Articles tagged with Diagnostic Imaging
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A study by the University of Turku found that regular caffeine consumption reduces dopamine transporter binding in Parkinson's disease patients, but has no impact on symptoms or motor function. High caffeine intake may also complicate brain imaging results, suggesting a 24-hour caffeine-free period before diagnosis is recommended.
Researchers explore nanoparticle-based therapies to specifically target lymphatic metastasis in breast cancer, providing a promising solution for patient treatment. Nanoparticles deliver drugs directly to tumors, targeting cancer cells to destroy them or slow their growth, while also enhancing the immune response.
Researchers discovered diamond dust's signal-enhancing properties, outperforming gadolinium. Diamond nanoparticles stay in blood vessels and shine brightly in MRI, without leaking into healthy tissue.
A new consensus statement provides recommendations for improving diagnosis of deep endometriosis through additional pelvic ultrasounds and imaging. The guidelines aim to reduce diagnostic delay and enhance detection of the condition, which affects 10% of women of reproductive age.
A combined PET-MRI scan improved treatment outcomes for 57 out of 205 patients with early-stage breast cancer. The scan helped doctors spot signs of tumour spread, enabling alternative treatments such as chemotherapy or different surgical approaches.
A Tohoku University team studied deep learning models for diagnosing drowning and found discrepancies between model results and medical experts' observations. The study highlights the need for new training methods that align AI model internals with human expertise.
A new blood test has been shown to diagnose Alzheimer's disease pathology as accurately as FDA-approved spinal fluid tests, making early diagnosis and treatment accessible to more people. The blood test measures levels of Alzheimer's proteins in the blood, detecting molecular signs of the disease even before symptoms appear.
A nurse practitioner at The Hospital for Sick Children reduced invasive scans by 58% after using an AI model to analyze ultrasounds. She developed a new type of bias called induced belief revision through repeated exposure to the model.
A new AI system developed by the University of Technology Sydney can rapidly detect COVID-19 from chest X-rays with high accuracy. The Custom Convolutional Neural Network (Custom-CNN) model streamlines the detection process, providing a faster and more accurate diagnosis.
A new framework has been established for standardized imaging of diffuse gliomas using amino acid PET, enabling the evaluation of treatment success and improving therapies. The RANO group has developed criteria that enable reliable imaging of tumor activity and extent.
A study found that clinicians can be fooled by biased AI models even with provided explanations, leading to serious declines in accuracy. While accurate AI models improved diagnostic decisions for some demographics, biased models worsened decisions for others.
Researchers developed a method to measure microvascular changes in the skin using AI and optoacoustic imaging technology, enabling non-invasive assessment of diabetes severity. The study identified 32 significant changes in blood vessels, which can be used to monitor disease progression.
Researchers at Osaka University have developed a novel radioactive antibody that can diagnose and treat a deadly type of pancreatic cancer. The antibody targets glypican-1, a protein highly expressed in PDAC tumors, allowing for early detection and treatment with improved survival rates.
Researchers developed an AI-based method to estimate BMD from plain X-ray images using a hierarchical learning framework. The approach showed high performance and reliability in estimating BMD, with correlation coefficients of 0.88 and 0.92 compared to DXA and QCT.
Researchers at NYU Abu Dhabi have developed acidity-triggered rational membrane peptide-functionalized nanospheres that combine tumor detection and monitoring with potent, light-triggered cancer therapy. These nanospheres enable improved efficacy of phototherapies with minimal systemic toxicity.
Researchers developed an AI model that estimates age from chest X-rays and found a correlation between age discrepancy and chronic diseases like hypertension and COPD. The model was validated using data from multiple institutions, showing strong results.
Researchers developed an AI model that accurately classifies six types of valvular heart disease using chest radiographs, with high accuracy rates. The model's potential applications include supplementing echocardiography in areas where specialists are scarce and improving emergency care.
Researchers at the University of Sydney have developed a photonic radar system that can accurately detect pauses in breathing patterns remotely. The system has been tested on cane toads and simulated human breathing devices, demonstrating its potential for non-invasive vital sign monitoring in clinical environments.
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.
Researchers developed a machine learning model that predicts strokes more accurately than current systems, using data available at hospital arrival. The model achieved high precision and sensitivity, outperforming existing scales.
