Articles tagged with Diagnostic Imaging
A new study found that A&E patients with visible blood in their urine who receive a scan within 48 hours are 2.5% less likely to die within three months. The study also showed that patients with cancer are diagnosed significantly faster when they receive prompt investigation.
Recent advances in photonic nanomaterials and healthcare devices have led to the development of wearable and implantable medical devices. These devices utilize light for precise manipulation of cells and tissues, offering new possibilities for early disease detection, light-based therapies, and personalized precision medicine.
A new imaging test, PSMA PET/CT scan, has been shown to safely reduce the number of biopsies needed for suspected prostate cancer, with no harm to patients. The PRIMARY2 trial found that the scan could identify low-risk patients who did not need a biopsy, while targeting suspicious areas for those who did.
Research presented at EAU26 confirms that prostate cancer screening leads to a reduction in mortality, but also detects cancers that would otherwise remain undetected. The study's findings suggest that using MRI in prostate cancer screening can reduce overdiagnosis and unnecessary biopsies.
Artificial intelligence can complement human expertise to improve radiology workflows and patient care. The JACR Focus Issue on AI in Workflow Optimization explores the integration of AI technology to enhance efficiency and deliver better care.
The study reveals marked variation in radiation dose to patients from diagnostic testing, particularly affecting low- and middle-income countries. Standardized protocols and updated equipment are urgently needed to reduce global radiation exposure and improve the quality of CAD diagnosis.
A new study highlights the clinical relevance of brain functional connectome uniqueness in identifying biomarkers for major depressive disorder. Researchers found that patients with MDD displayed reduced FC uniqueness, especially in frontoparietal and sensorimotor networks.
A new JACR focus issue explores alternative approaches to resident education, radiology residency applications, and investing in pre-clinical medical education. The study aims to spark meaningful dialogue around how radiology education is valued, supported, and delivered amidst economic challenges.
Steatotic liver disease can be precisely assessed using three-dimensional ultrafast vascular ultrasound. The technology visualizes subtle microvascular changes, enabling real-time monitoring of disease progression and therapeutic response.
A study published in Biological Psychiatry identifies a distinct immuno-inflammatory biomarker in the brain linked to immune system dysfunction and poorer response to standard treatments. The findings provide potential value for clinical prediction and precision therapies.
A new study led by Concordia researchers found that individuals with coronary artery disease (CAD) have widespread structural changes in their white matter compared to healthy controls. The changes were particularly noticeable in regions critical for cognitive and motor functions.
Researchers developed a revolutionary AI platform to assess the fairness and accuracy of commercial algorithms in detecting diabetic eye disease. The platform was tested on 1.2 million images from diverse ethnic groups, achieving high accuracy rates compared to human grading.
A study at Technical University of Munich found that AI-simplified CT reports reduced reading time to two minutes and improved patient comprehension. Patients rated the simplified texts as more helpful and informative, with 82% finding them easier to read and understand.
A new model called DAC enables medical image segmentation with limited labelled data, achieving consistent generalization across unseen domains. The approach uses feature-level supervision and asymmetric co-training to reduce errors, especially in low-contrast structures.
A deep learning system developed by Incheon National University integrates images and clinical details to improve early skin cancer diagnosis. The model achieved 94.5% accuracy, outperforming popular image-only models.
Artificial intelligence is being used to improve diagnostic accuracy in molecular pathology, with studies showing 93% accuracy for cancer diagnosis. Researchers are also using AI to analyze chromosomal changes in blood cancer patients and provide personalized treatment plans.
The new Molecular Imaging and Theranostics Centre at NUH and NUS Medicine enables faster, safer and more precise diagnoses, while researchers can observe real-time tracer movement throughout the body. This opens possibilities for validating next-generation diagnostics and therapies, advancing theranostics.
Researchers at the University of Arizona are developing a new optical technology that can image deep into biological tissues without invasive procedures. This approach aims to overcome current challenges in skin cancer imaging, allowing for earlier detection, precise evaluation, and real-time monitoring of treatment response.
A new study has uncovered 42 genetic locations associated with hypertrophy of the left ventricle, a major risk factor for sudden death. The research, conducted using three-dimensional MRI images and genome-wide analysis, could lead to earlier identification of individuals at greater risk.
A new study shows that a multi-step effort to reduce unneeded pre-operative testing, using a tailored program for each hospital, can lead to significant reductions in wasteful tests. The findings have already spurred an expansion of the program to 16 more Michigan hospitals.
