Articles tagged with Image Analysis
A research team developed an AI-powered method to automatically detect single cells in microscopic images, achieving a mean Average Precision (mAP) of 0.989. This technology reduces human labor and improves single-cell analysis, enabling the discovery of new treatment methods for diseases.
Scientists at Göttingen University have created a novel approach to produce X-ray images in color from a single exposure, eliminating the need for focusing and scanning. This breakthrough method uses an X-ray color camera and a specially structured plate to capture the intensity pattern of fluorescing atoms in a sample.
Researchers developed a multimodal imaging-based tool, the active biopsy guidance system (ABGS), to improve oral cancer diagnosis. The ABGS provides clinically actionable direct visible guidance for biopsies, enhancing precision and potentially saving lives.
Researchers use coherent correlation imaging to image the evolution of magnetic domains in time and space without prior knowledge. The study reveals thermal motion and pinning effects on domain boundaries, unlocking new insights into magnetism's microcosm.
Researchers have developed shortwave-infrared and thermal imaging techniques to accurately diagnose active dental caries. SWIR-based approach shows superior results in detecting lesions, while thermal imaging proves less effective.
Scientists have developed AI techniques to track crevasses on the Thwaites Glacier, which could impact global sea levels by up to 60cm. The study found a complex interplay between crevasse formation and ice flow speed changes.
A team of astronomers discovered 87 galaxies that could be the earliest known galaxies in the universe using data from NASA's James Webb Space Telescope. This finding suggests a revision to our understanding of galaxy formation, indicating that more galaxies may have formed earlier than previously thought.
Researchers found that white matter hyperintensities are independently associated with premature brain aging, leading to progressive age-like brain atrophy. The study used machine-learning algorithms to estimate brain age and analyzed the relationship between WMH load and BrainGAP scores.
Jon A. Jacobson, MD, FACR, has been recognized as the 2023 ARRS Distinguished Educator for his exceptional contributions to radiological education and innovative educational activities. He is a renowned expert in musculoskeletal ultrasound and MRI, with over 250 peer-reviewed publications.
Researchers at RIT have made a groundbreaking discovery confirming the light emitted by stars outside our galaxy is two to three times brighter than previously thought. This finding suggests a possible absence of optical light sources in the universe, potentially changing our understanding of how it formed over time.
Researchers used dental microwear texture analysis to infer feeding habits of large theropods, including T. rex and Allosaurus. The study found that these dinosaurs did not eat bones as frequently as thought, with juveniles having a different feeding behavior.
Scientists at Tokyo University of Science developed an 'extended Landau free energy model' to analyze complex interactions in nanomagnetic devices, enabling causal analysis and visualization. The model proposed optimal structures for nano-devices with low power consumption.
Researchers used microCT to create a 3D atlas of lung lesions in TB and COVID-19, revealing complex shapes and hidden pathological structures. The study provides new insights into the microscopic anatomy of these deadly diseases.
Researchers from the University of Johannesburg deployed Few Shot Learning (FSL) for NIALM, a non-intrusive appliance load monitoring system. FSL requires only 7 test images to recognize appliances with 97.83% accuracy, making it faster and more cost-effective than traditional Machine Learning.
Deep learning empowers cell image analysis by automating tasks such as segmentation and tracking, improving research efficiency. However, there are challenges in data quantity, quality, and confidence that remain to be addressed.
Researchers developed an AI tool that analyzes plaque features on coronary CT scans to predict reduced blood flow, potentially reducing invasive tests. The tool could help doctors determine the next step in treatment plans and risk-stratify patients correctly.
Researchers at CABBI used unmanned aerial vehicles with machine learning methods to select the best candidate genotypes in miscanthus breeding programs. The new method leverages high-resolution aerial imagery and three-dimensional neural networks to estimate crop traits such as flowering time, height, and biomass production.
Researchers from the University of Kassel developed an approach to extend the limits of interferometric topography measurements for optical resolution below small structures. Microsphere assistance enables fast and label-free imaging without requiring extensive sample preparation.
A Tel Aviv University study reveals that the Israeli shoreline is contaminated with more than two million tons of microplastics, with Tel Aviv and Hadera beaches being the most polluted. The researchers warn that exposure to microplastic waste is unavoidable and poses a risk to human health.
