Articles tagged with Medical Imaging
Researchers at NTNU have developed a way to reconstruct 3D models of the colon using single images taken by capsule endoscopy cameras. These models can aid in detecting abnormalities and signs of disease, enabling specialists to make diagnoses faster.
Researchers developed metamaterials-enhanced MRI technology using coaxial cables to boost signal-to-noise ratio. The innovative coils address patient discomfort and cost by providing adaptable, form-fitting designs for various anatomical sites.
Researchers are working to make publicly funded data collection in computed tomography widely available and understandable by diverse communities. The Non-Clinical Tomography Users Research Network aims to improve data handling across scientific disciplines and standardize data acquisition, handling, and sharing.
A novel technique using radiomics reveals imperceptible patterns in medical images to predict the risk of meniscus tears. The study correctly classified cases with a sensitivity of 70.6% and specificity of 95.0%, providing clinicians with a valuable tool for proactive knee health management.
Researchers at Pohang University of Science & Technology have developed the first wide field-of-hearing metalens, overcoming traditional acoustic lens limitations. The device achieves up to 140 degrees of field-of-hearing without sound distortion, enabling new applications in acoustic imaging and high-sensitivity sensing.
A research team at Mass General Brigham developed a foundation model that identified patterns predicting anatomical site, malignancy, and prognosis from radiological images. The approach remained powerful even with limited data, outperforming existing methods in specialized tasks.
Scientists at Harvard John A. Paulson School of Engineering and Applied Sciences have developed a compact, single-shot polarization imaging system that can provide a complete picture of polarization. The system uses two thin metasurfaces to capture the most complete polarization response of an object in real-time.
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.
DZNE researchers aim to measure intricate brain structures with artificial intelligence, using 3D brain scans and data from the Human Connectome Project. The goal is to better understand disease processes and normal brain changes over time.
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 suggests that tailored treatment can predict the response of breast cancer patients to chemotherapy, allowing for safe omission of extensive lymph node removal. The MARI protocol showed a 95% overall survival rate and 89% disease-free survival rate in patients who achieved pathological complete response.
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 novel transparent ultrasonic transducer (TUT) developed by POSTECH researchers offers exceptional optical transparency and maintains acoustic performance, surpassing conventional limitations. This breakthrough enables high-depth-to-resolution ratios for ultrasound imaging, with applications in various medical devices and fields.
New research reveals that radioguided surgery can effectively detect and remove metastatic pelvic lymph nodes in newly diagnosed prostate cancer patients. The procedure targets the prostate-specific membrane antigen, improving nodal staging to guide treatment recommendations.
Researchers warn that AI tools used in radiology contribute to greenhouse gas emissions through energy consumption and data storage. To minimize these impacts, experts recommend sharing resources, selecting sustainable hardware, and using data compression techniques.
Researchers have confirmed a new method for precise body composition analysis using 3D surface scans and dual-energy X-ray absorptiometry. This approach yields accurate measurements of fat, lean muscle, and bone, surpassing commercial software in clinical settings.
USC researchers have designed nanoparticles that can target and highlight cancer cells in lymph nodes, allowing for earlier detection of metastasis. The particles work by hitchhiking on immune cells to reach the lymph nodes, where they can amplify the signal detected by MRI scans.
Researchers found that bullied adolescents have lower levels of glutamate in the brain's anterior cingulate cortex region, which regulates emotions and decision-making. This imbalance is linked to higher levels of subclinical psychotic experiences in early adolescence.
The American Dental Association has released updated recommendations to enhance radiography safety in dentistry, emphasizing the appropriate and justified use of dental X-rays. The guidelines also advise against using lead abdominal aprons or thyroid collars, citing evidence that modern digital X-ray equipment and beam restrictions can...
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.
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 have developed a new technique that provides a previously unattainable view of the mechanical properties inside the cell nucleus. The study reveals the peculiar dynamic structural features in living cells, which appear to be crucial for cell function.
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.
The review discusses the optical aspects of QPAT, including mathematical models for light propagation and interaction with biological tissues. The authors outline two approaches to estimating chromophore concentrations from absorbed optical energy density data, highlighting the challenges associated with practical implementation, such ...
A new technology uses meta-optical devices to perform thermal imaging, providing richer information about imaged objects. The approach can be used for various applications such as autonomous navigation, material identification, security, and medical imaging.
Ashok Veeraraghavan, a Rice University professor, has won the Edith and Peter O'Donnell Award in Engineering from the Texas Academy of Medicine, Engineering, Science and Technology. His research focuses on making invisible objects visible through imaging technology that tackles challenges beyond current technologies.
Researchers at Linköping University have developed an aerogel material that can tune the transmission of terahertz signals between 13% and 91%, enabling various applications. The material's absorption property can be adjusted through a redox reaction, making it suitable for long-range signals from space or radar systems.
Researchers developed a method to measure microvascular changes in the skin using AI and optoacoustic imaging technology, enabling non-invasive assessment of diabetes severity. The study identified 32 significant changes in blood vessels, which can be used to monitor disease progression.
A study published in JAMA Oncology found that regular screening is not enough to prevent advanced breast cancer diagnoses, particularly among women of color who are overweight or obese. The researchers recommend primary prevention strategies to reduce the number of advanced cancer diagnoses.
Novel Dice loss functions, t-vMF Dice loss and Adaptive t-vMF Dice loss, have been developed to improve image segmentation accuracy in medical images. These new functions outperform conventional formulations and show great potential for critical fields like medical imaging and diagnosis.
