actual images, showing promise for future applications in cell analysis and artificial cell staining.
A new ultrasonic method improves fetal weight prediction accuracy by analyzing three-dimensional limb volumes and abdominal circumferences. The algorithm outperforms traditional formulas in predicting macrosomia and birth weights.
Researchers have developed a new MRI methodology called Correlation Tensor Imaging (CTI) that can analyze stroke lesions with unprecedented accuracy. This technology may predict individual outcomes and guide treatment, improving patient care.