All articles tagged: Radiology & Imaging

When making diagnostic decisions, radiologists and other physicians may rely too much on artificial intelligence (AI) when it points out a specific area of interest in an X-ray, according to a study published today in Radiology.

A paper titled "Deep Learning and Transfer Learning for Brain Tumor Detection and Classification" published in Biology Methods and Protocols shows that scientists can train artificial intelligence (AI) models to distinguish brain tumors from healthy tissue. AI models can already find brain tumors in MRI images almost as well as a human radiologist.

Artificial intelligence is making impressive strides in its ability to read medical images. In a recent test in Britain's National Health Service, an AI tool looked at the mammograms of over 10,000 women and correctly identified which patients were found to have cancer. The AI also caught 11 cases doctors had missed. But systemic diseases, such as lupus and diabetes, present a greater challenge fo

A newly developed technique uses a Bessel beam to extend the focus of optical coherence microscopy, allowing for a detailed imaging of large sections of the brain. Unlike traditional methods that either look at tiny volumes or lose detail over larger areas, this approach provides a comprehensive view of the vascular network.

The human body contains a vast, complex, and interconnected web of organic tunnels and passageways that weave their way through the cardiovascular, respiratory, and digestive systems. For physicians, reaching into this maze of arteries, bronchial tubes, and gastrointestinal chambers to view and treat diseased or damaged tissue can be—to put it mildly—challenging.

X-rays are a common component of diagnostic testing and industrial monitoring, used for everything from monitoring your teeth to scanning your suitcase at the airport. But the high-energy rays also produce ionizing radiation, which can be dangerous after prolonged or excessive exposures.

From 2008 to 2020, there was an increase in the percentage of U.S. radiologists involved in resident teaching, but teaching radiologists' total workload involving trainees has decreased, according to a study published online Oct. 22 in the Journal of the American College of Radiology.

Breast cancer screening helps people live longer. Screening can catch cancer early, either in the precancerous stage or when it's localized to a very small part of the body. When we can catch it early and decrease the risk of it coming back in the future, patients not only live longer, but they also may live with fewer symptoms and breast cancer or cancer treatment-related concerns.

A picture may be worth a thousand words, but they both have a lot of work to do to catch up to BiomedGPT. A Lehigh University research team has now collaborated with Massachusetts General Hospital in an effort to transform medical text and images into faster disease diagnosis, enhanced medical reporting, improved drug discovery, and more.

You're not feeling well. You've had a pounding headache all week, dizzy spells and have vomited up your past few meals.

A research team has developed a chest X-ray vision-language foundation model, MaCo, reducing the dependency on annotations while improving both clinical efficiency and diagnostic accuracy. The study was published in Nature Communications.

Prostate cancer is the second most common cancer in men, and almost 300,000 individuals are diagnosed with it each year in the U.S. To develop a consistent method of estimating prostate cancer size, which can help clinicians more accurately make informed treatment decisions, Mass General Brigham researchers trained and validated an AI model based on MRI scans from more than 700 prostate cancer pat

Knowing a patient's symptoms helps radiologists in lumbar spine MRI interpretation and diagnosis, according to a study published in Radiology.

Researchers from Peking University Third Hospital have developed a novel collaborative framework that integrates various semi-supervised learning techniques to enhance MRI segmentation using unlabeled data. This new approach, published in Health Data Science, leverages advanced deep learning models to significantly improve segmentation accuracy, even when labeled data is scarce.

Already used in many areas of medicine, AI has tremendous potential when it comes to helping doctors diagnose diseases with the help of imaging data. However, AI models have to be trained with large numbers of examples, which are generally available in sufficient quantities only for common diseases.

Compared with conventional colonoscopy, computer-aided detection (CADe) system-enhanced colonoscopies have an increased rate of adenomas per colonoscopy and higher adenoma detection rates, according to a review published online Oct. 22 in the Annals of Internal Medicine.

When you order a pizza, an online tracker can provide real-time data that lets you know when your pizza enters the oven, is boxed and is then on the way to your house.

A new PET scan reliably detects benign tumors in the pancreas, according to research led by Radboud University Medical Center. Current scans often fail to detect these insulinomas, even though they cause symptoms due to low blood sugar levels. Once the tumor is found, surgery is possible.

Having close family and friends who care about their health makes women more likely to get regularly screened for cancer, a new study has found.

When human radiologists examine scans, they peer through the lens of decades of training. Extending from college to medical school to residency, the road that concludes in a physician interpreting, say, an X-ray, includes thousands upon thousands of hours of education, both academic and practical, from studying for licensing exams to spending years as a resident.

Organoids, which are 3D miniature organs that mimic the structure and function of human organs, play an essential role in disease research and drug development. A Korean research team has overcome the limitations of existing imaging technologies, succeeding in the real-time, high-resolution observation of living organoids.

