Articles tagged with Radiology
A national survey of radiologists reveals moral distress is widespread, often stemming from high case volumes and lack of administrative support. The study highlights the need for education of leadership regarding causes of distress among frontline workers to address this issue.
Researchers found that DALL-E 2 has learned relevant representations of x-ray images and shows potential for text-to-image generation, but its performance varies with CT, MRI, and ultrasound image prompts. Synthetic data generated by DALL-E 2 could accelerate deep learning tools in radiology and address data sharing concerns.
A study of US newspaper articles found that lung cancer screening was generally positively covered between 2010 and 2022. However, crucial aspects such as enrollment criteria and cost issues were frequently omitted, highlighting a need for radiologists to take an active role in media coverage.
A large study of over 1 million women found DBT had a higher breast cancer detection rate and lower false positive rates compared to 2D digital mammography. The results suggest that DBT may be the more effective screening method for early breast cancer detection.
A recent study found that US zip codes in extreme socioeconomic disadvantage have limited access to accredited medical imaging facilities and centers of excellence. This lack of access can lead to delayed or missed diagnoses, exacerbating existing health disparities.
Researchers developed AI models based on UNet and MobileNet architectures to analyze standardized abnormalities in CT images, accurately identifying object presence and confidence. These models achieved an absolute percentage error of less than 5 percent, comparable to human professionals.
Axillary lymphadenopathy after a COVID-19 vaccine booster dose resolves at a mean of 102 days. Findings support follow-up intervals of at least 12 weeks to avoid delaying screening mammography for suspected vaccine-related lymphadenopathy.
A study published in Radiology found that an AI tool can accurately identify normal and abnormal chest X-rays with a high degree of sensitivity. The tool identified abnormal chest X-rays with a 99.1% sensitivity rate, outperforming clinical board-certified radiologists.
A novel AI architecture, relational reasoning network, accurately identifies anatomical landmarks in CT scans for orthodontic treatments. The model learns spatial relationships between landmarks without explicit image segmentation, achieving accuracy comparable to conventional methods.
A new consensus statement calls for routine radiological screening of contact children in suspected child physical abuse cases to prevent further harm. Siblings are at risk of experiencing physical abuse, with a strong association between index and contact children.
Using a dual-source CT scanner eliminates the need for beta-blockers and decreases nondiagnostic examinations, resulting in shorter CT completion times. The study found no difference in ED length of stay but demonstrated significant benefits in reducing nondiagnostic examinations.
The American Roentgen Ray Society (ARRS) has awarded four radiologists with the 2023 Resident/Fellow in Radiology Awards for their exceptional research, education, and administrative work. Drs. Elliot T. Varney, Vincius Alves, Luca Pasquini, and Pallavi Srivastava will present their research findings at the upcoming ARRS Annual Meeting...
Researchers found that IVC filters were effective in preventing pulmonary embolisms (PE) in patients with deep vein blood clots, even when used prophylactically. Approximately half of the patients had their filters removed without complication or recurrence of DVT or PE.
Automated AI body composition tools achieved high technical adequacy rates (97.8-99.1%) in a large sample of external abdominal CT scans. The results support the generalizability and potential for widespread adoption of these tools, which can help build trust among radiologists and physicians.
Researchers identified a higher rate of premature arteriovenous graft failure among African American patients undergoing dialysis access interventions. The study, published in Radiology, found that these patients were more vulnerable to experiencing early dialysis access dysfunction and failure.
Research found PIRI to be an independent predictor of infarct growth rate and 90-day outcomes in large-vessel occlusive stroke. The study suggests PIRI may help identify patients who could benefit from late-window endovascular thrombectomy.
Ultrafast MRI provides beneficial information for predicting DCIS upgrade to invasive cancer, including time to enhancement and lesion size. A study found that UF-MRI can accurately predict lesions diagnosed as DCIS at percutaneous biopsy that will be upgraded to invasive cancer at surgery.
Researchers found that AI significantly improved the detection of lung nodules on chest X-rays, detecting actionable nodules at a rate of 0.59% compared to 0.25% without AI assistance. The study suggests that AI may work consistently across different populations.
