Articles tagged with Medical Imaging
A 10-year study found that short-term exposure to heat and air pollution was associated with an increase in medical imaging exams, particularly for chest, neuro, and musculoskeletal imaging. High temperatures had a greater impact on X-ray utilization, while high particulate matter levels were linked to increased CT scans.
Researchers developed BiomedParse, an AI medical image analysis model that works across nine types of medical images to predict systemic diseases. This enables non-specialists to analyze and identify subtle variations in medical images, leading to improved diagnosis.
Researchers have developed a highly sensitive and foldable detector that produces good quality images with smaller dosages of X-rays. The new technology reduces detection limits and paves the way for safer medical imaging and industrial monitoring.
Researchers at Aalto University have designed realistic photonic time crystals that exponentially amplify light, paving the way for faster and more compact optical devices. The discovery has potential applications in nanosensing, imaging, and communication.
A study by Niigata University found that AI analysis of PET/CT images can predict the occurrence of interstitial lung disease, a serious side effect of immunotherapy. Patients with high inflammation in non-cancerous lungs are at a higher risk of developing this condition.
Scientists have designed bioluminescent proteins that can produce multiple colors of light for real-time imaging in cellular and animal models. These proteins are small, efficient, highly stable and can be used for non-invasive bioimaging, diagnostics, drug discovery and more.
TU Graz researcher Gerhard A. Holzapfel leads a six-year project to develop AI-based methods for analyzing soft tissue mechanical properties using transcriptomics and microstructure imaging. The team aims to improve disease diagnosis and therapy in clinical practice.
Aston University researcher has developed a new technique harnessing Orbital Angular Momentum (OAM) light to improve imaging and data transmission through skin and biological tissues. The OAM-based approach shows unmatched sensitivity and accuracy, paving the way for non-invasive medical diagnostics and imaging.
The new fringe photometric stereo technique reduces scanning time by over two-thirds while achieving micrometer-level accuracy, ideal for real-time scanning applications such as industrial inspection and medical procedures. The approach 'feels' the surface by projecting light patterns, improving precision measurements.
Researchers developed an AI model that uses imaging data to detect less frequent gastrointestinal tract diseases. The model's anomaly detection approach allows it to reliably identify rare diseases without extensive training data.
College of Medicine – Tucson researchers are using a $3.3 million grant to test advanced breast computed tomography (CT) scans that create high-resolution 3-D images without physical compression, aiming to enhance early cancer detection and diagnosis for women with dense breasts. The technology has the potential to detect more breast c...
Researchers used fluorescein angiography to visualize neural blood flow in rats and rabbits with chronic nerve compression neuropathy, correlating findings with electrodiagnostic testing. The study showed promising results for the method's potential to improve diagnosis and treatment outcomes in carpal tunnel surgery.
A study by Osaka Metropolitan University found that ChatGPT's diagnostic performance for brain tumors was comparable to that of neuroradiologists, with an accuracy rate of 73%. The model's performance varied depending on the type of clinical report written, with higher accuracy when using reports from neuroradiologist writers.
The new framework, called SLIViT, has been developed by UCLA researchers and achieved accurate disease risk biomarkers detection from medical scans. It consistently achieves better performance compared to domain-specific state-of-the-art models.
Researchers at TU Graz have developed a new machine learning method that generates precise live MRI images of the beating heart using only a few MRI measurement data. This breakthrough enables faster and cheaper MRI applications, including quantitative MRI for diagnoses.
Researchers found that gold nanoparticles can accurately assess kidney injuries using X-rays, correlating with nanoparticle accumulation. However, caution is needed when employing nanomedicines to patients with compromised kidneys.
The integration of artificial intelligence in intratumoral immunotherapy can refine diagnoses, guide interventions, predict treatment responses, and adapt therapeutic strategies. This enables the enhancement of patient outcomes, including improved survival rates and quality of life.
Researchers have developed a new technique to study anisotropic materials, capturing full complexity of light behavior in these materials. The method revealed detailed insights into how light scatters differently along various directions within materials, allowing retrieval of scattering tensor coefficients.
Researchers have introduced DSFN to improve the speed and accuracy of diagnoses of retinal disorders. This AI-powered medical imaging technique combines retina images with vascular distribution information to accurately locate the fovea in complex clinical scenarios, enabling doctors to detect early signs of ocular diseases.
Researchers at Stanford University developed a method to apply an FDA-approved dye to make mouse skin transparent, allowing for non-invasive visualization of internal organs. This breakthrough enables new approaches to biological and diagnostic testing, with potential applications in cancer treatment, blood draws, and cosmetic procedures.