At the intersection of AI and medical science, there is growing interest in using machine learning to enhance imaging data captured by magnetic resonance imaging (MRI) technology. Recent studies show that ultra-high-field MRI at 7 Tesla (7T) could have far greater resolution and clinical advantages over high-field MRI at 3T in delineating anatomical structures that are important for identifying an

UCLA researchers have developed a deep-learning framework that teaches itself quickly to automatically analyze and diagnose MRIs and other 3D medical images—with accuracy matching that of medical specialists in a fraction of the time. An article describing the work and the system's capabilities is published in Nature Biomedical Engineering.

The lymphatic system, the body's second largest circulatory network, plays a crucial role in maintaining fluid balance and immune function. Despite its importance, studying lymphatic vessel function has been challenging due to limited imaging techniques.

Using smartly trained neural networks, researchers at TU Graz have succeeded in generating precise real-time images of the beating heart from just a few MRI measurement data. Other MRI applications can also be accelerated using this procedure. The study is published in Magnetic Resonance in Medicine.

Imagine being able to see right through your skin to watch your muscles or organs in action. It sounds like science fiction, but a group of scientists at Stanford University were recently able to make the skin of live mice appear transparent—at least under certain light conditions.

A new hand-held scanner developed by UCL researchers can generate highly detailed 3D photoacoustic images in just seconds, paving the way for their use in a clinical setting for the first time and offering the potential for earlier disease diagnosis.

Obtaining accurate clinical measurements is essential for diagnosing and treating a wide range of health conditions. Regrettably, the impact of skin type and pigmentation is not equally considered in the design and calibration of non-invasive oxygen-monitoring medical technology.

A lumbar puncture, also known as a spinal tap, is a common clinical procedure that uses a hollow needle to penetrate the spinal canal and access the cerebrospinal fluid that surrounds the brain and spinal cord. This procedure is used to diagnose serious conditions of the nervous system such as meningitis or encephalitis or to inject anesthetic medicines or chemotherapy.

The Food and Drug Administration issued a rule in March of 2023 that requires health care professionals to notify people if they have dense breasts. Studies have shown that dense breast tissue can make it more difficult to detect breast cancer early.

A novel artificial intelligence approach can generate 3D brain MRI images using semantic segmentation masks, offering a breakthrough in medical image synthesis and privacy protection.

A technology most often used for medical imaging is being repurposed as a new tool for restoring blood flow in tissue damaged from disease, injury, and reconstructive surgery. Biomedical engineers at the University of Rochester are leveraging ultrasound waves to organize endothelial cells—the building blocks of blood vessels—into patterns that can promote the growth of new vessel networks within d

To the untrained eye, a medical image like an MRI or X-ray appears to be a murky collection of black-and-white blobs. It can be a struggle to decipher where one structure (like a tumor) ends and another begins.

Researchers at Ben-Gurion University of the Negev have developed a computational method that enables them to "reverse engineer" the AI's "decision" by partitioning medical images into components with distinct clinical interpretation that are important for the AI. Understanding the decision-making mechanism of AI models is key for deciphering biological processes and medical decisions.

The large language model GPT-4 with vision (GPT-4V) has high accuracy for text-only radiology questions, but much lower accuracy for image-based questions, according to a study published online Sept. 3 in Radiology.

In a new study, researchers made the skin on the skulls and abdomens of live mice transparent by applying to the areas a mixture of water and a common yellow food coloring called tartrazine.

A collaborative research team led by POSTECH Professor Chulhong Kim and Professor Chan Kwon Jung of Seoul St. Mary's Hospital, Catholic University of Korea, has developed an artificial intelligence (AI) system to analyze the label-free photoacoustic histological images of human liver cancer tissues. Their research was recently published in Light: Science & Applications.

Researchers evaluating the performance of ChatGPT-4 Vision found that the model performed well on text-based radiology exam questions but struggled to answer image-related questions accurately. The study's results were published in Radiology.

The use of artificial intelligence (AI) systems shows promise in medicine, where they can be used to detect diseases earlier, improve treatments, and ease staff workloads. But their performance depends on how well the AI is trained.

Medical imaging technology—such as MRI, ultrasound and X-ray—is gaining in power and precision, especially in the wake of recent breakthroughs in artificial intelligence. Several EPFL research groups are contributing to this progress and actively shaping the future in this area.

While the use of artificial intelligence for medical diagnosis is growing, new research by the University of Adelaide has found there are still major hurdles to cover when it is compared to a clinician.

In histopathology, where tissues are studied under the microscope to understand and diagnose diseases, stains represent a quintessential tool. Simply put, stains are carefully selected or crafted chemicals that adhere to specific cellular components. When viewed under a microscope, they help the user distinguish cell structures more easily by altering the observed colors.

Researchers at the University of Eastern Finland have developed a novel artificial intelligence-based algorithm, MV-DEFEAT, to improve mammogram density assessment. This development holds promise for transforming radiological practices by enabling more precise diagnoses. The study is published in IEEE Access.

In radiology, diagnostic imaging requires specialized knowledge to interpret the findings associated with a wide variety of diseases. Fortunately, in recent years, generative AI models, such as Chat Generative Pre-trained Transformer (ChatGPT), have shown potential as diagnostic tools in the medical field, but their accuracy must be evaluated for optimal use in the future.