Jeong Min Lee, KSR President and professor at Seoul National University Hospital, is honored with ARRS membership. He has published over 470 scientific articles and serves on the editorial board of several publications.
Researchers at KAIST have successfully developed a new X-ray microscope technology that can overcome the resolution limitations of existing microscopes. This breakthrough enables high-resolution imaging of nanoscale structures, with a resolution of 14 nm, which is comparable to that of electron microscopes. The technology uses random d...
Researchers have developed a new imaging approach to diagnose advanced non-alcoholic fatty liver disease (NASH). The enzyme-sensitive nanoprobe emits signals that can be detected by MRI techniques, providing more accurate and sensitive data for diagnosis. This breakthrough aims to improve the treatment outcomes of NASH patients.
Ringlike peripheral high iodine concentration maps from dual-energy CT can help guide management in patients with known lung cancer and an indeterminate solitary nodule. This finding showed excellent interobserver agreement, high specificity, and independently predicted pulmonary metastasis.
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.
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 have developed a new X-ray technology that visualizes lung tissue microstructure, providing additional information for accurate diagnosis. Dark-field X-ray images can differentiate between diseased and healthy lung tissue, potentially replacing computed tomography (CT) for repeated examinations.
A new machine learning fusion model has been developed to diagnose ovarian cancer more accurately by combining ultrasound and photoacoustic tomography imaging. The model achieved an accuracy of 90% in detecting ovarian lesions, outperforming previous methods.
Researchers at USC's Keck School of Medicine are collaborating with Polaris Dawn to develop a novel method for collecting X-rays in outer space. They aim to harness ambient radiation to create images using analog X-ray film, which could improve medical care on long-duration spaceflights.
Researchers from Xi'an Jiaotong-Liverpool University found that brain stimulation combined with a nose spray containing nanoparticles can improve recovery after ischemic stroke. The treatment increased cognitive and motor functions, and weighed more quickly than those treated with TMS alone.
Reduced kidney function and increased urine albumin excretion are associated with brain volume reduction in regions typically affected by Alzheimer's disease and other dementia etiologies. Cognitive decline is common in patients with kidney disease, and this study provides new insights into the etiology and localization of brain damage.
New research by UMass Amherst professor Jinglei Ping demonstrates the use of graphene for electrokinetic biosample processing and analysis, allowing for faster and more efficient detection of biomolecules. This breakthrough enables the creation of smaller lab-on-a-chip devices with improved time and size efficiencies.
The global contrast media shortage has affected millions of diagnostic imaging examinations and requires immediate attention. Researchers propose several methods to conserve contrast media, including weight-based dosing and adjusting CT settings.
Researchers developed an artificial intelligence method to generate high-quality 'PET/CT' images, reducing radiation exposure and allowing for more frequent imaging. This innovation has the potential to provide more accurate assessment of disease progression and treatment efficacy, ultimately improving patient care.
The new technique, 3D optical coherence refraction tomography (3D OCRT), produces highly detailed images revealing features difficult to observe with traditional OCT. It has the potential for biomedical research and eventually more accurate medical diagnostic imaging.
A study of over 12 million emergency department visits reveals significant racial and ethnic disparities in imaging rates among children. Non-Hispanic white patients consistently received more diagnostic imaging than their black counterparts, highlighting systemic inequities in healthcare.
A new review analyzes the efficacy of current non-invasive methods for assessing non-alcoholic fatty liver disease (NAFLD) and associated conditions. Blood-based biomarker tests and imaging methods are explored, with some showing promise in early diagnosis and staging liver disorders.
Researchers at Penn Medicine have developed an imaging agent that detects cancer cells in real-time during biopsies, offering promise for earlier and more accurate diagnoses. The technology, known as NIR-nCLE, uses a combination of near-infrared tracers and confocal laser endomicroscopy to identify microscopic cancer cells.
The American Journal of Roentgenology has published best practices for managing an iodinated contrast media (ICM) shortage in radiology practices. To mitigate the shortage, practice leaders should prioritize examination findings and reduce overall ICM usage.
Researchers at the University of Tsukuba developed a portable MRI system to identify wrist cartilage damage among athletes, providing a convenient means of early detection and treatment. The system enables quick screening at remote locations, eliminating the need for hospital visits.