Aston University researcher has developed a new method of analysing crystals in dehydrated blood samples using polarisation-based image reconstruction technique. The technique showed a 90% accuracy rate for early diagnosis and classification of cancer, making it less traumatic and risky for patients.
A large cohort study found that false-positive mammography results lead to a significant decrease in subsequent screening rates among women. The study, published in Annals of Internal Medicine, highlights the potential unintended consequence of false positives on routine breast cancer screening participation.
Researchers explore AI's role in addressing challenges in immunotherapy, developing new biomarkers for precise disease characterization and predicting treatment response. A comprehensive review applied AI/radiomics to cross-sectional imaging, showcasing the current landscape in IO treatment.
Researchers at Boston University have developed low-cost, high-impact metamaterials solutions to improve MRI technology, enabling clearer imaging in low-resource areas. Wearable and deployable devices can be tailored to specific body parts, boosting signal-to-noise ratio and reducing scanning time.
Researchers developed Stain SAN, a novel domain adaptation technique to correct color differences in stained histopathology images. The method improves consistency and comparability of data, leading to better performance in machine-learning-based classifiers.
Researchers at Osaka Metropolitan University found that AI model GPT-4 outperformed a weaker version of the model and was on par with radiology residents in diagnosing musculoskeletal conditions. However, it struggled to match the accuracy of board-certified radiologists.
A Rice University-led team is developing an affordable system to improve tumor removal accuracy for breast and head and neck cancer. The AccessPath system enables rapid, automatic tumor margin classification, revolutionizing real-time surgical guidance.
A research team developed an unsupervised domain adaptation approach called DDSP, achieving high-precision cross-modality segmentation without target modality labels. The framework diverges from GANs and incorporates inter-channel similarity feature alignment to boost flexibility and precision.
Researchers at WVU created a motion-compatible brain scanner that allows patients to move around during imaging. The Ambulatory Motion-enabling PET (AMPET) scanner can help study human behaviors, balance, and emotions, and may be used to monitor brain activity for PTSD treatment and mindfulness meditation.
A new microscopy method, Tempo STEM, significantly reduces radiation required by 'shutting off' the beam at peak efficiency. This approach eliminates excess damaging irradiation and avoids sample transformation or destruction.
A new method to measure continuous light spectrum improves thermal imaging accuracy without direct contact. It eliminates wavelength and temperature dependence, revealing higher surface temperatures of photothermal catalysts than previous methods.
The Special Report explores expert perspectives on deploying AI in radiology, emphasizing the need for trust, reproducibility, explainability, and accountability. Radiologists must be able to trust in AI systems' design and receive adequate training, while establishing clear guidelines regarding clinical accountability.
Researchers have developed a novel method to significantly enhance quantum technology performance by leveraging cross-correlation of two noise sources. This approach extends coherence time, improves control fidelity, and increases sensitivity for high-frequency sensing.
Children and young people are generally positive about AI's role in healthcare, with many believing it can improve their care and outcomes. However, they also emphasize the importance of human oversight, particularly when it comes to empathy and ethical decision-making.
Researchers have developed a new radiotracer, FDT, which enables accurate detection of live TB bacteria in the body. This approach is more specific than existing methods and could lead to improved diagnosis and treatment outcomes for TB patients.
Researchers at NTNU have developed a way to reconstruct 3D models of the colon using single images taken by capsule endoscopy cameras. These models can aid in detecting abnormalities and signs of disease, enabling specialists to make diagnoses faster.
Researchers developed metamaterials-enhanced MRI technology using coaxial cables to boost signal-to-noise ratio. The innovative coils address patient discomfort and cost by providing adaptable, form-fitting designs for various anatomical sites.
Researchers are working to make publicly funded data collection in computed tomography widely available and understandable by diverse communities. The Non-Clinical Tomography Users Research Network aims to improve data handling across scientific disciplines and standardize data acquisition, handling, and sharing.
A novel technique using radiomics reveals imperceptible patterns in medical images to predict the risk of meniscus tears. The study correctly classified cases with a sensitivity of 70.6% and specificity of 95.0%, providing clinicians with a valuable tool for proactive knee health management.
Researchers at Pohang University of Science & Technology have developed the first wide field-of-hearing metalens, overcoming traditional acoustic lens limitations. The device achieves up to 140 degrees of field-of-hearing without sound distortion, enabling new applications in acoustic imaging and high-sensitivity sensing.
