Articles tagged with Radiology
The Fleischner Society statement provides guidance on the use of chest imaging in COVID-19 management, recommending its use in patients with worsening respiratory status or moderate to severe features. The panel also found that CT is appropriate in patients with functional impairment and/or hypoxemia after recovery from COVID-19.
A new study found that artificial intelligence (AI) can accurately identify patients at risk of osteoporosis and fractures by analyzing electronic radiology reports, outperforming traditional manual methods. The AI tool, XRAIT, detected a significant number of major fractures, improving patient outcomes and reducing the burden of illness.
A new AI technique detects acute ischemic stroke lesions on MRIs with high accuracy, outperforming expert drawn gold standard. The fully automated approach reduces workflow time and operator bias in lesion segmentation.
A new online platform, CovED, aims to improve COVID-19 diagnosis by providing healthcare workers with rapid training and image-based diagnostic tools. Developed by DetectED-X, the platform can be accessed for free and is supported by leading corporations and healthcare experts.
A study found that per capita radiation exposure in the US decreased by 20% between 2006 and 2016, mainly due to a decline in nuclear medicine procedures. Meanwhile, CT scans increased but saw a small drop in effective dose thanks to advancements in dose modulation technology.
A panel of experts outlines priorities for handling COVID-19 cases, including early detection, limiting virus exposure, safety precautions, and training. Radiology departments must continue to plan and prepare for future outbreaks and pandemics.
The article highlights key areas of review for diagnostic radiologists, vascular and interventional radiologists, nuclear medicine and molecular imaging specialists, as well as radiographers and nursing units. It emphasizes the need for rapid sharing of accurate information, infection prevention, and control knowledge, and emotional ma...
A combination of AI algorithms and radiologist interpretations improved mammogram accuracy, avoiding unnecessary tests for 10% false positives. This approach has the potential to increase detection value and make healthcare more sustainable.
A recent study published in the American Journal of Roentgenology found that chest CT scans have a low misdiagnosis rate for COVID-19 and can standardize imaging features. However, CT remains limited for distinguishing between specific viruses.
A study published in Radiology analyzed chest CT images and X-rays of 14 teenagers with EVALI, revealing characteristic ground-glass opacity and subpleural sparing. The findings suggest that CT imaging is crucial for early diagnosis and timely management of the condition in pediatric patients
Mount Sinai physicians analyzed chest CT scans of Chinese COVID-19 patients, identifying specific patterns in lung disease as it develops over a week and a half. The study could lead to quicker diagnosis and prompt isolation in early stages.
A recent study found that chest CT imaging has a sensitivity of 98% for detecting COVID-19, compared to RT-PCR's 71%, making it a reliable and practical method for early diagnosis. This is particularly important given the highly contagious nature of the virus.
A meta-analysis of severe frostbite injuries found promising results using intraarterial and intravenous tissue plasminogen activator to reduce amputation rates. The study included 209 patients treated with thrombolytic therapy, resulting in a salvage rate of 76%.
Researchers describe key chest CT imaging findings in Wuhan coronavirus patients, highlighting bilateral ground-glass and consolidative pulmonary opacities. The study's findings suggest that lung cavitation, discrete nodules, and lymphadenopathy are characteristically absent in cases of 2019-nCoV.
Abnormal chest CT findings in patients with EVALI typically show diffuse lung injury with sparing of the periphery. Prompt medical treatment can decrease severity, but the exact cause of EVALI remains unclear. Long-term vaping risks pose concerns for nicotine and THC addiction, cardiovascular disease, and chronic pulmonary injury.
A study published in Radiology found that patients with abdominal aortic aneurysms and blood clots on the aorta wall experience faster growth and increased rupture risk. The researchers suggest adjusting imaging follow-up schedules for these patients to reduce the risk of rupture.
A team of researchers from Stanford University has developed a quantitative framework to sonographically differentiate between benign and malignant thyroid nodules. The framework achieved AUC values comparable to those of expert radiologists, suggesting its potential for establishing a fully automated system of thyroid nodule triage.
New statements from ACR and NKF clarify risk of contrast-induced acute kidney injury and provide recommendations for use of intravenous contrast media in patients with varying degrees of impaired kidney function. The authors emphasize the need for prospective controlled data to further understand the risk.
A deep learning method using a convolutional neural network (CNN) accurately differentiates between malignant and benign solid masses in small renal masses on contrast-enhanced CT scans. The corticomedullary phase showed the highest AUC value, indicating its effectiveness in malignancy prediction.
The Journal Healthcare Transformation: Artificial Intelligence, Automation, and Robotics explores the challenges and opportunities for integrating technology into consumer healthcare. A recent article found that five telemedicine companies provided nearly half of telemental health visits, highlighting the need for coordinated care.