A new fluorescent imaging probe can for the first time objectively and non-invasively measure loss of smell, clinically known as anosmia. Targeting the olfactory nerve, the new tool has potential to eliminate biopsies used to diagnose certain anosmia conditions and to aid in the development of therapeutic interventions. This research was published in the August issue of The Journal of Nuclear Medi

A commercial artificial intelligence (AI) tool used off-label was effective at excluding pathology and had equal or lower rates of critical misses on chest X-ray than radiologists, according to a study published today in Radiology.

The geographic distance to facilities offering breast magnetic resonance imaging (MRI) or ultrasound is further than to those offering mammography, according to a study published online Aug. 12 in the American Journal of Preventive Medicine.

Recently, a research team developed an unsupervised domain adaptation (UDA) approach, the dual domain distribution disruption with semantics preservation (DDSP) framework, achieving high-precision cross-modality segmentation without dependency on target modality labels. The team was led by Prof. Qin Wenjian from the Shenzhen Institute of Advanced Technology (SIAT) of the Chinese Academy of Science

Researchers have established a suite of parameters that can be determined using ultrasound to quantitatively measure different physical characteristics of the lung. The researchers also demonstrated that the parameters can be used to accurately diagnose and assess the severity of lung diseases in an animal model.

A deep learning model performs at the level of an abdominal radiologist in the detection of clinically significant prostate cancer on MRI, according to a study published today in Radiology. The researchers hope the model can be used as an adjunct to radiologists to improve prostate cancer detection.

A newly developed imaging method, granzyme B PET, can non-invasively detect inflammatory bowel disease (IBD) and provide a real-time picture of active inflammation in the tissue, according to new research published in the July issue of The Journal of Nuclear Medicine. Information garnered from the PET images can be used to monitor IBD treatment efficacy and could potentially guide more personalize

The radiology gender gap is decreasing, but there remains work to be done, according to an editorial published in RadioGraphics.

Artificial Intelligence (AI) tools can play a key role in medical imaging if radiologists trust in their design, deploy them with adequate training and establish clear guidelines regarding clinical accountability, according to a recently published Special Report in Radiology: Artificial Intelligence.

The performance of digital subtraction angiography (DSA)-guided tunneled dialysis catheters (TDC) is superior to that of ultrasound-guided TDC in renal replacement therapy, according to a study published online July 9 in Renal Failure.

Hepatic, or liver, disease affects more than 100 million people in the U.S. About 4.5 million adults (1.8%) have been diagnosed with liver disease, but it is estimated that between 80 and 100 million adults in the U.S. have undiagnosed fatty liver disease in varying stages. Over time, undiagnosed and untreated hepatic diseases can lead to cirrhosis, a severe scarring of the liver that cannot be re

Duke-NUS Quantitative Biology and Medicine Ph.D. candidate Yuan Han has developed a new method to improve the accuracy of AI when it is used to diagnose a collapsed lung, or pneumothorax, by limiting the area of a chest X-ray that AI uses when interpreting the image.

Amy Young says it's "time to humanize" the places where we work.

Using an advanced scanner, researchers from Aarhus University, among others, have developed a technology that can detect the earliest changes in the kidney when scar tissue begins to form. Their study is published in the journal Investigative Radiology.

Children and young people are generally positive about artificial intelligence (AI) and think it should be used in modern health care, finds the first-of-its-kind survey led by UCL and Great Ormond Street Hospital (GOSH). The findings are published in the journal European Radiology.

If there is one medical exam that everyone in the world has taken, it's a chest X-ray. Clinicians can use radiographs to tell if someone has tuberculosis, lung cancer, or other diseases, but they can't use them to tell if the lungs are functioning well.

Magnetic resonance imaging (MRI) has played an important role in modern medical diagnosis, generating petabytes of crucial data annually across health care facilities worldwide. However, the challenges in big data storage, data accessibility, data security, etc., have impeded its potential in further enhancing global health care.

The Korea Research Institute of Standards and Science (KRISS) has developed the world's first reference material to improve the accuracy of body fat measurements conducted through MRI and CT scans. The study is published in the journal Metrologia.

Pathological analysis has been the gold standard of disease detection, especially in tumor diagnosis. Traditional digital pathology relies on high-precision movement of a high numerical aperture (NA) objective to obtain the entire field of view (FOV) for a tissue slide, whose high cost severely hinders its widespread applications.

Biomedical engineer Abhinav Jha, assistant professor in the McKelvey School of Engineering and of radiology at Mallinckrodt Institute of Radiology in the School of Medicine, both at Washington University in St. Louis, has long advocated that AI tools used in medical applications for image processing need to be evaluated based on clinical tasks, not visual appeal.

