Articles tagged with Diagnostic Imaging
Researchers developed a deep learning algorithm to denoise ultra-low dose CT scans, improving image quality and accuracy. The study found that this approach can diagnose pneumonia in immunocompromised patients using only 2% of the radiation dose of standard CT scans.
A rare case of adenomyosis in an 81-year-old postmenopausal woman closely resembles invasive endometrial cancer, emphasizing the challenges of diagnosing this condition. Further studies are needed to refine diagnostic protocols and determine risk factors for postmenopausal patients.
A study found racial and ethnic minorities are less likely to receive standard-of-care diagnostic imaging after abnormal screening mammograms compared to white patients. Minority groups were also less likely to have access to same-day biopsy services, despite similar availability of most diagnostic services.
A study found significant racial disparities in who received same-day diagnostic services and biopsies after abnormal mammogram findings, with Black women being less likely to receive same-day biopsies. Rural-resident patients were more likely to receive same-day diagnostic services than urban residents.
Researchers from Japan develop a non-contact, millimeter-wave sensor system to monitor respiratory motion during diagnostic X-ray and CT imaging. The system has been validated through extensive testing with healthy volunteers and shows promise for improving diagnostic accuracy and treatment outcomes.
A recent neuroscientific study by UPF and Oxford reveals that long-range connections between brain regions are scarce but play a fundamental role in explaining brain dynamics. These rare connections serve to transmit information more quickly and directly, yielding optimal and efficient information processing.
A new experimental technique has developed a molecular flashlight to monitor molecular changes in the brain caused by cancer and other neurological pathologies. The technique uses vibrational spectroscopy to illuminate nerve tissue, allowing for the analysis of molecular changes caused by tumours or injuries.
A 10-minute brain scan can predict the effectiveness of a risky spinal surgery to alleviate intractable pain. The study found that patients who responded better to spinal cord stimulation therapy had weaker connections between certain brain networks.
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.
Researchers developed a new PET scan that accurately visualizes benign tumors in the pancreas, known as insulinomas, which can cause low blood sugar levels. The Exendin-PET scan uses a substance found in lizard saliva to detect these tumors, improving diagnosis and treatment outcomes.
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.
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.
A new AI model generates detailed images of cancer tissue that imitate what its staining would look like, reducing the need for resource-intensive lab analyses. The VirtualMultiplexer uses contrastive unpaired translation to create accurate virtual pictures of diagnostic tissue colorations.
F18-FDG PET-CT scans showed significantly higher levels of radioactively-labeled glucose in kidneys for patients with ICI-AKI compared to those with AKI from non-ICI causes. This suggests a potential non-invasive testing option for differentiating cause of AKI as being ICI-related vs. non-ICI-related.
Using tartrazine, researchers create transparency in tissues by matching refractive indices, revealing hidden organs and structures. This technique has implications for diagnosing injuries, digestive disorders, and cancers.
A multi-university research team led by University of Virginia engineering professor Gustavo K. Rohde has developed a system that can accurately spot genetic markers of autism in brain images. The system uses generative computer modeling technique called transport-based morphometry, which reveals brain structure patterns that predict v...
A new AI-based digital platform has been developed to analyze tissue sections from lung cancer patients, making diagnosis faster and more accurate. The platform uses algorithms that enable fully automated analysis of digitized tissue samples, allowing for personalized therapy based on molecularly specific genetic changes.
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.
Scientists create fluorescent sensors that can detect neurotransmitter levels in the brain, allowing for non-invasive live brain imaging. The technology has shown promise for advancing Alzheimer's disease diagnosis and treatment.
A new liquid biopsy method analyzes gene fragments in the bloodstream to detect and track cancer, enabling oncologists to tailor treatment approaches to individual patients. This non-invasive test can help monitor treatment success, detect cancer recurrence, and improve patient quality of life.
The 2024 Kavli Prize Laureates have made significant contributions to our understanding of exoplanet atmospheres, nanoscale materials for biomedical applications, and the localization of brain areas specialized for face recognition. Their work has broadened our knowledge of planetary life beyond Earth.
A study by Linköping University researchers found that disturbed blood flow can cause inflammation and breakdown of the vessel wall in cases of aortic dilation. This discovery could lead to better diagnosis and treatment options for patients at risk of serious complications.
A new study has found that FAPI PET/CT is more effective than FDG in predicting progressive pulmonary fibrosis in ILD patients. The study's results suggest that FAPI PET/CT can identify high-risk patients who require closer monitoring or preventive treatment.
The study uses the Dermatological Vision Dataset and compares vision transformers with traditional CNNs, achieving an impressive 97.8% accuracy on validation sets. Integrating ViTs into dermatology represents a promising step toward more accurate diagnostics.
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.
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.
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.