deep learning

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Sample analysis

Next-generation analytical lab software strengthens data exploration

Scientists in the life sciences can now benefit from upgrades to a suite of analytical software solutions with new features designed to enhance productivity, confidence and accuracy in numerous fields, including proteomics, food safety and biotherapeutic drug development. The latest suite of software strengthens laboratory workflows across a range of applications through expanded capabilities,…

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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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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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Tools for practitioners

Computational pathology: Heading for personalised medicine

Computational pathology has increased applications for diagnosis, prediction of prognosis and therapy response, facilitating the movement of healthcare towards personalised medicine. Coupled with deep learning, such tools are ever more efficient and robust within research and clinical settings. The growing role of computational pathology was highlighted by Professor Andrew Janowczyk at the…

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Experts express doubts

AI outperforming doctors: hype, exaggeration or fact?

Many studies claiming that artificial intelligence (AI) is as good as (or better than) human experts at interpreting medical images are of poor quality and are arguably exaggerated, posing a risk for the safety of ‘millions of patients’ warn researchers in The BMJ. Their findings raise concerns about the quality of evidence underpinning many of these studies, and highlight the need to improve…

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Deep learning & CNN

Algorithm differentiates small renal masses on multiphase CT

A deep learning method with a convolutional neural network (CNN) can support the evaluation of small solid renal masses in dynamic CT images with acceptable diagnostic performance, according to an article published ahead-of-print in the March issue of the American Journal of Roentgenology (AJR). Between 2012 and 2016, researchers at Japan’s Okayama University studied 1807 image sets from 168…

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DeepMind to help human radiologists

Google-powered AI spots breast cancer

A computer algorithm has been shown to be as effective as human radiologists in spotting breast cancer from x-ray images. The international team behind the study, which includes researchers from Google Health, DeepMind, Imperial College London, the NHS and Northwestern University in the US, designed and trained an artificial intelligence (AI) model on mammography images from almost 29,000 women.…

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Exposing the enemy

New algorithm detects even the smallest cancer metastases

Teams at Helmholtz Zentrum München, LMU Munich and the Technical University of Munich (TUM) have developed a new algorithm that enables automated detection of metastases at the level of single disseminated cancer cells in whole mice. Cancer is one of the leading causes of death worldwide. More than 90% of cancer patients die of distal metastases rather than as a direct result of the primary…

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Diagnostics & therapy

AI: Hype, hope and reality

Artificial intelligence (AI) opens up a host of new diagnostic methods and treatments. Almost daily we read about physicians, researchers or companies that are developing an AI system to identify malignant lesions or dangerous cardiac patterns, or that can personalise healthcare. ‘Currently, we are too focused on the topic,’ observes Professor Christian Johner, of the Johner Institute for…

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Deep learning vs. AML

AI-driven blood cell classification supports leukemia diagnosis

For the first time, researchers from Helmholtz Zentrum München and the University Hospital of LMU Munich show that deep learning algorithms perform similar to human experts when classifying blood samples from patients suffering from acute myeloid leukemia (AML). Their proof of concept study paves the way for an automated, standardized and on-hand sample analysis in the near future. The paper was…

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Artificial intelligence in radiology

Assessing the AI revolution

How will artificial intelligence (AI) affect continuing education and management in radiology? This issue was discussed by an expert panel at the ESR AI Premium meeting in Barcelona. Continuing education – It must be clear what radiologists need to learn about AI; one way to go could be to give it more space in the training curriculum, according to Elmar Kotter, deputy director of the radiology…

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Cardiology & radiology

AI opens up boundaries between medical disciplines

Uwe Joseph Schoepf, Professor for Radiology, Cardiology and Paediatrics and Director of the Department of Cardiovascular Imaging at the Medical University of South Carolina, discusses areas of application for AI-based radiology. The cardiothoracic imaging expert and his team were largely involved in the development and early clinical trials of the Siemens AI-Rad Companion Chest CT, a software…

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Microsatellites

Stomach and colorectal cancer: AI identifyies patients for immunotherapy

Changes in certain sections of the genetic material of cancer cells, so-called microsatellites, can provide an important indication of whether immunotherapy may be successful in a patient with stomach or colorectal cancer. Scientists from Uniklinik RWTH Aachen, the German Cancer Research Center (DKFZ), the German Cancer Consortium (DKTK) and the National Center for Tumor Diseases Heidelberg (NCT)…

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Advanced imaging

First CT scanner with AI installed in Belgium

Canon Medical has installed the Aquilion One Genesis, one of the first CT scanners with AI functionality in Europe, in Aalst’s General City Hospital, making it the first hospital in Belgium to boast AiCE technology. Canon Medical has named its AI application in the Aquilion One Genesis ‘AiCE’, which stands for ‘Advanced Intelligent Clear-IQ Engine’. AiCE is the first 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 considers premature, to say the least.

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