A newly developed chelator can significantly reduce off-target toxicity in prostate-specific membrane antigen (PSMA) radiopharmaceutical therapy, according to research presented at the 2024 Society of Nuclear Medicine and Molecular Imaging Annual Meeting. By improving the bond between the radioactive metal ion and the PSMA-targeting antibody, the chelator can make PSMA therapy safer and more effec

A new ultra-low dose imaging method used with long axial field-of-view PET scanners can obtain images with a historically low radiopharmaceutical dose—more than 50 times lower than the standard effective dose. Furthermore, the technique can generate quantitative PET images without the need for accompanying CT scans, greatly reducing the amount of radiation exposure to patients. This research was p

The presence of beta-amyloid deposits in the brain significantly limits the effectiveness of cognitive training in healthy older adults, according to new research presented at the 2024 Society of Nuclear Medicine and Molecular Imaging Annual Meeting. Assessed by PET imaging, adults with amyloid deposits were less likely to retain skills and information learned during cognitive training. This intri

The use of hearing aids can help to slow the metabolic decline that takes place in the brains of adults with mild cognitive impairment, according to research presented at the 2024 Society of Nuclear Medicine and Molecular Imaging Annual Meeting. Those who used hearing aids experienced less decline in brain metabolism than those with untreated hearing loss, especially in frontal regions of their co

Living bone is fascinating because of its unique ability to adapt to mechanical stress and regenerate without scarring. During fracture healing, blood vessels and bone cells work closely together to gradually replace the initial cartilaginous wound tissue with ingrowing blood vessels and new bone tissue.

MRI scans are commonly used to diagnose a variety of conditions, anything from liver disease to brain tumors. But, as anyone who has been through one knows, patients must remain completely still to avoid blurring the images and requiring a new scan. A prototype device described in ACS Sensors could change that. The self-powered sensor detects movement and shuts down an MRI scan in real time, impro

To the trained eyes of a doctor, the gray-white shapes and textures of an ultrasound image reveal more than to the layperson. But the tiniest vascular structures have remained elusive—until now. The team from the Department of Medical Engineering at Ruhr University Bochum headed by Professor Georg Schmitz is perfecting ultrasound localization microscopy (ULM).

In a study of nearly 5,000 screening mammograms interpreted by an FDA-approved AI algorithm, patient characteristics such as race and age influenced false positive results. The study's results were published in Radiology.

Researchers have successfully developed the technology that can accurately segment different body organs by effectively learning medical image data used for different purposes in different hospitals, which is expected to greatly contribute to the development of large-scale medical AI models in the future.

For up to 30% of patients who are allergic to medical contrast dye or have a dye restriction because of other health conditions, they might find that it takes longer to get a diagnosis when it comes to life-threatening lung issues such as pulmonary embolism. That's because imaging methods that detect lung problems but don't use contrast dye aren't as accurate and can be more time-consuming to admi

A large international team of micro-engineers, medical technologists, and neurosurgeons, has designed, built and tested a new type of probe that can be used to take pictures from inside arteries in the brain.

New research indicates that various features assessed through imaging tests can reveal an individual's risk of developing meniscus tears, which is one of the most common knee injuries.

How good would an algorithm have to be to take over your job?

A magnetic resonance imaging device built with off-the-shelf parts and paired with AI matched the performance of high-end MRI machines, according to a study published Thursday that could pave the way for greater access to the life-saving tools.

Text messaging women after initial outreach for breast cancer screening increases mammogram completion rates, according to a study published online May 6 in JAMA Internal Medicine.

Four proposed state-of-the art image search engines for automating search and retrieval of digital histopathology slides were found to be of inadequate performance for routine clinical care, new research suggests.

Use of publicly available large language models (LLMs) resulted in changes in breast imaging reports classification that could have a negative effect on patient management, according to a new international study published in the journal Radiology. The study findings underscore the need to regulate these LLMs in scenarios that require high-level medical reasoning, researchers said.

In a 3-stage study, three cohorts were used for diagnostic performance, and prognostic performance evaluation for this fully automated, one-click, on-site CT-FFR technique. The study was led by Professors Long Jiang Zhang and Guang Ming Lu (Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University). The work is published in the journal Science Bulletin.

Researchers at the Beckman Institute for Advanced Science and Technology have developed a new technique to make ultrasound localization microscopy, an emerging diagnostic tool used for high-resolution microvascular imaging, more practical for clinical settings. Their method uses deep learning to advance in the post-processing pipeline of ULM.

In an ischemic stroke, an artery in the brain is blocked by blood clots and the brain cells can no longer be supplied with blood as a result. Doctors must therefore act quickly and unblock the artery with the help of catheters. During the so-called mechanical thrombectomy, a lot of data has to be recorded and then transferred to various registers.

Large language model GPT-4 matched the performance of radiologists in detecting errors in radiology reports, according to research published in Radiology.

In biomedicine, segmentation involves annotating pixels from an important structure in a medical image, like an organ or cell. Artificial intelligence models can help clinicians by highlighting pixels that may show signs of a certain disease or anomaly.

Removing one step from a three-part MRI scan, which could make them quicker, cheaper, and more accessible, had no negative impact on diagnostic accuracy, according to clinical trial results led by UCL and UCLH.