A systematic review and meta-analysis found that MRI-based surveillance after surgical treatment can detect clinically occult local recurrences, potentially improving patient outcomes. The study included 19 studies and showed a significant association between high-intensity surveillance and the detection of local recurrences.
A deep learning model distinguishes colon carcinoma from acute diverticulitis in CT images, improving diagnostic performance and reducing false-negative and false-positive findings. The model's sensitivity and specificity were significantly improved, supporting radiologists in patient care.
A new study published in Radiology: Cardiothoracic Imaging shows that photon-counting detector CT can acquire high-quality images at lower contrast media volume than conventional CT scanners. The technology reduces the amount of contrast needed for CT angiography, improving image quality and reducing environmental impact.
Researchers created monthly infant brain atlases to track normative trends in brain development. The atlases reveal changes in brain structure, cortical geometry, and tissue contrast, shedding light on conditions like ADHD and dyslexia. The study aims to facilitate discovery of new insights into child cognition and social behavior.
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 used deep learning AI to analyze chest X-rays of patients with acute chest pain syndrome, identifying those at high risk for life-threatening conditions. The model significantly improved prediction accuracy beyond conventional clinical markers, allowing for more accurate triage and potential deferral of additional testing.
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.
A recent study found that AI failed to pass a radiology qualifying examination, with an average accuracy of 79.5%. However, the researchers suggest that further training and revision could lead to improved results.
A new study published in the American Journal of Roentgenology found significant cost savings by eliminating sedation during pediatric brain MRI examinations. The analysis calculated costs for sedated, nonsedated, and limited MRI exams at three US hospitals, revealing that labor costs were the largest expense category.
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.
The American Roentgen Ray Society has awarded the 2023 ARRS Gold Medal to Dr. Bernard F. King, Jr., MD, FACR, FSAR, in recognition of his distinguished service to radiology. Dr. King has made significant contributions to the field, including the development of contrast media recognition and treatment protocols.
A team led by Abhinav Jha is developing a novel low-count quantitative single photon emission computed tomography (LC-QSPECT) method to measure the concentration of radiopharmaceutical material in tumors and vital organs. This technique aims to provide accurate measurements of absorbed dose from radiation therapy, helping plan treatmen...
Veterinary experts warn that AI algorithms used in radiology and imaging can provide faulty or incomplete diagnoses, posing risks to patient care. The absence of regulatory oversight for veterinary AI products increases concerns over accuracy, transparency, and potential harm.
A study published in the American Journal of Roentgenology found that reconstruction using BI64 kernel and 0.4-mm slice thickness yielded improved bronchial division identification and pulmonary fissure sharpness without loss in pulmonary vessel sharpness or pathology conspicuity.
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.
For adrenal lesions evaluated by single-phase dual-energy CT, fat fraction had significantly higher sensitivity than virtual noncontrast attenuation at both clinically optimal threshold and traditional ≤10 HU threshold. Fat fraction-derived metrics can help definitively diagnose incidental adrenal lesions as adenomas.
Researchers found a strong association between hotter weather, more sunshine, and higher volumes of polytrauma CT scans. The study used machine learning algorithms to forecast daily polytrauma CT occurrence, predicting 73% of high-demand days and 83% of low-demand days.
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.
A large-scale international study found that patients diagnosed with lung cancer at an early stage via CT screening have a 20-year survival rate of 80 percent. This is compared to the average five-year survival rate for all lung cancer patients, which is 18.6 percent.
Researchers evaluated modified CEUS using perfluorobutane for diagnosing hepatocellular carcinoma in high-risk patients. The study found no significant difference in sensitivity, specificity, or accuracy between modified CEUS and CT/MRI LI-RADS v2018.
Researchers from the University of Ottawa found an association between marijuana smoking and chronic damage to airways, including higher rates of paraseptal emphysema and airway inflammatory changes. The study suggests that marijuana smoking may lead to additional lung damage beyond what is seen in tobacco smokers.
The IOF and ESCEO position paper recommends retaining the current WHO diagnostic criteria for osteoporosis, which has served the field well. The paper suggests that distinguishing between diagnostic and intervention thresholds can help improve treatment of patients at high risk of fractures.
A multicenter study found that left ventricular global longitudinal strain is a significant independent predictor of all-cause mortality and/or heart-failure hospitalization in patients with ischemic or nonischemic dilated cardiomyopathy. The study used cardiac MRI feature tracking to calculate six myocardial strain parameters.
