Articles tagged with Radiology
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.
A commercial AI tool did not provide additional benefit to mammography with supplementary ultrasound in patients with dense breasts. Mammography with supplementary ultrasound showed higher accuracy, specificity, and lower recall rate compared to AI alone or combined with US.
A new position paper reviews TBS for fracture risk prediction, treatment initiation and monitoring in osteoporosis. The evidence-based guide provides practical guidance for clinicians to integrate TBS into clinical practice.
Lacking Medicare coverage for screening CTC may contribute to greater income-based differences in its use compared to other recommended screening strategies or diagnostic CTC. Medicare coverage of CTC could reduce income-based disparities for individuals avoiding optical colonoscopy due to invasiveness or complication risk.
A synoptic report improved the completeness of pretreatment CT reports in patients with advanced ovarian cancer. Involvement of surgically established unresectable or challenging-to-resect disease sites was mentioned in all disease-specific synoptic reports, compared to 37% of simple structured reports.
Researchers developed a chest imaging protocol using photon-counting CT, allowing for simultaneous evaluation of lung structure, ventilation, vasculature, and perfusion. The protocol showed advantages over standard CT, providing high image quality at lower radiation doses and better spectral resolution.
Using MRI instead of CT with CTA alone in patients presenting to the ED with dizziness was associated with greater critical neuroimaging results and more frequent echocardiography. Patients with dizziness undergoing MRI showed a lower frequency of 90-day ED readmissions compared to those discharged after CT with CTA alone.
Researchers at Paul Scherrer Institute have developed a beamline modification called momentum cooling to increase the transmission of proton beams, allowing for shorter treatment times. This breakthrough could enable faster treatments while maintaining accuracy and sparing healthy tissue, with potential benefits including reduced costs...
A new AI tool utilizing deep-learning algorithms and volumetric thresholds accurately detected splenomegaly in patients undergoing CT scans. The study analyzed a large sample of 8,901 patients, revealing that splenic volume is strongly associated with weight.
A deep learning-based model developed in 7,105 patients predicted 30-day all-cause mortality in patients with CAP with AUCs ranging from 0.77 to 0.80. The model showed higher specificity than the CURB-65 score at the same sensitivity.
A study published in Radiology found that AI algorithms with high diagnostic accuracy improved radiologists' detection of lung cancers on chest X-rays. High-accuracy AI led to more frequent changes in reader determinations, suggesting increased human trust in AI.
The study found that preferential use of pulmonary MRA for diagnosing PE in the general population helped conserve iodinated contrast media. This substitution reduced iodine usage by approximately 27 liters during the specified time period, which is a noteworthy benefit considering the shortage's impact.
Researchers found that ChatGPT 4 can accurately identify appropriate imaging services for breast cancer screening and breast pain. The AI model outperformed its predecessor, ChatGPT 3.5, in providing evidence-backed guidelines for radiologists.
A study found that patients with transient ischemic attack (TIA) who received incomplete neurovascular imaging were at higher odds of experiencing a stroke within 90 days. The research, published in the American Journal of Roentgenology, used Medicare claims data to analyze TIA emergency department encounters.
A recent study published in Radiology found that AI algorithms performed better than the standard Breast Cancer Surveillance Consortium (BCSC) risk model for predicting five-year breast cancer risk. The AI models extracted hundreds of additional mammographic features, leading to improved predictive performance.
A multicenter study found that deep learning-based computer-aided diagnosis significantly improved radiologists' diagnostic performance for breast lesion classification on ultrasound. The use of CAD resulted in a 6.0% upgrade of BI-RADS category 3 assessments to category 4A, with 16.7% being malignant.
Researchers investigate the potential of ChatGPT in clinical radiology, highlighting its positive considerations, such as enhancing patient care and radiologist education, as well as negative concerns, like accuracy and transparency issues. The paper aims to initiate an engaging discussion on the use of AI-powered chatbots in this field.
A study found that expert mammogram radiologists exhibit visual hindsight bias, leading to improved performance on blurry images after prior clear images were viewed. This bias can impact the detection of lesions and has implications for negligence lawsuits and patient outcomes.
A recent study found that COVID-19 infection can lead to increased infarct growth and ongoing tissue consumption even after successful reperfusion through angiography. This suggests a potentially aggressive clinical course for patients with COVID-19 and large-vessel occlusion acute ischemic stroke.
Online radiologists choose studies based on financial attractiveness, leading to delays in high-priority cases. This study found that expedited priority class contained the highest percentage of delayed studies.
Large language models like ChatGPT show incredible potential in radiology, but also struggle with reliability and accuracy. The latest model GPT-4 improves advanced reasoning capabilities and performs better on higher-order thinking questions.
A study of 5302 patients found no significant difference in bleeding events between those temporarily discontinuing and maintaining antithrombotic therapy during image-guided breast biopsies. The findings support the safety of continuing antithrombotic therapy, but counsel patients on bruising risk.
