Articles tagged with Radiology
Priscilla Slanetz, a breast radiologist and professor at Boston University School of Medicine, has received the 2024 Marie Sklodowska-Curie Award for her outstanding contributions to advancing women in radiology. She is recognized for her clinical research, educational efforts, and leadership in promoting diversity and inclusion.
A seminal life cycle assessment (LCA) study found that diagnostic services generate the equivalent of nearly 1,100 gas-powered cars annually, with CT scans accounting for the majority of GHG emissions. The team identified areas to reduce energy consumption and greenhouse gas emissions without compromising patient care.
This editorial introduces persistence landscapes as a mathematical method to identify and correct biases in medical imaging. Persistence landscapes offer a way to reduce random noise while preserving important details, making it easier for clinicians to focus on meaningful image parts.
Researchers found lower pulmonary gas exchange may be associated with impaired cognitive function in patients with long COVID. The study also showed a potential treatment strategy using methods that target improved gas exchange, suggesting a causative relationship between cognitive dysfunction and lung dysfunction.
Researchers introduce a new tool called 'persistence images' that uncovers hidden biases in medical imaging data, advancing fairness in healthcare AI. The technology helps detect bias, filter out noise, and improve overall accuracy, leading to more reliable diagnoses and better patient outcomes.
Researchers introduce persistence barcodes, a tool that reduces diagnostic bias and uncovers subtle details in medical images. The method preserves key structural details, enabling clearer insights into the data.
A 10-year study found that short-term exposure to heat and air pollution was associated with an increase in medical imaging exams, particularly for chest, neuro, and musculoskeletal imaging. High temperatures had a greater impact on X-ray utilization, while high particulate matter levels were linked to increased CT scans.
Researchers have trained AI models to distinguish brain tumors from healthy tissue using convolutional neural networks and transfer learning. The models achieved an average accuracy of 85.99% at detecting brain cancer, with the ability to generate images showing specific areas in its tumor-positive or negative classification.
A study published in Radiology found that radiologists and physicians who received local explanations from artificial intelligence (AI) systems performed better and made faster diagnoses than those who received global explanations. The researchers also found that AI advice was trusted more quickly when it provided local explanations.
A novel deep learning model called Co-Plane Attention Across MRI Sequences (CoPAS) has been developed to assist with classifying 12 common types of knee abnormalities. The model achieved high classification accuracy comparable to that of radiologists, improving diagnostic efficiency and reducing errors.
A new approach using topological data analysis (TDA) enhances the reliability and reduces bias in AI systems used for medical diagnosis in radiology. TDA captures intricate features and provides a holistic view of medical images, leading to more accurate diagnoses and equitable patient care.
Neuro-oncology experts developed guidelines to standardize AI use in brain cancer diagnosis and treatment. The recommendations aim to ensure reliable clinical trial results and protect patients.
Researchers developed a new AI-powered diagnostic system, FastGlioma, which reveals invisible cancerous tissue in brain tumor surgery. The technique may delay or prevent recurrence of high-grade tumors and improve patient survival.
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.
The International Osteoporosis Foundation's Capture the Fracture campaign highlights top-performing Fracture Liaison Services that deliver gold-standard care. These services prioritize continuous care, tailored treatment plans, and teamwork to reduce secondary fractures and improve patient outcomes.
Researchers developed an AI model that can identify and measure aggressive prostate cancer lesions with high accuracy. The model's estimates of tumor size were associated with the likelihood of cancer recurrence or metastasis.
A new study suggests that MRI can predict patient outcomes and the risk of tumor reccurrence or spread for patients with rectal cancer who have undergone chemotherapy and radiation. This information could help doctors determine the best course of treatment, including whether to spare patients from invasive surgery.
Researchers develop Knowledge-enhanced Bottlenecks (KnoBo) method to emulate human physicians' education, resulting in more accurate and interpretable AI models for medical image recognition. KnoBo-based models outperform existing best-in-class models on accuracy and robustness, especially in handling confounded data.
A new study from UCSF has found that ChatGPT overprescribes in emergency care situations, providing unnecessary x-rays and antibiotics. The AI model was less accurate than resident physicians, with a tendency to err on the side of caution. Researchers emphasize the need for better frameworks to evaluate clinical information before AI c...
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.
The #HOPE4LIVER trial demonstrated the safety and efficacy of histotripsy as a treatment for primary and metastatic liver tumors, achieving a 95% technical success rate. The procedure's non-invasive nature and ability to spare critical vessels and bile ducts offer promising alternatives to traditional treatments.
A new study published in the Journal of Vascular and Interventional Radiology found that genicular artery embolization (GAE) provides durable benefit to patients with osteoarthritis in the knees for at least 2 years. The procedure targets abnormal blood flow to reduce inflammation, quickly improving patients' pain.
The integration of artificial intelligence in intratumoral immunotherapy can refine diagnoses, guide interventions, predict treatment responses, and adapt therapeutic strategies. This enables the enhancement of patient outcomes, including improved survival rates and quality of life.
A 10-year study found that digital breast tomosynthesis increases cancer detection rates and reduces advanced cancers compared to conventional 2D digital mammography. DBT detected more aggressive cancers at an earlier stage than digital mammography, leading to improved cancer detection and lower recall rates.
A new library of healthy participants' neuroimaging data has been created to aid in the diagnosis and treatment of traumatic brain injury (TBI). The Normative Neuroimaging Library consists of data from approximately 1900 individuals, providing a comprehensive dataset for clinicians and researchers.
The study found that ChatGPT-4 Vision performed well on text-based radiology exam questions but struggled with image-related questions, achieving an accuracy of 65.3%. The model's performance varied across subspecialties and images, with limitations in interpreting certain types of radiologic images.
Women with regular annual mammograms had higher overall survival and lower late-stage cancer rates than those screened less frequently. The study's findings contradict current guidelines, suggesting a significant benefit to annual screening for women over 40.
A uniform lexicon has been developed and endorsed by a multi-medical society panel to describe observations on ultrasound during the first trimester of pregnancy. The new terms aim to minimize bias, clarify findings, and respect patient preferences.
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.
A commercial AI tool was effective in excluding pathology and had lower rates of critical misses on chest X-rays compared to radiologists. The study found that the AI tool could autonomously report more than half of all normal chest X-rays without decreasing standard of care.
A new deep learning model detects clinically significant prostate cancer on MRI with performance comparable to experienced abdominal radiologists. The model's prediction can be used as an adjunct to improve diagnostic performance and reduce false positives.
Researchers found the woman was embalmed with costly imported ingredients, contradicting traditional beliefs about mummification. The study also revealed she suffered from arthritis and had a unique facial expression that may be attributed to a cadaveric spasm caused by intense pain.
Despite progress, radiology's gender gap persists due to structural biases. RSNA's efforts have increased female representation in faculty and leadership positions.
The Special Report explores expert perspectives on deploying AI in radiology, emphasizing the need for trust, reproducibility, explainability, and accountability. Radiologists must be able to trust in AI systems' design and receive adequate training, while establishing clear guidelines regarding clinical accountability.
The study establishes criteria for identifying extranodal extension on imaging, allowing radiologists to make accurate calls and provide patients with less invasive treatments. This shift towards practice change has the potential to reduce side effects and improve outcomes for patients with HPV-associated head and neck cancers.
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 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.
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...
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.
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.
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.