Researchers have discovered that reef halos, visible from satellite imagery, can provide insights into coral reef health globally. These bands of bare seafloor surrounding coral patch reefs are more common and dynamic than expected, with the potential to serve as a window into reef health.
A new study from the University of Kansas reveals that media coverage of Hurricane Harvey in 2017 perpetuated racial biases in image presentation, showing people of color as displaced migrants, women as damsels in distress, and white men as saviors. The study highlights how these representations reinforce cultural narratives and stereo...
A new study published in Nature on saliency heat maps in medical imaging reveals that these tools consistently underperform human radiologists in image assessment and identifying pathological lesions. The researchers caution that saliency-based heat maps should be further refined before being widely adopted in clinical AI models.
New research highlights the dangers of AI-powered recruitment tools that claim to remove discrimination from hiring. The tools reduce race and gender to trivial data points and often rely on personality analysis that is
A new study has identified 195 previously unknown nectar plants for the painted lady butterfly, providing a source of food in Southern California. The research suggests that droughts may impact the butterflies' ability to reproduce, but the discovery of these nectar plants offers hope for their survival.
A Brazilian study reveals that SARS-CoV-2 targets astrocytes in the brain, reducing neuron viability and causing damage. Infection was confirmed using MRI scans of mild COVID-19 patients and experiments on human nerve cells.
Researchers developed a novel three-core optical fiber sensor to accurately measure both the magnitude and direction of spine curvature. The sensor offers advantages like low cost, high sensitivity, and small size, making it a promising tool for doctors to diagnose problems in spine curvature.
A team of scientists from China published a perspective paper on the use of AI in skin diseases, highlighting its potential to assist clinicians. They propose several recommendations to improve AI-assisted diagnosis systems, including establishing a robust database and adapting algorithms to existing real-world databases.
A large retrospective study found that visceral fat area from fully automated and normalized abdominal CT analysis predicts subsequent myocardial infarction or stroke in Black and White patients. The study suggests that body composition analysis using machine learning could be widely adopted to add prognostic utility to clinical practice.
A team of Brazilian researchers has developed a novel technique using light and artificial intelligence to identify the maturity stages of soybean seeds. The method utilizes chlorophyll fluorescence and machine learning algorithms to classify commercial seeds with high accuracy. This innovation avoids destroying seeds, which are then c...
A homemade microspectrometer invented by Dr. Jamie Laird enables scientists to image defects in perovskite solar cells, improving stability and efficiency. This innovative technique has the potential to revolutionize next-generation photovoltaics, including space missions.
Researchers developed a label-free Raman spectroscopy approach with enhanced sensitivity and speed, allowing for non-invasive imaging of biological samples. The new CARS microscopy system can acquire microscopic images and identify biomolecules with unprecedented resolution and speed.
Researchers used machine learning algorithms and k-D tree data structure to identify 11 previously undetected space anomalies, seven of which are supernova candidates. The team analyzed digital images of the Northern sky taken in 2018 using a k-D tree to detect anomalies through the 'nearest neighbour' method.
A new photoswitching fingerprint analysis overcomes the sub-10 nm resolution barrier in super-resolution microscopy, enabling the imaging of dynamic interactions with other molecules in cells. This breakthrough reveals molecular functions and architectures at the nanoscale, shedding light on cellular processes such as learning and memory.
A new software program, Mistic, allows for the simultaneous viewing of tissue images through dimensionality reduction methods. The software enables the extraction of data from multiplexed images, facilitating the understanding of cancer biology and the development of new therapies.
Researchers developed a mathematical model to predict the efficiency of nanoparticle delivery into cells, particularly in stem cells. They found that nanoparticles become trapped in bubble-like vesicles, preventing them from reaching their targets.
Researchers at Hebrew University have developed a new method using quantitative MRI to diagnose early-stage Parkinson's disease. This technique reveals biological changes in the brain, enabling early diagnosis and monitoring of treatment efficacy.
A new AI technology called in silico FOCUS analyzes cell images to predict therapeutic effect of drugs for neurodegenerative disorders like Kennedy disease. The technology has 100% accuracy and can analyze several hundred thousand cells in just a few minutes.