A new decision rule has been developed to identify patients who can safely forgo a head CT scan after falling, reducing unnecessary imaging and costs. The Falls Decision Rule can be applied to all older adults who have fallen, regardless of sustaining a head injury or recalling the fall.
Outdated medical tools cause delayed recognition and management of adverse reactions in patients of color, leading to significant distress. A more inclusive approach is urgently needed to address structural racism in radiotherapy treatment and education.
Researchers from The University of Texas at Austin have demonstrated a compact particle accelerator that produces an electron beam with an energy of 10 billion electron volts (10 GeV) in a chamber less than 20 meters long. The breakthrough uses nanoparticles to boost the energy delivered to electrons, enabling new applications in semic...
The American Gastroenterological Association released a new guideline recommending the use of biomarkers to help manage Crohn’s disease. Biomarkers such as C-reactive protein and fecal calprotectin can measure levels of inflammation, helping doctors assess disease activity and guide treatment decisions.
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.
Researchers developed an AI-based method to estimate BMD from plain X-ray images using a hierarchical learning framework. The approach showed high performance and reliability in estimating BMD, with correlation coefficients of 0.88 and 0.92 compared to DXA and QCT.
Researchers found that humans can make abstract choices without linking them to motor actions, and the brain processes these choices separately. This study provides new insights into how our brains process decisions related to perception and action.
Researchers at Johns Hopkins University have developed a new algorithm to filter out unwanted signals in medical images of darker skin tones, producing significantly sharper images. This breakthrough aims to mitigate bias in imaging technologies and improve surgical navigation and medical diagnostics for patients with diverse skin tones.
A new method was developed to predict organ motion in cancer patients undergoing radiation therapy, utilizing cross-sectional images from three orientations. The results showed improved accuracy when analyzing all three directions, with errors reduced to as low as 2.13mm.
A recent study published in Radiology: Artificial Intelligence found significant racial and sex-related biases in an AI chest X-ray foundation model, affecting its performance across patient subgroups. The researchers highlighted the need for comprehensive bias analysis to ensure diversity and representativeness in dataset collection.
A study found that ultrasound scans performed by emergency physicians can nearly halve the time patients spend in emergency departments. This technology, known as point-of-care ultrasound (POCUS), enables faster diagnoses and treatment for suspected deep vein thrombosis (DVT).
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.
Researchers developed a new imaging technique using Bessel beam two-photon microscopy to detect stalling in brain capillaries, which can indicate acute neurological issues. The approach generates clear images of all capillaries every two seconds, providing better temporal resolution and enabling the detection of short stalling events.
A team of Chinese and UK researchers has identified superoxide dismutase 1 (SOD1) as a potential target for reversing drug resistance in ovarian cancer. By using nanoparticles to deliver siRNA that reduces SOD1 levels, the study showed reduced growth and decreased resistance to cisplatin in female mice.
A new imaging technique called quantitative photoacoustic tomography (QPAT) has the potential to provide high-quality images of cancerous tissues. By combining ultrasound and optical tomography, QPAT can help doctors make more accurate diagnoses in shorter time frames.
Researchers developed an AI model that estimates age from chest X-rays and found a correlation between age discrepancy and chronic diseases like hypertension and COPD. The model was validated using data from multiple institutions, showing strong results.
Researchers demonstrate the potential of optical imaging for safely measuring vocal fold elasticity and pliability. The study found good agreement between Brillouin microspectroscopy results and conventional elasticity measurements.
Researchers developed an innovative optical tool, the Schistoscope, to capture microscopy images of urine samples for efficient detection of Schistosoma haematobium eggs. A two-stage diagnostic framework using deep learning accurately identified and counted eggs in field settings with high sensitivity, specificity, and precision.
A new approach boosts light absorption in thin silicon photodetectors with photon-trapping structures, increasing the absorption efficiency over a wide band in the NIR spectrum. The findings demonstrate a promising strategy to enhance the performance of Si-based photodetectors for emerging photonics applications.
Researchers at Monash University developed a co-training AI algorithm that can effectively mimic human oversight in medical imaging. The algorithm achieved an average improvement of 3% compared to state-of-the-art approaches using limited annotated data, enabling AI models to make more informed decisions and uncover accurate diagnoses.
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.
A new geometric deep learning model called GFCN has been developed to detect stroke lesions in brain imaging scans. The model leverages rich geometric information to segment brain tissue and achieves higher segmentation performance than other neural network architectures.
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 introduce a novel class of X-ray scintillators based on monodisperse copper-iodine clusters, demonstrating remarkable sensitivity and stability. The copper-iodide cluster-based microcubes exhibit excellent scintillation performance and exceptional water resistance, making them suitable for large-area flexible X-ray detectors.
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 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.
Engineers at Rice University and the University of Maryland developed NeuWS, a technology that can undo light scattering effects, enabling full-motion video through various media. The technology measures wavefronts to rapidly decipher phase information, overcoming the 'holy grail problem' in optical imaging.
Researchers created a new method, RESORT, to image and analyze living systems in unprecedented detail. The technique combines benefits of super-resolution fluorescence and vibrational imaging, allowing for high spatial resolution and analysis of complex interactions.
Researchers have developed a new manufacturing pipeline to simplify and advance high-value manufacturing of tissue-compatible organs, reducing costs and increasing efficiency. This breakthrough aims to address the dire need for artificially engineered organs and tissue grafts, potentially saving thousands of lives in the UK.
Researchers from UT Austin created a new composite material that efficiently converts low energy light to higher energy, with applications in bioimaging, solar panels, and night vision goggles. The breakthrough could reduce the size of solar panels by 30% and enable systems for autonomous vehicles and fog detection.