Magnetic resonance-guided focused ultrasound (MRgFUS) is a non-invasive technique for neuroregulation. It offers several advantages, including being non-invasive, eliminating the need for general anesthesia requirements, and enabling real-time target localization and real-time temperature monitoring.

The world's most powerful MRI scanner has delivered its first images of human brains, reaching a new level of precision that is hoped will shed more light on our mysterious minds—and the illnesses that haunt them.

Following Cures Act implementation, the time for patients to access imaging results decreased, while the proportion of patients who accessed their reports before the ordering provider increased, according to a study published online March 27 in the American Journal of Roentgenology.

Magnetic resonance imaging (MRI) is a cornerstone in the landscape of medical diagnostics, celebrated for its non-ionizing and non-invasive nature. With nearly 40% of MRI procedures requiring contrast agents for optimal sensitivity, the quest for safer and more effective alternatives to gadolinium-based agents (GBCAs) has intensified.

An artificial intelligence (AI) breast screening solution called Mia helped doctors find an additional 12% more cancers than in routine practice, as announced today in collaboration with Kheiron Medical Technologies, NHS Grampian, the University of Aberdeen and Microsoft.

Artificial intelligence can spot COVID-19 in lung ultrasound images, much like facial recognition software can spot a face in a crowd, new research shows.

The thorax, the part of the body between the neck and abdomen, provides medical professionals with a valuable window into a patient's respiratory health. By evaluating sound vibrations produced by the airflow induced within the lungs and bronchial tree during normal breathing as well as those produced by the larynx during vocalizations, doctors can identify potential disease-related abnormalities

One of the most touted promises of medical artificial intelligence tools is their ability to augment human clinicians' performance by helping them interpret images such as X-rays and CT scans with greater precision to make more accurate diagnoses.

The 'ultrasound-photoacoustic dual-modal imaging system' combines molecular imaging contrast with ultrasound imaging, and it can visualize molecular and structural information inside the body in real time without any ionizing radiation. This advantage gives it the potential to enhance medical diagnosis by providing diverse physiological and histological information, ensuring greater accuracy and s

Particle detectors like the ones used by physicists at CERN can have wide applications beyond fundamental research. Scientists from the German National Center for Tumor Diseases (NCT), the German Cancer Research Center (DKFZ), and the Heidelberg Ion Beam Therapy Center (HIT) at Heidelberg University Hospital are now testing a new imaging device supplied by the Czech company ADVACAM on its first pa

The use of marijuana and new weight loss pharmaceuticals can have an impact on gastric emptying scans commonly used to evaluate patients with gastrointestinal complaints. With the increased usage of these drugs, physicians need to be more attuned to their side effects to perform gastric emptying scans most effectively, according to research published in the Journal of Nuclear Medicine Technology.

Medical diagnostics expert, doctor's assistant, and cartographer are all fair titles for an artificial intelligence model developed by researchers at the Beckman Institute for Advanced Science and Technology.

A Western University team has harnessed the bright light of the Canadian Light Source at the University of Saskatchewan (USask) to obtain highly detailed images of the structures in the inner ear responsible for transmitting sound signals to the brain. With these images, they've helped pioneer customized programming strategies for hearing implants.

A research team from Tohoku University Graduate School of Medicine undertook a detailed examination of the medical validity of deep learning models using post-mortem imaging for diagnosing drowning. The results reveal an inconsistency between the deep learning models' results and the medical professionals' observations.

As artificial intelligence (AI) is increasingly used in radiology, researchers caution that it's essential to consider the environmental impact of AI tools, according to a focus article published in Radiology.

Autopsy has a central role in shedding light on diseases, helping to uncover the cause of death. Tissue samples from various organs are sampled, stained, and examined under a light microscope to evaluate their histological characteristics. However, chemical staining of autopsy samples often suffers from fixation-related artifacts due to unavoidable delays in tissue preservation.

Osteoporosis can be detected and prevented more effectively thanks to a new diagnostic tool created by a team made up of researchers from the UPF BCN MedTech unit, the companies 3D-Shaper Medical and CETIR Medical Group, and Hospital de la Santa Creu i Sant Pau.

Detecting cancer during its early stages, that is, before it spreads to other parts of the body, almost always leads to better treatment outcomes and lower mortality rates. However, for people without good access to health care, such as those with low resources or who live in rural areas, timely diagnosis is rare. This is in great part due to the lack of simple, quick, and cost-effective diagnosti

The vast majority of positron emission tomography (PET) imaging systems map out how the body uses a radioactive form of glucose for energy. Since many cancers use glucose as a metabolic fuel, they light up on glucose PET scans. However, not all cancers use glucose as fuel, and some normal organs, like the brain and heart, use high amounts of glucose, too, making it difficult to identify some disea

Locating specific, localized distributions of fat and muscle in body composition is intuitively and imprecisely implied by body shape. Now, a team of researchers has discovered that the unique combination of modern deep learning models, specified equipment and three-dimensional body scans has given a quantitative validation on medical imaging that was previously only qualitative.