A new AI model developed by researchers can predict which women are at future risk of breast cancer with higher accuracy than existing models. The deep neural network-based approach has a lower false negative rate, indicating that it can identify women who would benefit from additional screening with MRI.
A new study analyzed X-ray and CT scans of people injured in e-scooter accidents, finding that the majority had injuries to their upper extremities, particularly wrists. The study emphasizes the importance of helmet use and speed limits to prevent such injuries.
Researchers developed AI models for chest X-ray interpretation that can detect fractures, nodules, opacity and pneumothorax as effectively as experienced radiologists. The models were trained on large datasets and evaluated using a panel of radiologists to increase expert consensus and accuracy.
A panel of medical professionals will discuss the public health impact of e-cigarette use, with a focus on radiologic findings associated with vaping-related lung injury. The session aims to educate radiologists about this critical public health issue and provide guidance on identifying cases.
A new study presents a minimally invasive procedure using focused ultrasound to reduce tremors and improve quality of life in patients with Parkinson's disease. The treatment, known as MRgFUS thalamotomy, shows substantial improvement in 95% of patients, with significant reductions in tremor severity and quality of life.
An interdisciplinary team developed a simple method to identify the most accurate experts in groups by analyzing decision similarity. The research tested it successfully in various fields, including radiology and geopolitics, and found that the method accurately predicted accuracy.
A deep convolutional neural network-based software improved radiologist detection of malignant lung nodules by 70.3%, reducing false positives to 0.18 per X-ray. The study suggests machine learning methods can help overcome challenges in detecting lung lesions on chest X-rays.
Researchers have developed an intelligent metamaterial that boosts the energy emitted by a patient's body, increasing signal-to-noise ratio and improving MRI imaging. The technology reduces scan time and cost, making high-quality imaging more accessible to patients worldwide.
Researchers observed reversed halo signs in most cases of CT-based septic pulmonary embolism diagnosis related to IV substance use disorder. The sign is an early and reliable imaging finding.
A new AI algorithm developed by UCSF and UC Berkeley outperformed two out of four expert radiologists in detecting tiny brain hemorrhages on head scans. The algorithm achieved exam-level accuracy, tracing detailed outlines of abnormalities within the brain's three-dimensional structure.
Researchers developed a neural network, PatchFCN, trained on 4,396 CT scans to detect brain hemorrhage abnormalities with accuracy similar to human experts. The algorithm achieved high accuracy and pixel-level delineation, classifying abnormalities into different pathological subtypes.
A new AI tool, trained on a large dataset of mammography images, accurately identified breast cancer with 90% accuracy when combined with radiologist analysis. The study suggests that AI can augment human radiologists' diagnoses, reducing false-positive and false-negative results.
The AI system uses a huge database of x-ray images to identify collapsed lungs with 75% accuracy, outperforming medical specialists who diagnose fewer than 50%. Researchers plan to integrate the technology into a software system and apply it to other conditions, reducing treatment delays and improving patient outcomes.
The American Journal of Roentgenology review article details common imaging manifestations of vaping-associated lung injury, including hypersensitivity pneumonitis, diffuse alveolar hemorrhage, and organizing pneumonia. The article emphasizes the importance of recognizing these patterns in radiologists to prompt clinical teams to ask a...
A study of academic radiology departments found that there is no uniform approach to handling outside imaging studies, with some requiring formal reports and others allowing them in the institutional PACS. This lack of consensus may impact patient care and radiation safety.
The multi-society statement focuses on three areas: data, algorithms, and practice. It emphasizes the importance of ethical use of AI in radiology, ensuring benefits and harms are distributed fairly among stakeholders. Radiologists will need to acquire new skills to work effectively with AI tools.
A new AI system can accurately identify key findings in chest X-rays of pneumonia patients in just 10 seconds, significantly outperforming current clinical practice. This ultra-quick detection enables physicians to confirm a pneumonia diagnosis faster, allowing for timely treatment and reducing delays for severely ill patients.
A study published in the American Journal of Roentgenology found that diagnostic radiologists with lifetime ABR certificates were significantly less likely to participate in Maintenance of Certification (MOC) programs. Participation rates were only 13.9% among those with lifetime certificates, compared to nearly universal participation...
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.
A large study of almost 200,000 patients found that premedicating with antihistamines and switching contrast media can reduce recurrent allergic reactions. Genetic predisposition may also play a role in reaction to CT contrast agents, according to the researchers.
A lack of government action on NHS staffing undermines efforts to diagnose cancer early, with nearly half of all cancers diagnosed in England at stage 3 or 4. The health service needs an extra 1,700 radiologists and nearly 2,000 therapeutic radiographers to improve staff efficiency.
