Keyword: deep learning

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Distributed learning

Radiomics on tap in 5-10 years

Keeping data within the hospital by sending the learning modules to each hospital database might prove a game-changer in radiomics, a leading Dutch researcher will show at ECR 2019. Radiomics, a field that aims to extract large amounts of quantitative features from medical images using data-characterisation algorithms, is a major advance for healthcare, according to Philippe Lambin, a radiation…

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Breast and skeletal health

AI is proving pivotal in women’s health solutions

Artificial Intelligence (AI) is proving pivotal as Hologic evolves its women’s health solutions. With a focus on breast and skeletal health, future steps will see the medical technology company incorporate a more integrated approach to drive better, more cost effective, outcomes that are clinically supported to deliver an improved patient experience. Pete Valenti, Hologic’s division president…

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Future healthcare

AI in radiology: beyond imaging

Today, artificial intelligence (AI) can be found everywhere: in our cars, our smartphones and even our working environments. AI has many areas of application, including in the healthcare sector. AI will change the interaction between doctors and patients, but most patients won’t even know it’s involved. That’s because improving the patient experience, helping to increase productivity,…

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AI in radiology

Augmented intelligence rather than artificial

Artificial intelligence (AI) will increase efficiency and improve quality as well as clinical outcomes – and thus strengthen rather than weaken the role of radiologists, said Dr Joon Beom Seo at ECR 2018. A spectre is haunting radiologists – the spectre of artificial intelligence. Is AI about to replace radiologists? Wrong question,’ declared radiologist Dr Joon Beom Seo, professor at the…

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Artificial intelligence

Deep learning may help reduce gadolinium dose in MRI

Researchers are using artificial intelligence to reduce the dose of a contrast agent that may be left behind in the body after MRI exams, according to a study presented at the annual meeting of the Radiological Society of North America (RSNA). Gadolinium is a heavy metal used in contrast material that enhances images on MRI. Recent studies have found that trace amounts of the metal remain in the…

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Intensive care

Deep learning software helps to locate the carina

The ability to accurately check the position of the endotracheal tube for patients in intensive care units is crucial to their wellbeing and safe treatment. A pivotal element of this lies in identifying the position of the carina, a ridge of cartilage in the trachea that occurs between the division of the two main bronchi. Yet highlighting its location often proves problematic on portable chest…

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Future of radiology

'Radiologists who do not use AI will be replaced by those who do'

Recent developments in artificial intelligence (AI) created a veritable hype. However, that initial awe was increasingly mixed with apprehension about the potential effects of AI on healthcare. In radiology, bleak dystopias are conjured up with AI replacing the human radiologist. A scenario that Dr Felix Nensa, consultant at the Institute of Diagnostic and Interventional Radiology and…

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AI structure

Machine Learning: into the pumpkin patch

Standardised and well-structured data, as well as the definition of clear objectives, are indispensable prerequisites for artificial intelligence implementation into clinical processes. ‘Ask not what artificial intelligence can do for you but what you can do for artificial intelligence!’ This variation on John F Kennedy’s famous quote comes from Dr Ben Glocker, Senior Lecturer in Medical…

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The revolution escalates

AI image analysis: Opportunity or threat?

'Image Computing, including image analysis, artificial intelligence, artificial neural networks und deep learning, is starting a revolution,’ says Dr Paul Suetens, professor of Medical Imaging and Image Processing at University Hospital Leuven. Artificial Intelligence (AI) is not new – research in this field was carried out as far back as the 1950s – but, whilst in the early days AI learnt…

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Heard at the 14th ECDP in Helsinki

Digital pathology: Sometimes AI can outperform experts

Machine learning is adding a new dimension to pathology and already outperforming experts during some tasks, according to several speakers at the 14th European Congress on Digital Pathology (ECDP) who revealed up-to-date developments. However, whilst AI is set to herald a new future for digital pathology, Johan Lundin, associated professor for biomedical informatics and research director at the…

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AI & Deep Learning

How to escape from data silos

Artificial Intelligence (AI) and machine learning are poised to transform healthcare, potentially freeing practitioners across many disciplines from routine tasks and saving lives through efficient early detection. Offering insight into the health of both individuals and populations, these ’deep learning‘ algorithms have the potential to process vast amounts of data and identify warning…