In the realm of biomedical sciences, the quest for accurate and efficient diagnostic tools is ever-evolving. One such promising innovation making waves is the photoacoustic (PA) technique. In the past decade, PA imaging has emerged as a viable imaging modality demonstrated in many clinical applications with promising outcomes. Unlike traditional methods, PA offers a noninvasive approach to probing

A study conducted by Aston University researchers has demonstrated that the appearance of aging skin looks noticeably different compared to younger skin, when examined under polarized laser light.

A suite of filters that can be applied to medical images to help health care professionals with analysis and diagnosis has been developed by an international team of researchers.

Radiation is a targeted form of cancer treatment used for up to half of all patients with cancer. Most radiation treatments are delivered using focused X-rays. Because X-rays travel through tissues, this can lead to extra exposure to radiation for healthy organs and tissue.

Non-alcoholic fatty liver disease (NAFLD), recently renamed metabolic dysfunction-associated fatty liver disease (MAFLD), is spectrum of diseases that ranges from non-alcoholic fatty liver (NAFL) to non-alcoholic steatohepatitis (NASH).

A novel imaging approach, 68Ga-pentixafor PET/CT, has been shown to accurately identify sub-types of primary aldosteronism (an adrenal gland disorder), outperforming traditional methods for diagnosis. Reported in The Journal of Nuclear Medicine, this detailed imaging technique provides a clearer picture of the adrenal glands, helping doctors decide more confidently whether surgery is the right opt

According to new research in theAmerican Journal of Roentgenology (AJR), the No Surprises Act (NSA)'s independent dispute resolution (IDR) process would be financially unfeasible for a large portion of out-of-network (OON) claims for hospital-based specialties—more so for radiologists than other specialists.

A research team at POSTECH (Pohang University of Science and Technology) has investigated kidney diseases using ultrafast ultrasound that captures 1,000 images in just one second.

Researchers are developing portable ultrasound devices that promise improved treatments for millions of patients.

As I checked in at a Manhattan radiology clinic for my annual mammogram in November, the front desk staffer reviewing my paperwork asked an unexpected question: Would I like to spend $40 for an artificial intelligence analysis of my mammogram? It's not covered by insurance, she added.

A newly developed PET imaging agent has been found to be effective in identifying medullary thyroid cancer (MTC) in preclinical and clinical studies, according to new research. The results of the studies indicate that the PET imaging agent may be a promising theranostic candidate for clinical use.

Steatotic liver disease (SLD), previously known as non-alcoholic fatty liver disease, which includes a range of conditions caused by fat build-up in the liver due to abnormal lipid metabolism, affects about 25% of the population worldwide, making it the most common liver disorder. Often referred to as "silent liver disease," SLD progresses without noticeable symptoms and can lead to more severe co

Magnetic resonance imaging (MRI) offers great opportunities when it comes to diagnosing cancer. However, the scanning procedure is extremely sensitive. One of the many problems occurs if the patient moves ever so slightly during the scan, resulting in a blurry image. This makes it more difficult to accurately determine the size and position of the cancer tumor. However, precision treatment, for ex

Chronic liver diseases such as hepatitis, liver cirrhosis, and hepatocellular carcinoma are a major cause of morbidity and mortality worldwide. Fibrosis—the thickening and scarring of connective tissue—plays a major role in these liver diseases but detection of fibrosis is limited to biopsy, which suffers from limitations including the risk of complications, sampling only a tiny fraction of the li

Researchers have developed an artificial intelligence (AI) system that can rapidly detect COVID-19 from chest X-rays with more than 98% accuracy. The study results have just been published in Scientific Reports.

Research published in Journal of Shanghai Jiao Tong University (Science) has proposed a new method for lung image registration named Dlung. Dlung is an unsupervised few-shot learning-based diffeomorphic lung image registration, which can help construct respiratory motion models based on limited data with both high speed and high accuracy, offering a more efficient method for respiratory motion mod

While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Is AI the solution?

A 57-year-old woman from Wisconsin recently sustained a rather unfortunate injury to her buttock. She had attended the hospital for an MRI (magnetic resonance imaging) scan and had entered the machine with a concealed firearm. The machine's powerful magnet caused the gun to discharge.

Researchers at the Indian Institute of Science (IISc), in collaboration with Aster-CMI Hospital, have developed an AI tool that can identify the median nerve in ultrasound videos and detect carpal tunnel syndrome (CTS). The study was published in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

An international team of researchers from TU Darmstadt, the University of Cambridge, the science and technology company Merck, and the Klinikum rechts der Isar of TU Munich, participated in an international and interdisciplinary collaboration that studied how software systems collect, process, and evaluate task-specific relevant information, thereby supporting the work of humans, in this case, rad

Changes in multiple biochemical and hematological parameters occur up to eight years before diagnosis of Crohn disease and up to three years before diagnosis of ulcerative colitis, according to a study published in the Nov. 21 issue of Cell Reports Medicine.

Detecting a deadly tumor as early as possible is vital to stopping its spread. However, creating the solution doesn't necessarily solve the problem—it needs to be widely adopted by medical professionals. That's why standards like the new IEC 62220-2-1: 2023 Medical electrical equipment—Characteristics of digital X-ray imaging devices are so important.

