AI

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Imaging informatics meeting

SIIM 2020: Glancing back at 40 years and ahead to the future

Forty years ago, a group of visionaries who believed that computers would have a huge impact on the functions of radiology departments formed the Radiology Information System Consortium (RISC). In 1989, RISC created the Society for Computer Applications (SCAR) to promote computer applications in digital imaging, and these organizations ultimately evolved to become the Society for Imaging…

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Smart breathing support

Self-learning ventilators could save more COVID-19 patients

As the corona pandemic continues, mechanical ventilators are vital for the survival of COVID-19 patients who cannot breathe on their own. One of the major challenges is tracking and controlling the pressure of the ventilators, to ensure patients get exactly the amount of air they need. Researchers at the Eindhoven University of Technology (TU/e) have developed a technique based on self-learning…

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Future of contrast agents

Gadolinium in MRI is here to stay (at least for a while)

Manganese and iron oxide contrast agents can replace gadolinium-based contrast agents (GBCA) in a number of MRI examinations, but gadolinium remains a strong candidate when properly indicated, especially with AI-driven dose reduction and advances to increase relaxivity, a French expert explained at ECR 2020. GBCA have been MRI companions for many years. In France, 30% of all MR examinations are…

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Going digital

How digital pathology is shaping the future of precision medicine

In recent years, technological and regulatory advances have made digital pathology a viable alternative to the conventional microscope. The obtention of a digital replica of the traditional glass slide and its use for primary diagnosis has revolutionized pathology and is shaping the future of the discipline. A digital pathology lab uses digital histology slides for routine diagnosis, and these…

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Brain tumor treatment network

'Federated learning' AI approach allows hospitals to share patient data privately

To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study…

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Deep learning in imaging

1.5T MR system receives FDA clearance for AI-based image reconstruction technology

Canon Medical Systems USA, Inc. has received 510(k) clearance on its Advanced intelligent Clear-IQ Engine (AiCE) for the Vantage Orian 1.5T MR system, continuing to expand access to its new Deep Learning Reconstruction (DLR) technology. This technology, which is also available on the Vantage Galan 3T MR system and across a majority of Canon Medical’s CT product portfolio, uses a deep learning…

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Algorithmic challenges

Radiographers urge caution when working with AI

The Artificial Intelligence (AI) landscape confronting the radiographer profession will be outlined in sessions at ECR 2020, with leading practitioners urging the need for an evidence-based approach in order to deliver a safe and effective service for patients. The session, under the broad heading of “Artificial intelligence and the radiographer profession”, aims to discuss AI within the…

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Rad Companion

Siemens expands AI portfolio in clinical decision-making

The AI-Rad Companion family supports radiologists, radiation oncologists, radiotherapists and medical physicists through automated post processing of MRI, CT and X-ray datasets. It saves the clinicians' time and helps them to increase their diagnostic precision. The steady rise of radiology examinations and staff shortages lead to a limited amount of time per case as well as an increasing danger…

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Expectations vs. reality

AI in clinical practice: how far we are and how we can go further

Luis Martí-Bonmatí, Director of the Medical Imaging Department at La Fe Hospital in Valencia, highlighted the need to assess utility when developing AI tools, as he looked into the present and future of artificial intelligence’s adoption in radiology during ECR 2020. Artificial intelligence (AI) can impact and improve many aspects of clinical practice. But current expectations are too great…

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Deep Learning in Radiology

New Levels of Precision with Self-learning Imaging Software

The complex form of machine learning DLIR (Deep Learning Image Reconstruction) is based on a deep neuronal network which is similar to the human brain. The artificial neurons of this network learn according to their biological model through intensive training. For the DLIR image reconstruction, the network is fed with sample data from phantom images on the one hand and high-resolution images of…

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New

Improving hospitals’ time efficiency via a Connected Radiology platform

Thales’ expert knowledge in digital technology as well as in hardware and software systems has enabled the company to become a market leader in major innovation fields such as the cloud, connectivity and artificial intelligence. Thales is proud to launch its unique Connected Radiology platform which will bring multiple benefits to the efficiency of hospitals through the non-stop use of…

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high-density EEG

A deeper look inside the brain

Understanding the source and network of signals as the brain functions is a central goal of brain research. Now, Carnegie Mellon engineers have created a system for high-density EEG imaging of the origin and path of normal and abnormal brain signals.