Researchers analyzed prenatal MRI scans of children later diagnosed with autism and found significant differences in brain structures at 25 weeks' gestation. These differences may be a strong biomarker for predicting ASD emergence, with the insular lobe showing increased volume.
A study published in JAMA Network Open found that half of all women will experience at least one false positive mammogram over a decade of annual breast cancer screening with 3D mammography. The risk of false positives is lower for women screened every other year, and non-dense breasts also show a lower false positive risk.
A multicenter study found relatively high interobserver agreement for chronic pancreatitis findings in children using both CT and MRI. However, subjective findings like atrophy showed moderate agreement, emphasizing the need for standardized criteria.
Researchers at the University of New Hampshire have mapped magnetic fields in three dimensions, enabling improved diagnostic imaging and enhanced storage capacity for devices. The breakthrough has implications for medical imaging technologies like CT scans and magnetic memory devices.
A novel technology has been developed for fast and reliable detection of SARS-CoV-2 in saliva samples using a flow virometer that utilizes fluorescent light markers. The device achieved high sensitivity and specificity in a blind test on over 50 patients, outperforming commercial antigen tests.
A study of 74 healthy individuals found correlations between spleen volume and gender, age, and certain blood parameters such as HGB, HCT, RBC, ALT, but not others like LYM%, PLT. Spleen volume was estimated using the Cavalieri principle and stereological method.
Duke researchers developed a method to increase the depth of view of optical coherence tomography (OCT), allowing for clear images from beyond a millimeter beneath the skin's surface. The new technique, known as dual-axis OCT, tilts the light source and detector to collect more scattered light from deep tissues.
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.
Researchers at the University of Houston have developed a new, label-free biosensing platform for detecting cancer biomarkers in exosomes. The technology uses gold nanodisks to capture and analyze surface proteins on exosomes with high sensitivity.
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.
New estimates suggest nearly half (47%) of the global population has limited or no access to key tests and services for diagnosing common diseases. The Lancet Commission on Diagnostics calls for urgent investment and training to improve access to testing in primary care, especially point-of-care testing.
Researchers developed a method to apply deep learning to polarization-sensitive optical coherence tomography, enhancing cancer diagnosis. The technique enables OCT systems to detect abnormalities on a deeper level, differentiating microstructural features such as collagen fiber orientations.
A new paradigm in breast cancer screening has been established with immediate-read screenings during the pandemic. This approach reduces racial and ethnic disparities in same-day diagnostic imaging, while speeding up diagnoses.
Researchers have developed a flexible and wearable X-ray detector using metal-organic frameworks (MOFs) that don't contain harmful heavy metals. The device shows high-sensitivity sensing and imaging capabilities, making it suitable for various radiation monitoring and medical imaging applications.
A new study analyzes 377 severe harm and death incidents in acute medical units across England and Wales between 2005-2015. The most common incident types were diagnostic errors, medication-related errors, and failures in monitoring patients. Implementing electronic prescribing systems and checklists can reduce diagnostic errors, while...
Researchers discovered racial and ethnic differences in diagnostic imaging for children at pediatric emergency departments in the United States. The study found that these disparities are not solely due to patient factors, but also reflect biases in healthcare providers' decision-making.
A new detector using cesium lead bromide perovskite crystals has been developed to aid in the detection and identification of radioactive isotopes. The device achieves high resolution in energy detection, allowing for differentiation between legal and illegal gamma rays.
Research finds that women with food and housing insecurity are at risk of undiagnosed breast cancer due to lapses in follow-up appointments. The study, presented at the Radiological Society of North America meeting, found that these unmet social needs lead to longer intervals between diagnostic imaging and biopsy.
A study found that diagnostic imaging studies with positive conclusions were associated with a shorter time from study completion to publication, contributing to an overrepresentation of positive results in the test accuracy literature. Studies with positive titles showed no significant association with faster publication times.
Nonphysician providers, including nurse practitioners and physician assistants, rarely render diagnostic imaging services, with most being radiography and fluoroscopy. Despite growing involvement in imaging-guided procedures, NPPs still represent a small fraction of all diagnostic imaging interpretations.