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The impact of AI

Radiology and radiologists: a painful divorce

Artificial intelligence based applications will replace radiologists in some areas, the physicist Bram van Ginneken predicts. ‘The profession of radiologist will change profoundly,’ predicts Gram van Ginneken, Professor of Medical Image Analysis at Radboud University Medical Centre. The cause is automatic image analysis by computers (first published in a paper in 1963) and deep learning, the…

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Machine learning

Google AI now can predict cardiovascular problems from retinal scans

Google AI has made a breakthrough: successfully predicting cardiovascular problems such as heart attacks and strokes simply from images of the retina, with no blood draws or other tests necessary. This is a big step forward scientifically, Google AI officials said, because it is not imitating an existing diagnostic but rather using machine learning to uncover a surprising new way to predict these…

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Smart techniques

Machine learning is starting to reach levels of human performance

Machine learning is playing an increasing role in computer-aided diagnosis, and Big Data is beginning to penetrate oncological imaging. However, some time may pass before it truly impacts on clinical practice, according to leading UK-based German researcher Professor Julia Schnabel, who spoke during the last ESMRMB annual meeting. Machine learning techniques are starting to reach levels of human…

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TAITRA presentation

Taipei hits highs in Medica 2017

3-D visualisation, augmented reality, automated tumour classification – today, the Republic of China produces cutting-edge medical technology and it’s a long time since ‘Made in Taiwan’ stood for inferior, copied products. Over recent years, this island state has successfully morphed into a productive and, above all, innovative manufacturer of medical technology available on the world…

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A new chance for screening

Deep Learning shows potential for accurately reading mammograms

The use of deep learning (DL) technology could help radiologists increase the quality of breast cancer screening programs, lower costs, and reduce the variability in the cancer detection process. And the role of DL technology in imaging doesn't stop there. In fact, it is likely that DL computers can be trained to read mammograms as well as radiologists and — in the future — maybe even…

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Technological turmoil

Deep Learning and AI will redefine radiology

While there has been a lot of hype — and even fear — about the role deep learning (DL) and artificial intelligence (AI) play in radiology, the reality is that they are both potentially useful technologies that will add value to the specialty in a number of ways. "Deep Learning is not going to replace us," said Paul Chang, MD, of the University of the Chicago School of Medicine,…

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Digital Pathology

Deep learning and AI progress

Early adoption of image analytical tools and artificial intelligence (AI) are crucial if health systems across Europe are to see the full potential of digital pathology, according to leading expert Professor Johan Lundin, Research Director at the Institute for Molecular Medicine Finland (FIMM) at the University of Helsinki. Although European institutions increasingly embrace digital pathology, he…

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Deep learning

Samsung: AI develops beyond the breast

Access, accuracy and efficiency are at the core of Samsung’s healthcare strategy, explained Insuk Song, Vice President of Product Planning, Healthcare and Medical Equipment at Samsung Electronics, during our exclusive European Hospital interview. Samsung, the Korean giant, is now proceding with its artificial intelligence (AI) deployment, notably with the S-Detect software to help ultrasound…

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Algorithmic examination

Using machine learning to predict sepsis

A machine-learning algorithm has the capability to identify hospitalized patients at risk for severe sepsis and septic shock using data from electronic health records (EHRs), according to a study presented at the 2017 American Thoracic Society International Conference. Sepsis is an extreme systemic response to infection, which can be life-threatening in its advanced stages of severe sepsis and…

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Imaging technology

Visiopharm engages in major initiative for Deep Learning

Visiopharm A/S announces the first result of their multifaceted strategy to apply Deep Learning technologies to its leading image analysis solution for cancer research and diagnostics. Visiopharm considers Deep Learning an important technological breakthrough for tissue pathology that offers the potential to make a real difference in the assessment of tissue structures, which is probably one of…

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Deep Learning

Philips and LabPON plan to create world’s largest pathology database

Royal Philips (NYSE: PHG, AEX: PHIA) and LabPON, the first clinical laboratory to transition to 100% histopathology digital diagnosis, today announced its plans to create a digital database of massive aggregated sets of annotated pathology images and big data utilizing Philips IntelliSite Pathology Solution1. The database will provide pathologists with a wealth of clinical information for the…

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Deep Learning

Deep Learning predicts hematopoietic stem cell development

Autonomous driving, automatic speech recognition, and the game Go: Deep Learning is generating more and more public awareness. Scientists at the Helmholtz Zentrum München and their partners at ETH Zurich and the Technical University of Munich (TUM) have now used it to determine the development of hematopoietic stem cells in advance. In ‘Nature Methods’ they describe how their software…

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