The American Journal of Roentgenology has dramatically increased its impact factor from 3.959 to 6.582 in 2021, according to Journal Citation Reports. The journal's new ranking places it in the 83rd percentile in the Radiology category, reflecting the high quality of work by authors and reviewers.
Researchers developed a neural network algorithm that recognizes emotions and engagement from video images of faces, outperforming existing models in accuracy. The system can be integrated into video conferencing tools and online learning systems to analyze participant engagement and emotions.
A new open-path mid-infrared spectrometer can precisely measure isotopologue ratios in atmospheric water vapor in under 15 minutes, offering improved accuracy for climate change modeling and air quality monitoring. The instrument's dual-comb technique enables spatially resolved studies of water vapor transport over natural ecosystems.
The study found that the use of comics for humour, visual-verbal interplay, informality, simple narrative plots, and exaggerated personalities contributed to the campaign's enduring success. The researchers argue that humour is a powerful discursive strategy to educate the public, making content relatable while addressing taboo topics.
A scoping review of knowledge graph applications in medical imaging analysis identifies increasing trend and potential future directions. The study highlights the effectiveness of prior knowledge in medical imaging tasks, but also reveals limitations, including limited annotated data and weak generalizability.
A new analysis method reveals that the original SARS-CoV-2 viral strain used a sugar binding feature to infect human cells, which was later lost in variants. This discovery raises questions about the origins of the virus and its transmission to humans.
Computer scientists have developed a system to analyse noise produced by individual cameras, linking videos or images to specific cameras. The tool extracts camera-specific noise from video frames, achieving high accuracy rates and potential practical use for law enforcement.
Researchers use machine learning to automatically analyze Reflection High-Energy Electron Diffraction (RHEED) data, enabling faster and more efficient discovery of new materials. The study focused on surface superstructures in thin-film silicon surfaces and identified optimal synthesis conditions using non-negative matrix factorization.
Researchers developed an AI method to automatically detect plaque erosion in heart arteries using OCT images. The new technique uses neural networks and post-processing algorithms to predict regions of possible plaque erosion and refine the initial prediction based on clinically interpretable features.
A team from KAUST has developed a low-cost system for imaging plant growth dynamics noninvasively and at high throughput. The Mutiple XL ab system combines computer vision and pattern recognition technologies with machine learning to analyze and quantify root growth dynamics.
Researchers have used high-speed 4D neutron computed tomography to visualize the three-dimensional water distribution within fuel cells. This allows for optimized channel design and increased efficiency, as excess water can be drained without compromising membrane integrity.
Researchers used AI to compare brain images of individuals with Autism Spectrum Disorder to computer-generated simulations, revealing links between brain anatomy and symptoms. The discovery could help understand ASD causes and develop targeted interventions.
Researchers studied meiotic cohesin complexes' effect on chromosome structure and genomic integrity in embryonic stem cells. Maintaining adequate levels of REC8 and STAG3 factors ensures chromosomal stabilization and sister chromatid cohesion.
The review highlights advances in fundamental visualization methods for medical images in 3D, including scalar, vector, and tensor data. Medical professionals can quickly locate proper techniques using a taxonomy of medical problems and examples of health applications.
A new computer-aided diagnostic tool, Deep-Lung Parenchyma-Enhancing (DLPE), uses artificial intelligence to reveal signs of pulmonary fibrosis in COVID long-haulers, helping to explain respiratory symptoms and improve disease management.
Researchers developed a system to estimate the probability of a majority response being wrong, eliminating unnecessary task assignments and increasing efficiency in crowdsourcing projects. This method can be applied to various binary classification tasks, reducing labor and improving accuracy.
The QUSTom project aims to develop a new medical imaging modality using ultrasound and supercomputing, improving breast cancer diagnosis and potentially replacing mammograms. The technology will offer superior image quality and better tumor monitoring, while being completely safe for patients.
The Cardiovascular Research Foundation (CRF) announced that TVT: The Structural Heart Summit will feature 12 studies as Late-Breaking Clinical Science and Featured Clinical Research. These studies highlight advances in transcatheter aortic valve replacement, mitral, tricuspid, and left atrial appendage occlusion therapies.
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.