The American Roentgen Ray Society (ARRS) named Dr. Sarah Kamel of Thomas Jefferson University Hospital and Dr. Ankur Goyal of the All India Institute of Medical Sciences as Melvin Figley and Lee Rogers Fellows in Radiology Journalism, respectively. They will receive expert instruction in scientific writing and communication through the...
Dual-energy CT (DECT) metrics provide insight into physiologic changes after lung cancer surgery, beyond PFTs and conventional CT. Lung perfusion ratio increases were greater after lobectomy than limited resection.
An AI-based model has been developed to assist radiologists in detecting and identifying leadless implanted electronic devices (LLIEDs) on chest X-ray images. The model achieved high detection and classification accuracy, even with suboptimal image quality, and showed promise for real-world deployment.
A study published in American Journal of Roentgenology found that a PACS tool improved completion rates of clinically necessary follow-up imaging. The tool identified socioeconomically disadvantaged patients at increased risk of failure, and referrer agreements played a crucial role in improving recommendations.
A deep learning algorithm trained on adult populations shows improved sensitivity for diagnosing thyroid nodules in children, with higher accuracy than traditional ACR TI-RADS. The study suggests the potential benefits of this new approach for evaluating thyroid nodules in younger patients.
A study by the Radiological Society of North America found that health insurance companies often pay more than necessary for radiology services, leading to higher premiums and out-of-pocket costs. The study also suggests that radiologists can play a key role in delivering high-quality, low-cost care to patients.
A new report from a joint task force recommends improved access to interventional radiologists in small and rural areas. The task force focused on strategies for recruitment and retention, including training opportunities, financial models, and community development.
A study of over 4 million claims found that Black women had less access to new mammography technology, even when receiving mammograms at the same institution. The disparities in digital breast tomosynthesis use among Black and white women began to subside after 2016.
Researchers have developed a novel, non-invasive way to measure blood flow to the brains of newborn children at the bedside. This method uses real-time ultrasound color flow technique and 3D sampling to capture flow in a completely painless and safe manner.
A study using gadoxetate disodium-enhanced MRI found associations between imaging characteristics and hepatocellular adenoma (HCA) subtypes. The algorithm identified common HCA subtypes with high accuracy, including β-catenin exon 3 mutations.
A new study found that fewer Asian, Black, and Hispanic patients qualify for anti-amyloid monoclonal antibody treatments, which may slow Alzheimer's progression. The treatment's effectiveness depends on the presence of amyloid plaques, with lower incidence in minorities.
A study published in the American Journal of Roentgenology found that large specialized referral centers for complex IVC filter retrievals achieved a 100% success rate and low adverse event rates. Prolonged filter dwell time was identified as an independent predictor of adverse events.
A prospective multicenter case-control study found that third-trimester fetuses exposed to opioids in utero exhibited smaller brain biometric measurements and altered fetal physiology. The study suggests a possible link between prenatal opioid exposure and postnatal clinical outcomes.
A prospective study found that patient decision aids improve patients' self-perceived understanding of image-guided procedures and satisfaction with the consent conversation. Patients who received a PDA before their visit reported significantly greater understanding and felt more listened to by their clinician.
The contrast-enhanced in-phase Dixon sequence demonstrated higher sensitivity and reader confidence for detecting breast biopsy clips on MRI. The sequence also showed a higher contrast-to-noise ratio without affecting positive predictive value.
A new AI tool has been developed to detect pancreatic cancer on CT scans with high accuracy, achieving 90% sensitivity and 96% specificity. The tool's performance is comparable to that of radiologists, offering a promising supplement for early detection and improved treatment outcomes.
A study found that trained radiographers perform as well as radiologists in key areas of double reading mammograms, improving cancer detection rates. The results suggest that focused training and experience are key factors in reader performance, not just medical degree or broad radiology education.
Researchers at Mayo Clinic Comprehensive Cancer Center found that metabolic imaging, combined with traditional treatment response assessment methods, can provide critical information to guide therapy for pancreatic cancer patients. The use of positron emission tomography (PET) with 18-fluorodeoxyglucose (FDG) tracer adds significant pr...