A new machine learning-based model evaluates immunohistochemical characteristics in patients with suspected thyroid nodules, achieving excellent performance for individualized noninvasive prediction. The model improves thyroid nodule diagnoses and helps identify papillary thyroid cancers earlier.
A recent study found that vaping impairs vascular function, reducing blood flow and increasing arterial stiffness in healthy adults. The study used MRI scans to measure the effects of e-cigarette inhalation on the body's vascular system.
The article provides a comprehensive overview of gender affirmation surgical therapies encountered in diagnostic imaging. It defines normal postsurgical anatomy and describes select complications using a multidisciplinary, multimodality approach. Key findings include the importance of regular prostate cancer screening for trans-female ...
A multidisciplinary team at Kaiser Permanente developed a complete imaging history definition, including four prompts for date, location, pain, and concern. This led to a significant increase in orders containing all four components, from 16% to 52%, and improved mean character count of entered histories.
A study found that smoking impairs the effectiveness of embolization treatment for pulmonary arteriovenous malformations (PAVMs) in patients with HHT. Smokers experienced a higher rate of PAVM persistence after treatment compared to non-smokers, with those who smoked more than 20 pack-years facing a fivefold increased risk.
A new AI tool, CXR-risk, analyzes chest X-ray images to predict health and mortality, providing independent information that surpasses radiologists' readings. The study found that CXR-risk identified people at high risk for future heart attack, lung cancer, or death with accuracy.
A new study found increased CT use for suspected urolithiasis patients in emergency departments, with utilization rates increasing by 100.8% between 2006 and 2014. Geographic variation was also noted, with CT scans being more frequent in higher-income ZIP codes and urban hospitals.
A new machine learning approach for low-dose CT imaging has been shown to perform as well as, or better than, traditional iterative techniques in an overwhelming majority of cases. The method allows radiologists to fine-tune images according to clinical requirements, enabling faster and more accurate scans.
A new AI tool developed by researchers at Stanford University improves clinicians' ability to correctly identify brain aneurysms by highlighting areas of interest on scans. The HeadXNet algorithm reduces the 'miss' rate and increases consensus among clinicians, with promising results but further investigation needed.
A new study published in the Journal of Vascular and Interventional Radiology found significant growth in hemodialysis conduit angiography utilization, increasing by 1700% nationally from 2001 to 2015. This trend was particularly pronounced among nephrologists, with a 24.0% increase in procedures performed annually in office settings.
A new AI system has been developed that can detect lung cancer with high accuracy, potentially leading to earlier treatment. The system uses deep learning to analyze low-dose chest computed tomography (LDCT) scans and performs better than human radiologists in detecting malignant nodules.
Researchers evaluated clinical and radiological features of fibro-adipose vascular anomaly (FAVA), a soft-tissue vascular lesion. FAVA is characterized by unique clinico-radiological features, including a painful intramuscular lesion in extremities, progressive heterogeneous enhancement on MRI, and associated phlebectasia.
A recent study reveals that radiology residents in the US are not adequately trained to identify and report child abuse. The study, presented at the ARRS 2019 Annual Meeting, found that residents correctly identified only 37.6% of non-accidental trauma cases over a five-year period.
Jonathan S. Lewin, a renowned expert in interventional and intraoperative MR imaging, has received the 2019 ARRS Gold Medal for his distinguished contributions to radiology. With over 200 peer-reviewed scientific manuscripts published, he is a pioneer in advancing knowledge and skills in radiology.
The upgraded picture archiving and communication system (PACS) significantly reduces the time radiologists spend searching for prior studies, with average search times decreasing from 6.1 to 4.6 seconds. The new system also reduces total study reading time, with a decrease of 137 to 113 seconds per study.
Christine Menias, MD, is recognized for her dedication to radiology education, with a notable track record of instructional courses and awards. She has been named the 2019 ARRS Distinguished Educator for her inspirational teaching and commitment to improving competence and patient outcomes.
A survey of fourth-year radiology residents who took the 2018 ABR Core Exam found that those who passed perceived higher value in preparation resources. Residents who passed also had more study time off and higher USMLE step 1 scores compared to those who failed.
A new AI system called FocalNet has been developed to aid radiologists in detecting prostate cancer, achieving 80.5% accuracy in reading MRI scans. The system uses machine learning to evaluate tumors and predict their aggressiveness, providing diagnostic guidance to less-experienced radiologists.
A workshop published a roadmap for AI in medical imaging, highlighting key research themes and prioritizing foundational machine learning research. The report emphasizes the need for collaboration among professionals, funding agencies, and institutions to develop innovative imaging technologies.