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Algorithmic enhancement

Improved MRI scans could aid in development of arthritis treatments

An algorithm that analyses MRI images and automatically detects small changes in knee joints over time could be used in the development of new treatments for arthritis. A team of engineers, radiologists and physicians, led by the University of Cambridge, developed the algorithm, which builds a three-dimensional model of an individual’s knee joint in order to map where arthritis is affecting the…

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Glioma grading

AI enhances brain tumour diagnosis

A new machine learning approach classifies a common type of brain tumour into low or high grades with almost 98% accuracy, researchers report in the journal IEEE Access. Scientists in India and Japan, including from Kyoto University’s Institute for Integrated Cell-Material Sciences (iCeMS), developed the method to help clinicians choose the most effective treatment strategy for individual…

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Immune cell monitoring

AI could predict risk of lung cancer recurrence

Computer scientists working with pathologists have trained an artificial intelligence (AI) tool to determine which patients with lung cancer have a higher risk of their disease coming back after treatment. The AI tool was able to differentiate between immune cells and cancer cells, enabling researchers to build a detailed picture of how lung cancers evolve in response to the immune system in…

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From physical to computational staining

Deep learning accurately stains digital biopsy H&E slides

Tissue biopsy slides stained using hematoxylin and eosin (H&E) dyes are a cornerstone of histopathology, especially for pathologists needing to diagnose and determine the stage of cancers. A research team led by MIT scientists at the Media Lab, in collaboration with clinicians at Stanford University School of Medicine and Harvard Medical School, now shows that digital scans of these biopsy…

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Lesion differentiation

AI successfully identifies different types of brain injuries

Researchers have developed an AI algorithm that can detect and identify different types of brain injuries. The researchers, from the University of Cambridge and Imperial College London, have clinically validated and tested the AI on large sets of CT scans and found that it was successfully able to detect, segment, quantify and differentiate different types of brain lesions. Their results,…

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Coronavirus imaging

AI enhanced lung ultrasound for COVID-19 testing

Establishing whether a patient is suffering from severe lung disease, possibly COVID-19, within a few minutes: this is possible using fairly simple ultrasound machines that are enhanced with artificial intelligence. A research team at Eindhoven University of Technology (TU/e) and the University of Trento in Italy has been able to translate the expertise of top lung specialists into a software…

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Algorithm-assisted diagnostics

AI in imaging: not as reliable as you'd think

Machine learning and AI are highly unstable in medical image reconstruction, and may lead to false positives and false negatives, a new study suggests. A team of researchers, led by the University of Cambridge and Simon Fraser University, designed a series of tests for medical image reconstruction algorithms based on AI and deep learning, and found that these techniques result in myriad…

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Better tissue discrimination, lower radioation dose

Improving image quality of CT scans

Computed tomography (CT) is one of the most effective medical tests for analysing the effects of many illnesses, including COVID-19, on the lungs. An international team led by the Universitat Oberta de Catalunya (UOC) has developed a new method that improves the quality of the images obtained from CT scans. The algorithm, which has been tested on simulated data, enables them to distinguish…

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Symptom study app

AI diagnostic to predict COVID-19 without testing

Researchers at King’s College London, Massachusetts General Hospital and health science company ZOE have developed an artificial intelligence (AI) diagnostic that can predict whether someone is likely to have COVID-19 based on their symptoms. Their findings are published in Nature Medicine. The AI model uses data from the COVID Symptom Study app to predict COVID-19 infection, by comparing…

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