A new automated machine-learning algorithm has been developed by researchers at Edith Cowan University (ECU). The algorithm accurately assesses abdominal aortic calcification (AAC) during routine bone density testing. AAC is a recognized measure of advanced vascular disease.

Brigham researchers' findings support developing public health interventions that incorporate components of environmental health literacy alongside cancer screening efforts.

Interstitial fluid flow is closely related to drug delivery and distribution, playing a crucial role in their therapeutic effects on tumors. However, few non-invasive measurement methods are available for measuring low-velocity biological fluid flow.

A collaborative study among Warwick, King's College London and several NHS sites has demonstrated how AI can analyze X-rays and diagnose medical issues just as accurately or more accurately than doctors.

Artificial intelligence is finding more and more applications every day. One of the newest is in the biomedical field, using AI to control and drive single-molecule microscopy in ways no human can. The result is a landmark capacity of nanoscale optical imaging deep into tissue—visualizing 3D ultrastructure of the brain circuitry and plaque-forming amyloid beta fibrils in healthy and diseased brain

Annual mammograms are recommended indefinitely for breast cancer survivors in many countries, including the U.S., but a large British study finds that less frequent screening is just as good.

Millions suffer from musculoskeletal injuries every year, and the recovery process can often be long and difficult. Patients typically undergo rehabilitation, slowly rebuilding muscle strength as their injuries heal.

Some diseases and conditions hide inside our bodies: calcified arteries that could cause a heart attack, infections, slight fractures in a bone, or even cancerous tumors. Special imaging, like X-rays, MRIs, or CT scans, makes them visible. But what's revealed in those bluish-tinted black-and-white pictures takes a highly discerning eye to decode.

How do emergency department staff determine whether older adults who have fallen need imaging? A new decision rule will help emergency department physicians determine which older adults need imaging for head injuries, describes new research in the Canadian Medical Association Journal.

Everyone should get quality care, no matter the color of their skin. However, implicit bias, micro-aggressions, and a lack of cultural understanding persist, leading to oppression and unequal treatment in health care.

Youth baseball players are prone to elbow pain and injuries, including repetitive overuse changes and fractures, based on the maturity of their bones, according to a new study being presented today at the annual meeting of the Radiological Society of North America (RSNA).

Breast cancer mortality is significantly reduced when women regularly attend screening mammograms, according to research being presented today at the annual meeting of the Radiological Society of North America (RSNA).

New research being presented this week at the annual meeting of the Radiological Society of North America (RSNA) links soccer heading—where players hit the ball with their head—to a measurable decline in the microstructure and function of the brain over a two-year period.

New research from CSIRO, Australia's national science agency, has identified methods for improving artificial intelligence (AI) diagnosis of heart and lung conditions using X-ray.

MIT researchers have designed a wearable ultrasound monitor, in the form of a patch, that can image organs within the body without the need for an ultrasound operator or application of gel.

X-ray fluoroscopy is frequently used in orthopedic surgery. Despite its imaging capabilities, physicians heavily rely on their experience and knowledge to align the 3D shape of the target area using the 2D X-ray image. If X-ray images captured during surgery could be superimposed onto a pre-surgical 3D model (CT model) obtained from a CT scan, it would alleviate the cognitive load associated with

The number of U.S. Food and Drug Administration (FDA)-approved artificial intelligence (AI) products is expected to increase from 69 in 2022 to 350 in 2035, according to a study published online Oct. 16 in the Journal of the American College of Radiology.

A research team led by Professor Sang-hyun Park of the Department of Robotics and Mechanical Engineering/Artificial Intelligence, Daegu Gyeongbuk Institute of Science and Technology (DGIST), collaborated with a research team from Stanford University in the US to develop federated learning AI technology that enables large-scale model learning without sharing personal information or data.

Medical imaging generates a lot of data, for example during computer tomography. This data is important when it comes to personalized medicine. Artificial intelligence methods, such as machine learning, use this data to learn and help tailor diagnoses and therapies to individual needs in the future. However, such technology is still burdened with uncertainties.

For computation of the skeletal muscle index (SMI), calculated as skeletal muscle area (SMA) at L3 divided by patient height squared, a height scaling power of 1 seems better than 2, according to a study published online Oct. 25 in the American Journal of Roentgenology.

For patients with inflammatory bowel disease (IBD), monitoring by intestinal ultrasound (IUS) results in reduced time to treatment change and to remission, according to a study presented at the annual meeting of the American College of Gastroenterology, held from Oct. 20 to 25 in Vancouver, British Columbia, Canada.

Recent advances in ultrasonography technology have led to increased usage at the point-of-care, with handheld devices offering both diagnostic and therapeutic applications.

When an algorithm-driven microscopy technique developed in 2021 (and able to run on a fraction of the images earlier techniques required) isn't fast enough, what do you do?

