Keyword: machine learning

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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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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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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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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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Reinforced learning

AI masters tightrope walk of cancer treatment dosage

Using a new approach called 'reinforced learning', researchers have taught an artificial intelligence (AI) to responsibly choose the right amount of chemo- and radiotherapy for glioblastoma patients. The technique, which is insprired by behavioural psychology, has given the AI the ability to master the tightrope walk between effective tumor shrinkage and the medications' severe side effects.

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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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Man against machine

AI is better than dermatologists at diagnosing skin cancer

Researchers have shown for the first time that a form of artificial intelligence or machine learning known as a deep learning convolutional neural network (CNN) is better than experienced dermatologists at detecting skin cancer. In a study published in the leading cancer journal Annals of Oncology, researchers in Germany, the USA and France trained a CNN to identify skin cancer by showing it more…

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Machine Learning

Finding the right algorithms to tackle big data

Tracy Accardi, Hologic’s Vice President (Global R&D), spoke of the importance of innovation, tomosynthesis, artificial intelligence/deep learning and open dialogue with the radiology community. Hologic addresses a broad spectrum of gynaecological, perinatal, aesthetic, skeletal and breast women’s health issues. To enhance this approach, Accardi, explained the importance of working closely…

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The InnerEye Project

AI drives analysis of medical images

Some time in the distant future artificial intelligence (AI) systems may displace radiologists and many other medical specialists. However, in a far more realistic future AI tools will assist radiologists by performing very complex functions with medical imaging data that are impossible or unfeasible today, according to a presentation at the RSNA/AAPM Symposium during the Radiological Society of…

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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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Automation in radiology

Machine learning techniques generate clinical labels of medical scans

Researchers used machine learning techniques, including natural language processing algorithms, to identify clinical concepts in radiologist reports for CT scans, according to a study conducted at the Icahn School of Medicine at Mount Sinai and published in the journal Radiology. The technology is an important first step in the development of artificial intelligence that could interpret scans and…

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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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