Articles tagged with Radiology
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 found that AI models that analyze medical images can predict patient demographics with high accuracy but struggle to diagnose patients from diverse backgrounds. The models use demographic shortcuts, leading to incorrect results for women, Black people, and other groups.
The policy statement offers guidelines for institutions and medical professionals to weigh benefits and risks of imaging in emergency departments. It includes recommendations for deferring imaging in children transferred to pediatric referral centers and using shared decision-making strategies when multiple reasonable choices exist. Th...
A new study from the University of Copenhagen reveals that AI has helped detect significantly more cases of breast cancer and reduce radiologist workloads. The AI system has been used to analyze tens of thousands of X-rays every year, resulting in a 12% increase in detected cases, including more small tumours of one centimetre or less.
Researchers found that hospitals that implemented standardized protocols from the American Heart Association and American Stroke Association saw significant reductions in stroke treatment times. The protocols, which include specific limits on time between symptom onset and hospital arrival, helped medical teams respond more quickly to ...
A recent study published in The Lancet Oncology found that an AI system can detect prostate cancer nearly seven percent more significantly than a group of radiologists using MRI scans. Additionally, the AI identifies suspicious areas less often, potentially reducing unnecessary biopsies by half.
A study published in Radiology found that AI significantly improved breast cancer detection and reduced false-positive findings. The AI system detected more cancers while lowering the rate of unnecessary recalls, reducing radiologist workload by 33.4%.
A study published in Radiology found that low-level light therapy increased resting-state functional connectivity in patients with moderate traumatic brain injuries. The treatment showed improved brain connectivity within the first two weeks, but its long-term effects are still unclear.
A new study found that GPT-4 and Google Gemini performed poorly in breast imaging classification, with a high percentage of discordant assignments that could impact patient management. The researchers emphasize the need for regulation of these large language models in high-level medical scenarios.
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.
Radiologists are proposing actions to reduce their department's greenhouse gas emissions and become more resilient to the effects of climate change. They suggest implementing a coordinated approach, establishing sustainability teams, and optimizing scanner usage to reduce emissions by up to 33% for MRI and CT scans.
A study found that GPT-4 matched the performance of radiologists in detecting errors in radiology reports, with an accuracy rate of 82.7%. The use of GPT-4 resulted in lower mean correction cost per report than the most cost-efficient radiologist.
The International Osteoporosis Foundation (IOF) and ESCEO presented awards to 20 young investigators from 16 countries for their outstanding research abstracts. The awards recognize the researchers' contributions to the prevention, diagnosis, and treatment of osteoporosis and related musculoskeletal diseases.
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 found that a non-invasive imaging test can help identify patients with coronary artery blockage or narrowing who need a revascularization procedure. The test, called CT-FFR, uses CTA images and AI algorithms to model coronary blood flow, reducing the need for invasive procedures.
Researchers developed an AI algorithm to identify normal mammograms and ran a simulation on patient data, revealing that AI can reduce unnecessary testing while maintaining cancer detection rates. The study suggests that AI can help doctors focus on more questionable scans, reducing false positives and improving workflows.
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 comprehensive review examines the impact of osteoporosis on fracture healing and the effects of osteoporosis medications. The study concludes that there is no deleterious effect of osteoporosis medications on fracture healing, and prompt treatment can reduce the risk of secondary fractures.
Researchers found that doctors can confidently skip 50% of biopsies by combining MRI-based prostate imaging reporting and data system scores with prostate-specific antigen density testing. The study suggests that this approach can decrease patient harm and healthcare costs associated with unnecessary biopsies.
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 new study finds that individual clinician differences play a significant role in how human radiologists perform with AI assistance. The research suggests that AI can boost performance for some doctors but harm others, highlighting the need for personalized assistive AI systems.
Researchers have discovered that neurochemicals influence neural activity, blood flow, and fMRI measurements. The study found that certain neurochemicals can cause constrictions in blood vessels, leading to negative fMRI signals.
A new AI model, called Lars, can accurately detect signs of lymph node cancer in 90% of cases, reducing workload for radiologists and increasing access to healthcare. The model was developed using a large dataset of over 17,000 images from 5,000 patients and is based on deep learning technology.
A study by researchers at UNC School of Medicine found that chronic cocaine use alters the functional networks in the brain, including the default mode network and salience network. This disruption can make it harder for individuals to focus, control impulses, or feel motivated without the drug.
The new guidelines recognize the aorta as an independent organ, bundling its treatment with other specialties. This holistic approach improves treatment outcomes for patients with aortic rupture and other serious diseases.
Researchers have developed magnet-guided microrobots that can target and treat liver tumors using an MRI device. The robots are guided by a magnetic field and use gravity to navigate to the tumor, preserving healthy cells.
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.
A new study found that propranolol, a medication for high blood pressure, can lower anxiety levels in individuals with autism spectrum disorder. The study involved 69 patients and showed significantly reduced anxiety levels compared to a placebo group.
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 at the University of Münster found that DBT+SM detects invasive breast cancers more effectively than conventional DM, with a higher rate of early tumor stages. This approach may lead to improved screening effect and reduced breast cancer mortality.
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.
Experts recommend increasing the use of intravascular ultrasound (IVUS) in lower extremity revascularization procedures to optimize outcomes and reduce complications. The technique provides detailed information about vessel walls, plaque composition, and blood flow characteristics, enabling more accurate diagnosis and treatment planning.
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 new article in Radiology discusses AI applications for musculoskeletal radiology, highlighting its potential to improve complex tasks like disease prognostication and prediction of clinical outcomes. However, challenges such as data quality and multi-institutional collaboration need to be overcome for successful implementation.