The PRISM trial will examine the impact of AI on mammogram accuracy and patient experience, with hundreds of thousands of mammograms analyzed. The study aims to understand whether AI enhances cancer detection by radiologists or leads to more false alarms.
Researchers developed an AI model that diagnoses achalasia using plain chest X-rays, outperforming physicians' reviews. The diagnostic performance of the AI model demonstrated higher sensitivity and specificity, enabling early diagnosis and potentially improving treatment efficacy.
A new machine learning model predicts heart disease risk in women by analyzing mammograms, offering a 'two-for-one' screening approach that combines breast and cardiovascular screenings. The model performs comparable accuracy to traditional risk calculators without requiring extensive clinical data.
Researchers have demonstrated a portable, noninvasive technology that can detect metabolic changes linked to Alzheimer's disease by measuring cytochrome c oxidase activity. The study found that including oxCCO measures improved the ability of the brain-monitoring tool to capture clinically relevant brain changes.
The International Osteoporosis Foundation identifies critical global barriers to osteoporosis care, including limited DXA scanning and outdated treatment criteria. The IOF advocates for a paradigm shift in bone health management worldwide, recognizing high fracture risk as a valid criterion for treatment and reimbursement.
Scientists at Saarland University have developed a pioneering robot-assisted procedure for joint replacement surgery, eliminating the need for bone pins and external infrared cameras. The new approach uses the robot's built-in sensors to precisely scan the surgical field and create an accurate 3D model.
A new diagnostic method confirms sepsis infections in as little as two hours, cutting critical time for treatment. The technique uses centrifugation and artificial intelligence to detect bacteria in blood samples, enabling prompt antibiotic treatment and reducing survival rates by 8% per hour of delayed treatment.
A new diagnostic schema for COPD improves patient identification by incorporating CT lung imaging and respiratory symptoms. This approach identified additional individuals at risk of poor respiratory outcomes and excluded those with airflow obstruction without respiratory symptoms.
A new AI tool can learn to read medical images with far less data, cutting down the amount of required data by up to 20 times. The tool improves upon medical image segmentation, a labor-intensive task often performed by experts, and boosts model performance in settings with limited annotated data.
Researchers develop compact, noninvasive imaging system combining LC-OCT and Raman microspectroscopy to examine skin cancer structures and chemical composition. The AI model achieves high classification accuracy for basal cell carcinoma and other types, offering new insights into cancer development and behavior.
The 250,000-square-foot facility provides greater availability to high-quality integrated care, addressing the region's increasing demand for innovative diagnostic and treatment services. The pavilion brings together a broad range of nationally ranked clinical programs, including cancer clinics, neurology, and surgery.
A newly developed low-cost, handheld intraoral device combines optical diagnostics and image-guided photodynamic therapy to detect and treat early-stage oral cancer. The device shows promising accuracy and effectiveness in detecting PpIX fluorescence and monitoring treatment in real-time.
A recent survey by the Annenberg Public Policy Center found that nearly half of Americans believe women should begin regular mammograms at age 40, while younger women are less certain. Women aged 18-29 were most likely to choose an incorrect age, with 27% selecting 30 years old as the start age.
Researchers at the University of Illinois Urbana-Champaign have developed a new non-invasive MRI approach that visualizes brain metabolism and detects metabolic alterations associated with brain diseases. The technique uses magnetic resonance spectroscopic imaging to provide insights into brain function and disease processes.
A research team at Osaka Metropolitan University has developed an AI model that can detect fatty liver disease from chest X-ray images with an accuracy rate of 0.82-0.83. This breakthrough has the potential to improve early detection and treatment of the disease, which affects one in four people worldwide.
A Mount Sinai study found a post-pandemic increase in head and shoulder injuries among female and male youth hockey players, with higher rates of hospitalizations and fractures. The research highlights the importance of safer equipment standards, injury prevention strategies, and education to protect young athletes.
A landmark review highlights how diabetes alters bone microarchitecture and increases fracture risk in people with type 2 diabetes, despite normal or elevated bone mineral density. The authors advocate for updated diagnostic tools, including a revised TBS algorithm, to more accurately reflect bone quality in individuals with central ob...
A team of scientists from Colorado State University and the University of São Paulo have developed a seismological solution to improve the resolution of ultrasound images for lung monitoring. This breakthrough could lead to improved critical care for patients, including continuous lung monitoring at the bedside. The technique uses seis...
