Articles tagged with Radiology
A study found racial and ethnic minorities are less likely to receive standard-of-care diagnostic imaging after abnormal screening mammograms compared to white patients. Minority groups were also less likely to have access to same-day biopsy services, despite similar availability of most diagnostic services.
Researchers developed a robust AI model that automates segmenting of MRI images, reducing radiologist workload and improving consistency. The TotalSegmentator MRI model achieved high performance on various anatomical structures, with a Dice score of 0.839.
A new study published in The Lancet Digital Health found that AI-supported breast cancer screening increased the detection of early-stage invasive cancers by 24% compared to traditional screening. AI also identified pre-cancerous lesions with a 51% higher rate, without increasing false positives.
A study by Bonn researchers has shown that local Large Language Models (LLMs) can structure radiological findings in a privacy-safe manner, without transferring data to external servers. The open-source LLMs performed equally well as commercial models with advantages of being developed locally.
Mayo Clinic is developing foundation models with Microsoft Research and Cerebras Systems to personalize patient care, accelerating diagnostic time and improving accuracy. These multimodal models integrate radiology images and genomic sequencing data to transform clinical diagnosis and treatment.
A recent study published in Nature Medicine found that AI significantly improves breast cancer detection rates, detecting an additional cancer case per 1,000 women screened. The results show improved efficiency without increasing false positives or unnecessary follow-ups.
A deep learning model developed by researchers at San Diego State University can accurately diagnose chronic obstructive pulmonary disease (COPD) using a single inhalation lung CT scan. The study found that the model performed similarly to traditional two-phase CT measurements, with added clinical data improving accuracy.
Fogarty's research aims to monitor language function and recovery in post-stroke patients using DOT. She hopes to establish the feasibility of brain-computer interfaces to restore inter-personal communication for post-stroke patients.
The study found a significant increase in late-stage breast cancer diagnoses among U.S. women, with annual percentage increases of up to 2.9% between 2004 and 2021. The researchers attribute the trend to factors such as lack of national screening programs, inconsistent guidelines, and increasing obesity rates.
A new research paper published in Oncotarget introduces an innovative AI tool combining CT scans and body composition data to predict severe liver problems in primary sclerosing cholangitis (PSC) patients. The model achieved impressive results, correctly identifying at-risk patients with 97% accuracy.
Teletrix's VIZRAD AR platform simulates ionizing radiation behavior using augmented reality technology, providing a safe and immersive way to learn about radiation without exposure. The platform offers improved training capabilities for workers in radiation-controlled areas.
Researchers have developed a new coronary risk score specifically for women, accurately predicting and categorizing the risk of major adverse cardiovascular events. The findings suggest that this novel approach can help identify high-risk women earlier, reducing the risk of heart attacks and sudden cardiac death.
A new study found significantly higher injury rates among transgender women compared to cisgender women, with increased risk of head, facial, and chest injuries. The findings emphasize the need for timely IPV screening and support for vulnerable populations.
Researchers at the University of Bonn are developing an AI foundation model to improve radiology and detect Parkinson's disease earlier. The 'Re-Start PD' project aims to identify the condition before symptoms appear, with potential applications in diagnosis, treatment, and prevention.
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 study found that skeletal muscle loss, particularly in the temporalis muscle, is associated with an increased risk of developing Alzheimer's disease dementia. Participants with smaller temporalis muscle size were more than twice as likely to develop dementia over a median follow-up period of 5.8 years.
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.
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.
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.
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.