Researchers at UCLA Health have received a $3 million grant to identify novel biomarkers and develop AI for detecting aggressive prostate cancer. The project aims to improve treatment accuracy and reduce unnecessary interventions.
Researchers analyzed over 24 million assault-related injuries and found that 40% resulted in anoxia, with IPV accounting for 30-40% of neck contusions. The study aims to raise awareness of strangulation among medical providers and advocate for comprehensive screening in IPV patients.
Researchers at Temple University Health System report that the BASHIR Endovascular Catheter reduces blockages in lung arteries and correlates with improved right ventricle function, suggesting better survival outcomes. The catheter also shows significantly lower bleeding rates compared to other devices.
A new study has found that most patients with osteoporosis want to receive information on their fracture risk, but only half of them actually get it. Patients prefer visual representations, such as graphs with a colored traffic-light system, to communicate fracture risk and are more likely to take medication if presented in this way.
Researchers used a stellate ganglion block to restore the sense of smell in patients with long-COVID, showing near 100% resolution of phantosmia in some patients. The treatment was effective in improving symptoms for up to 49% of patients at three months.
A new study published in Radiology shows that patients diagnosed with lung cancer via CT screening have a 20-year survival rate of 81 percent, significantly higher than the average five-year survival rate for all lung cancer patients. Early detection through routine screening can lead to a cure if found early.
Researchers at the University of Cincinnati Cancer Center found that SmartClips, a wireless localization method for breast tumors, is safe and effective. The devices reduce patient discomfort and improve surgery efficiency by allowing surgeons to locate tumors more accurately.
Researchers used dynamic total-body PET scans to visualize immune T cell distribution in recovering patients. The study found increased concentrations of CD8+ T cells in the bone marrow of recovering COVID patients compared to healthy controls.
A simple MRI scan and a new metric for prostatic urethra length may help identify men at risk of chronic urinary side effects after radiation therapy. The study found that longer prostatic urethras are associated with a higher likelihood of these symptoms, which can impact quality of life.
A new method using deep learning can provide as much information from brain images taken with CT as images captured with MRI, enhancing diagnostic support for conditions like dementia and other brain disorders. The software has been trained on 1,117 people and shows promise in diagnosing normal pressure hydrocephalus.
A new study from Linköping University has found that the appearance of the thymus gland in chest CT scans is linked to immune system ageing. The researchers examined over 1,000 Swedish individuals aged 50-64 and found that fatty degeneration of the thymus was more common in men and those with abdominal obesity. Lifestyle factors such a...
Researchers tested the feasibility of using locally run LLMs like Vicuna-13B to label key findings in chest X-ray reports while preserving patient privacy. The results showed moderate to substantial agreement with non-LLM computer programs, suggesting that these models can be a viable option for AI research.
A new AI tool developed by Brazilian researchers can detect potentially cancerous lung nodules in CT reports, missing a crucial early diagnosis. The NLP tool achieved an accuracy rate of 97% in identifying suspicious nodules.
The study developed a highly accurate AI model for fully automated cancer detection, including small and difficult-to-detect tumors. The model could detect visually imperceptible cancer from normal-appearing pancreases substantially early before clinical diagnosis, with a median of 438 days.
A new generative AI model enhances clinical accuracy and textual quality of chest radiograph reports, surpassing traditional methods like teleradiology. Its implementation could enable timely alerts for life-threatening conditions, aiding imaging interpretation and documentation.
A new AI model integrates imaging and non-imaging patient data for improved diagnostic performance on chest X-rays. The multimodal model outperformed other models for diagnosing up to 25 conditions, showing potential as an aid to clinicians in high-pressure diagnoses.
A new size threshold for retropharyngeal lymph nodes is proposed to improve risk stratification and treatment decisions in patients with nasopharyngeal carcinoma. Using a 6-mm threshold, the study found significant differences in overall survival between stage-I and stage-II disease.
A study comparing radiologist and AI performance in interpreting 2,000 chest X-rays found that AI tools achieved moderate sensitivity but high false-positive rates. Radiologists outperformed AI in detecting the absence of disease, especially for complex cases.
A study comparing PI-RADS 2.0 and 2.1 found no significant differences in upgrade (29% vs. 22%) or downgrade (19% vs. 21%) rates from targeted biopsy to radical prostatectomy, suggesting no improvement in prostate cancer grade assessment with the latest PI-RADS update.
An AI system by Lunit identified improperly positioned endotracheal tubes with high sensitivity and specificity, allowing for earlier repositioning and reducing complications. The study included 539 chest radiographs from three institutions and showed promising results in detecting ETT presence and improper position.
The European Society of Cardiology calls for competency-based cardiac imaging delivery to enhance effective and efficient patient care. This approach enables cardiologists to deliver high-quality imaging services, select the most appropriate modality for each clinical scenario, and provide informed decision-making.
Researchers developed a deep-learning model to assess CXR images for probable COVID-19 severity. The model achieved an area under the receiver operating characteristic curve of 0.78 when predicting intensive care need within 24 hours.
A new study by Harvard Medical School researchers found that automated scoring systems for AI-generated radiology reports fail to reliably identify clinical errors. The team designed a new method and composite evaluation tool to better evaluate the performance of AI tools in generating clinically useful and trustworthy reports.
A radiomic-based model using T2-weighted MRI data achieved high accuracy in diagnosing pediatric Crohn disease, outperforming expert radiologists. The model was ensembled with clinical data to further improve performance.
Researchers at Lund University found AI-supported mammography screening to be safe and effective, detecting 20% more cancers than standard double reading without increasing false positives. The study also reduced radiologists' screen-reading workload by 44%, saving approximately five months of time.