A collaborative research team led by KAIST has developed a groundbreaking technology that uses advanced optical techniques combined with an AI-based deep learning algorithm to create realistic 3D images of cancer tissue. This breakthrough paves the way for next-generation non-invasive pathological diagnosis.
A novel screening approach called VEST uses virtual echocardiography to identify patients at high risk of pulmonary arterial hypertension, a life-threatening form of heart failure. The tool has been shown to generate accurate PAH risk scores without manual calculations and can guide timely referrals for expert care.
Researchers found that vision-language models fail to understand negation words like 'no' and 'not', which can cause incorrect diagnoses. This limitation affects multiple choice question answering with negated captions, with accuracy dropping below random chance.
Researchers at TUM developed an AI-powered algorithm to predict kidney damage in prostate cancer patients undergoing lutetium-177 PSMA therapy. Early detection could enable personalized treatment adjustments to prevent organ damage.
A recent meta-analysis found that generative AI's diagnostic accuracy is lower than that of specialist doctors, with an average accuracy of 52.1%. The study suggests that while generative AI has the potential to support non-specialist doctors in diagnostics, further research is needed to improve its capabilities.
Swedish researchers have identified 20 genetic variants associated with an increased risk of atherosclerosis, a leading cause of cardiovascular disease. The study used advanced imaging techniques to examine millions of genetic variants, providing new insights into the disease process and potential ways to prevent it.
Researchers developed a deep learning algorithm to denoise ultra-low dose CT scans, improving image quality and accuracy. The study found that this approach can diagnose pneumonia in immunocompromised patients using only 2% of the radiation dose of standard CT scans.
A rare case of adenomyosis in an 81-year-old postmenopausal woman closely resembles invasive endometrial cancer, emphasizing the challenges of diagnosing this condition. Further studies are needed to refine diagnostic protocols and determine risk factors for postmenopausal patients.
A study found significant racial disparities in who received same-day diagnostic services and biopsies after abnormal mammogram findings, with Black women being less likely to receive same-day biopsies. Rural-resident patients were more likely to receive same-day diagnostic services than urban residents.
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 from Japan develop a non-contact, millimeter-wave sensor system to monitor respiratory motion during diagnostic X-ray and CT imaging. The system has been validated through extensive testing with healthy volunteers and shows promise for improving diagnostic accuracy and treatment outcomes.
A recent neuroscientific study by UPF and Oxford reveals that long-range connections between brain regions are scarce but play a fundamental role in explaining brain dynamics. These rare connections serve to transmit information more quickly and directly, yielding optimal and efficient information processing.
A new experimental technique has developed a molecular flashlight to monitor molecular changes in the brain caused by cancer and other neurological pathologies. The technique uses vibrational spectroscopy to illuminate nerve tissue, allowing for the analysis of molecular changes caused by tumours or injuries.
A 10-minute brain scan can predict the effectiveness of a risky spinal surgery to alleviate intractable pain. The study found that patients who responded better to spinal cord stimulation therapy had weaker connections between certain brain networks.
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.
Researchers developed a new PET scan that accurately visualizes benign tumors in the pancreas, known as insulinomas, which can cause low blood sugar levels. The Exendin-PET scan uses a substance found in lizard saliva to detect these tumors, improving diagnosis and treatment outcomes.
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.
Researchers have introduced DSFN to improve the speed and accuracy of diagnoses of retinal disorders. This AI-powered medical imaging technique combines retina images with vascular distribution information to accurately locate the fovea in complex clinical scenarios, enabling doctors to detect early signs of ocular diseases.
A new AI model generates detailed images of cancer tissue that imitate what its staining would look like, reducing the need for resource-intensive lab analyses. The VirtualMultiplexer uses contrastive unpaired translation to create accurate virtual pictures of diagnostic tissue colorations.
Using tartrazine, researchers create transparency in tissues by matching refractive indices, revealing hidden organs and structures. This technique has implications for diagnosing injuries, digestive disorders, and cancers.
F18-FDG PET-CT scans showed significantly higher levels of radioactively-labeled glucose in kidneys for patients with ICI-AKI compared to those with AKI from non-ICI causes. This suggests a potential non-invasive testing option for differentiating cause of AKI as being ICI-related vs. non-ICI-related.
A multi-university research team led by University of Virginia engineering professor Gustavo K. Rohde has developed a system that can accurately spot genetic markers of autism in brain images. The system uses generative computer modeling technique called transport-based morphometry, which reveals brain structure patterns that predict v...