A research team at Mass General Brigham developed a foundation model that identified patterns predicting anatomical site, malignancy, and prognosis from radiological images. The approach remained powerful even with limited data, outperforming existing methods in specialized tasks.
Scientists at Harvard John A. Paulson School of Engineering and Applied Sciences have developed a compact, single-shot polarization imaging system that can provide a complete picture of polarization. The system uses two thin metasurfaces to capture the most complete polarization response of an object in real-time.
A study published in the Canadian Association of Radiologists Journal shows an increase in breast cancer diagnoses among females in their Twenties, Thirties, and Forties. Younger women are more likely to be diagnosed at later stages and with more aggressive cancer.
DZNE researchers aim to measure intricate brain structures with artificial intelligence, using 3D brain scans and data from the Human Connectome Project. The goal is to better understand disease processes and normal brain changes over time.
Two new Review articles explore AI's application in early cancer detection, highlighting its potential to enhance diagnostic accuracy and improve treatment selection. The articles emphasize the need for a robust, multi-disciplinary approach to integrate AI into medicine.
A new study suggests that tailored treatment can predict the response of breast cancer patients to chemotherapy, allowing for safe omission of extensive lymph node removal. The MARI protocol showed a 95% overall survival rate and 89% disease-free survival rate in patients who achieved pathological complete response.
A recent study reveals significant differences in intimate partner violence injury patterns across age groups, with adolescents experiencing a higher incidence of sexual assault and unique fracture patterns. Healthcare providers can use these findings to detect IPV in previously overlooked age groups, potentially preventing the cycle o...
A novel transparent ultrasonic transducer (TUT) developed by POSTECH researchers offers exceptional optical transparency and maintains acoustic performance, surpassing conventional limitations. This breakthrough enables high-depth-to-resolution ratios for ultrasound imaging, with applications in various medical devices and fields.
New research reveals that radioguided surgery can effectively detect and remove metastatic pelvic lymph nodes in newly diagnosed prostate cancer patients. The procedure targets the prostate-specific membrane antigen, improving nodal staging to guide treatment recommendations.
Researchers warn that AI tools used in radiology contribute to greenhouse gas emissions through energy consumption and data storage. To minimize these impacts, experts recommend sharing resources, selecting sustainable hardware, and using data compression techniques.
Researchers have confirmed a new method for precise body composition analysis using 3D surface scans and dual-energy X-ray absorptiometry. This approach yields accurate measurements of fat, lean muscle, and bone, surpassing commercial software in clinical settings.
USC researchers have designed nanoparticles that can target and highlight cancer cells in lymph nodes, allowing for earlier detection of metastasis. The particles work by hitchhiking on immune cells to reach the lymph nodes, where they can amplify the signal detected by MRI scans.
Researchers found that bullied adolescents have lower levels of glutamate in the brain's anterior cingulate cortex region, which regulates emotions and decision-making. This imbalance is linked to higher levels of subclinical psychotic experiences in early adolescence.
The American Dental Association has released updated recommendations to enhance radiography safety in dentistry, emphasizing the appropriate and justified use of dental X-rays. The guidelines also advise against using lead abdominal aprons or thyroid collars, citing evidence that modern digital X-ray equipment and beam restrictions can...
The American College of Radiology has issued a joint statement with four other radiology societies to address the development and use of AI tools in radiology. The statement emphasizes the need for increased monitoring of AI utility and safety, advocating for collaboration among developers, clinicians, purchasers, and regulators.
Researchers developed an AI model using CT images to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors. The model achieved high accuracy, even for smaller tumors, and can help guide surgical decisions.
Researchers have developed a new technique that provides a previously unattainable view of the mechanical properties inside the cell nucleus. The study reveals the peculiar dynamic structural features in living cells, which appear to be crucial for cell function.
A study found that the No Surprises Act's independent dispute resolution (IDR) process would be financially unfeasible for a large portion of out-of-network claims for hospital-based specialties, particularly radiologists. This could undermine patient access to in-network care due to limited bargaining power.
A new technology uses meta-optical devices to perform thermal imaging, providing richer information about imaged objects. The approach can be used for various applications such as autonomous navigation, material identification, security, and medical imaging.
The review discusses the optical aspects of QPAT, including mathematical models for light propagation and interaction with biological tissues. The authors outline two approaches to estimating chromophore concentrations from absorbed optical energy density data, highlighting the challenges associated with practical implementation, such ...