Keyword: machine learning

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Fat radiomic profile

Using AI to predict heart attacks

Technology developed using artificial intelligence (AI) could identify people at high risk of a fatal heart attack at least five years before it strikes, according to new research funded by the British Heart Foundation (BHF). The findings are being presented at the European Society of Cardiology (ESC) Congress in Paris and published in the European Heart Journal. Researchers at the University of…

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Artificial selection

Improving clinical trial recruitment with AI

Clinical trials are a critical tool for getting new treatments to people who need them, but research shows that difficulty finding the right volunteer subjects can undermine the effectiveness of these studies. Researchers at Cincinnati Children’s Hospital Medical Center designed and tested a new computerized solution that used artificial intelligence (AI) to effectively identify eligible…

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Bacterial virus

Machine learning detects inuviruses

A team led by scientists at the U.S. Department of Energy (DOE) Joint Genome Institute (JGI) developed an algorithm that a computer could use to conduct a similar type of search in microbial and metagenomic databases. In this case, the machine “learned” to identify a certain type of bacterial viruses or phages called inoviruses, which are filamentous viruses with small, single-stranded DNA…

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Will machines take over?

AI? We shouldn’t worry about it – yet

Humanity is not doomed to submit to machines as in the Terminator movies – or at least not yet. Artificial intelligence (AI) systems are still far from capable to imitate the human brain in all its complexity. Yet there is no doubt that AI will have a global and huge impact, particularly for professionals such as radiologists, who should look at AI critically and focus on the many new…

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AI in imaging

Radiologists must control their own destiny

Radiologists have not ended talk about artificial intelligence and machine learning but, rather than fear for the future of their profession, they themselves must decide how that should be, an eminent expert Dr Woojin Kim warned ECR delegates in Vienna in March. Two years in discussion and the hype around artificial intelligence (AI) is far from fading. Interest has never been higher, and the…

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At age 2

AI and MRIs at birth can predict cognitive development

Researchers at the University of North Carolina School of Medicine used MRI brain scans and machine learning techniques at birth to predict cognitive development at age 2 years with 95 percent accuracy. “This prediction could help identify children at risk for poor cognitive development shortly after birth with high accuracy,” said senior author John H. Gilmore, MD, Thad and Alice Eure…

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Man and machine

The radiologist as today’s centaur

Artificial intelligence (AI) continues to drive radiologists’ discussions. Among them, Associate Professor Georg Langs, head of the Computational Imaging Research Lab (CIR) at the University Clinic for Radiology and Nuclear Medicine at the Medical University of Vienna, believes: ‘The evaluation of patterns in data from imaging examinations and clinical information about patients using machine…

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Overheard at RSNA

Radiologists optimistic about AI

The topic of artificial intelligence (AI) was omnipresent at RSNA2018, the annual meeting of the Radiological Society of North America. From the opening presidential address, throughout scientific sessions and educational presentations, to the vendors’ technical exhibition, around 53,000 attendees learned about pioneering new products, research, plus challenges and opportunities to implement…

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Radiomics

A boost for thoracic radiology

A new radiomics study could help unlock one of the more challenging issues facing thoracic radiologists. Distinguishing non-small cell lung cancer from benign nodules is a major challenge due to their similar appearance on CT images. Now, however, researchers from Case Western Reserve University in Cleveland, Ohio, have used radiomic features extracted from CT images to differentiate between…

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Machine learning tool

AI can predict survival of ovarian cancer patients

Researchers have created a new machine learning software that can forecast the survival rates and response to treatments of patients with ovarian cancer. The artificial intelligence software, created by researchers at Imperial College London and the University of Melbourne, has been able to predict the prognosis of patients with ovarian cancer more accurately than current methods. It can also…

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Foodborne illnesses

Machine learning gets to the source of Salmonella

A team of scientists led by researchers at the University of Georgia Center for Food Safety in Griffin has developed a machine-learning approach that could lead to quicker identification of the animal source of certain Salmonella outbreaks. In the research, published in Emerging Infectious Diseases, Xiangyu Deng and his colleagues used more than a thousand genomes to predict the animal sources,…

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Accuracy improvement

Predicting prostate cancer with radiomics and machine learning

A team of researchers from the Icahn School of Medicine at Mount Sinai and Keck School of Medicine at the University of Southern California (USC) have developed a novel machine-learning framework that distinguishes between low- and high-risk prostate cancer with more precision than ever before. The framework, described in a Scientific Reports paper, is intended to help physicians—in particular,…

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Chatbots & AI

Increasing conversational intelligence with machine learning

Nuance Communications, Inc., at its Customer Experience Summit, revealed Project Pathfinder, a breakthrough technology that uses machine learning and Nuance AI innovation to increase the conversational intelligence of virtual assistants (VAs) and chatbots. Project Pathfinder reads existing chat logs and transcripts of conversations between agents and customers within contact centers, and…

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ICU alarm algorithms

Machine learning eliminates false alarms in intensive care

In intensive care units (ICU), some monitoring device or other is always sounding the alarm. Whether it’s a patient whose blood oxygen level is too low, someone in the next bed whose intracranial pressure is rising, or someone else whose blood pressure has taken a nosedive. Or perhaps just because a patient has shifted position in bed. False alarms like this last are all too common. They…

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AI in cardiology

Cardiac ultrasound: Harnessing anatomical intelligence

A new cardiac ultrasound solution is harnessing the power of anatomical intelligence to offer greater diagnostic confidence to clinicians. New EPIQ cardiac ultrasound solutions launched by Philips during the 2018 ESC Congress in Munich, have been designed to simplify workflow The CVx platform which, the firm reports, includes higher processing power, improved image clarity and sharpness, and more…

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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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Non-invasive diagnostics

Detecting bladder cancer with atomic force microscopy

A research team led by Tufts University engineers has developed a non-invasive method for detecting bladder cancer that might make screening easier and more accurate than current invasive clinical tests involving visual inspection of bladder. In the first successful use of atomic force microscopy (AFM) for clinical diagnostic purposes, the researchers have been able to identify signature features…

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Machine learning

Training a computer to classify breast cancer tumors

Using technology similar to the type that powers facial and speech recognition on a smartphone, researchers at the University of North Carolina Lineberger Comprehensive Cancer Center have trained a computer to analyze breast cancer images and then classify the tumors with high accuracy. In a study published in the journal NPJ Breast Cancer, researchers reported they used a form of artificial…

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Open source

Machine Learning tool could help choose cancer drugs

The selection of a first-line chemotherapy drug to treat many types of cancer is often a clear-cut decision governed by standard-of-care protocols, but what drug should be used next if the first one fails? That’s where Georgia Institute of Technology researchers believe their new open source decision support tool could come in. Using machine learning to analyze RNA expression tied to…

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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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A challenger arrives

AI – just a tool or the future of healthcare?

Neuroscientist Lynda Chin MD, Founder and CEO of Real-world Education Detection and Intervention, has little doubt: ‘Artificial intelligence to the rescue,’ she proclaimed in her keynote address at the Artificial Intelligence and Machine Learning Summit, held in Las Vegas this spring. ‘We need a system and analytics to interpret data!’ she urged, despite being well aware that building a…

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A valuable assistant

AI will transform radiologists into data scientists

Machine learning is increasingly helping radiologists to acquire faster and better quality images, and measure heart function. This is just the tip of the iceberg; artificial intelligence has far more to bring to the heart, explained Daniel Rueckert, Head of the Department of Computing at Imperial College London, during CMR 2018. Machine learning (ML) is becoming a valuable assistant for…

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