Ultrasound is used to diagnose many diseases in the abdominal cavity. A new study conducted at the University of Leipzig Medical Center and supported by the Helmholtz Institute for Metabolism, Obesity and Vascular Research (HI-MAG) shows that obesity affects the quality of ultrasound scans of the liver and kidneys. It also shows that the use of high-performance ultrasound probes can improve the an

In a proof-of-concept study published in The British Journal of Radiology, UC Davis clinicians and researchers have shown that a new, low-field 0.55T MRI (magnetic resonance imaging) system can create clear videos of moving wrists. This emerging technology could provide important insights to improve diagnoses and better understand wrist anatomy.

For the three- and four-dimensional cone-beam computed tomography (CBCT) imaging, flat-panel detector (FPD) is critical among applications such as oral imaging, image-guided radiation therapy and interventional therapy.

Traditional medical imaging works well for people with light skin, but has trouble getting clear pictures from patients with darker skin. A Johns Hopkins University–led team found a way to deliver clear pictures of anyone's internal anatomy, no matter their skin tone.

An artificial intelligence (AI) tool for interpreting chest radiographs produces reports that are not significantly different from those of radiologists, according to a study published online Oct. 5 in JAMA Network Open.

Locally run large language models (LLMs) may be a feasible option for extracting data from text-based radiology reports while preserving patient privacy, according to a new study from the National Institutes of Health Clinical Center (NIH CC) published in Radiology. LLMs are deep-learning models trained to understand and generate text in a human-like way.

Radiation therapy, employed for treating cancer and other ailments, is distinctive for its minimally invasive nature, facilitating outpatient treatment and a fast return to society. However, a notable challenge arises due to the potential impact of radiation on the adjacent healthy organs, especially while applying high radiation doses to diseased tissues that are in motion. Regular movements such

A new artificial intelligence (AI) model combines imaging information with clinical patient data to improve diagnostic performance on chest X-rays, according to a study published in Radiology.

In order to understand and detect diseases scientists and medical staff often rely on imaging methods such as ultrasound or X-ray. However, depending on the tissue the resolution and depth of the resulting image is limited or insufficient.

The axial field of view (AFOV) plays a pivotal role in determining image quality in positron emission tomography (PET). While total-body PET scanners like the uEXPLORER offer superior sensitivity, they come at a higher cost and limited accessibility.

An AI chest X-ray foundation model for disease detection demonstrated racial and sex-related bias leading to uneven performance across patient subgroups and may be unsafe for clinical applications, according to a study published in Radiology: Artificial Intelligence. The study aims to highlight the potential risks for using foundation models in the development of medical imaging artificial intelli

In a study of more than 2,000 chest X-rays, radiologists outperformed AI in accurately identifying the presence and absence of three common lung diseases, according to a study published in Radiology, a journal of the Radiological Society of North America (RSNA).

La Trobe University researchers have used a technique to look for early markers of neurological decline in both young and older retired contact sport athletes.

Neck pain is quite common, but care and treatments can vary widely. According to a UC Davis Health study, the specialist you see first for your neck pain makes a big difference in how it is diagnosed and treated.

Magnetic resonance (MR) microscope has previously been used to observe chemically fixed specimens of human embryos during their early developmental stages. This tool has been instrumental in advanced studies of human embryology.

According to an accepted manuscript published in the American Journal of Roentgenology, an artificial intelligence (AI) system developed by Lunit (Seoul, Korea) identified improperly positioned endotracheal tube (ETTs) on chest radiographs obtained after ETT insertion, as well as on chest radiographs obtained from patients in the ICU at two institutions.

Lene Andersen, MSW, has been living with rheumatoid arthritis and disability since childhood. Her personal experience with limited mobility and the challenges faced in accessing mammography screening in Toronto, Ontario, has fueled her determination to advocate for change. Her story is featured in an upcoming themed issue of the Journal of Medical Imaging and Radiation Sciences on the topic of spe

Invasive medical procedures, such as surgery requiring local anesthesia, often involve the risk of nerve injury. During operation, surgeons may accidentally cut, stretch, or compress nerves, especially when mistaking them for some other tissue. This can lead to long-lasting symptoms in the patient, including sensory and motor problems. Similarly, patients receiving nerve blockades or other types o

Using a standardized assessment, researchers in the UK compared the performance of a commercially available artificial intelligence (AI) algorithm with human readers of screening mammograms. Results of their findings were published in Radiology.

SFU engineering science professor Mirza Faisal Beg is spearheading research that could help to redefine cancer treatment. Using artificial intelligence (AI) his breakthrough research enables imaging of the human body and organ measurements much faster than traditional methods, signaling a leap forward in harnessing AI for individually personalized health care solutions.

Optical-resolution photoacoustic microscopy is an up-and-coming biomedical imaging technique for studying a broad range of diseases, such as cancer, diabetes and stroke. But its insufficient sensitivity has been a longstanding obstacle for its wider application.

The most impressive uses of artificial intelligence rely on good data—and lots of it. Chatbots, for example, learn to converse from millions of web pages full of text. Autonomous vehicles learn to drive from sensor data recorded on millions of road trips.