The American College of Radiology (ACR) has introduced new guidelines recommending earlier and more intensive breast cancer screening for high-risk women, including Black women. According to the ACR, risk assessment by age 25 is necessary to determine if screening should start before age 40.
Researchers successfully performed the first in-utero surgery to repair a potentially deadly developmental condition called vein of Galen malformation. The procedure involved treating an aggressive vascular malformation before birth, resulting in improved cardiac output and no signs of negative effects on the brain after birth.
A new onsite deep-learning based FFR-CT algorithm demonstrates excellent diagnostic performance, with high interobserver and intraobserver reproducibility. The algorithm showed AUC of 0.975, sensitivity of 93.5%, and specificity of 97.7% for hemodynamically significant stenosis.
A study published in Radiology found that AI-based decision support systems can impair radiologist accuracy on mammograms, particularly for less experienced radiologists. Even highly experienced radiologists were adversely impacted by the system's judgments, highlighting the need for safeguards to mitigate automation bias.
A new study found that turning off MRIs overnight reduces their energy use by 25-33% and switching to 'power save' mode decreases energy use by 46-51%. This technique could save U.S. healthcare between $8 million and nearly $11 million annually.
A new study found that an AI-based algorithm was superior at detecting extranodal extension in oropharynx cancer compared to expert radiologists. This technology aims to improve treatment outcomes by predicting which patients are suitable for surgery or chemotherapy and radiation.
A study presented at the ARRS Annual Meeting reveals institutional variability in projection order and image acquisition timing for CEM protocol. Earlier-obtained recombined imaging is significantly preferred for cancer lesion characterization, with a trend towards prioritizing imaging the side of pathology.
A study found that sleeve gastrectomy decreases bone mineral density and strength in adolescents and young adults. Participants who underwent weight loss surgery lost significant amounts of weight and experienced reductions in abdominal fat tissue and muscle mass.
A study by University of Cambridge researchers found that minority ethnic groups are less successful in applying to specialty training programmes in the NHS. Female applicants had a higher success rate overall, but certain specialities tend to appeal more to one gender or the other.
The American Roentgen Ray Society presents its 2023 Honorary Lecture on Advanced High-Resolution CT, dedicated to the late Dr. W. Richard Webb, who transformed thoracic imaging with his foundational work in HRCT. The lecture explores multiple conditions diagnosed via HRCT and the radiologist's role on multidisciplinary teams.
A special report highlights radiology's role in combatting climate change, emphasizing the importance of sustainable practices in healthcare. Radiologists are well-positioned to innovate more sustainable methods of care, such as telemedicine and energy-efficient equipment purchasing.
A study at ECCMID shows AI software can detect TB from chest X-rays with sensitivity and specificity comparable to that of trained radiologists. The technology has the potential to improve diagnosis in low-resource settings where radiologists are scarce.
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 found that correctly occluded left atrial appendages minimized left atrial flow stasis and thrombogenicity using 4D flow MRI and 3D phantoms. The study provides a clinical goal for the procedure in patients with atrial fibrillation.
Dr. Erik K. Paulson, MD, chair of Duke University's radiology department, has been elected as the 123rd President of ARRS for a one-year term starting April 2023. He succeeds Gary J. Whitman, who served from 2022 to 2023.
A novel technique of three-dimensional (3D) slice-to-volume (SVR) MRI allows for precise delineation and measurement of the fetal optic pathway (FOP). The study found that FOP was visualized in 9 cases in unprocessed fetal MRIs, compared to 55 cases in 3D SVR images.
Hokkaido University researchers evaluate a material used to build model arteries, finding it suitable for medical education and surgical practice. The study also highlights the potential for improved assessment of 3D printing technology in creating highly accurate models of individual patient's artery structure.
A study published in Radiology found that digital breast tomosynthesis improved breast cancer screening performance and increased radiologists' interpretive accuracy compared to digital mammography. The results showed higher detection rates, sensitivity, and specificity for DBT, with 97.6% of assessed radiologists meeting recommended p...
A new AI-powered worklist reprioritization tool has been shown to significantly reduce both report turnaround time and wait time for pulmonary embolus-positive CT pulmonary angiography examinations. The study found a reduction of 47.6 minutes in report turnaround time and 21.4 minutes in wait time, with no significant difference in rea...
A new imaging technique using a novel nuclear medicine tracer has been developed to improve the treatment of intestinal strictures in Crohn's disease patients. The technique allows for precise differentiation between inflammation and fibrosis, enabling targeted therapy.
A large-scale study found that diagnostic errors in neuroradiology were associated with longer interpretation times and higher shift volumes. The study also revealed a significant increase in diagnostic errors during weekend work, highlighting the need for targeted quality improvement interventions.
A new PET/CT scanner prototype has shown promising results in preclinical testing, offering higher resolution and accurate tumor assessment. The scanner aims to enhance treatment planning for head and neck cancers, which have a low five-year survival rate and high recurrence rate.