Keyword: machine learning

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Decision support

AI can predict septic shock

Researchers at Linköping University (LiU) have developed an algorithm that can identify patients at a higher risk of septic shock, a life-threatening condition that is difficult for doctors to predict. At the same time, it is important to recognise the symptoms as early as possible, since early treatment increases the chance of survival. A group of LiU researchers is using artificial…

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MRI & machine learning

A look into the genome of brain tumors

Researchers at Osaka University have developed a computer method that uses magnetic resonance imaging (MRI) and machine learning to rapidly forecast genetic mutations in glioma tumors, which occur in the brain or spine. The work may help glioma patients to receive more suitable treatment faster, giving better outcomes. The research was recently published in Scientific Reports. Cancer treatment…

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A closer look at blood lipids

Lipidomics and machine learning predict diabetes risk

Using lipidomics, a technique that measures the composition of blood lipids at a molecular level, and machine learning, researchers at Lund University in Sweden have identified a blood lipid profile that improves the possibility to assess, several years in advance, the risk of developing type 2 diabetes. The blood lipid profile can also be linked to a certain diet and degree of physical activity.…

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Machine learning for the mind

Astrophysics and AI team up for early dementia diagnosis

Crucial early diagnosis of dementia in general practice could improve thanks to a computer model designed in a collaboration between Brighton and Sussex Medical School (BSMS) and astrophysicists at the University of Sussex. Currently, only two-thirds of people with dementia in the UK receive a formal diagnosis, and many receive it late in the disease process, meaning that a large number are…

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Single cell analysis & machine learning

scPred: Finding the ‘fingerprint’ of human cells

Researchers say a new method to analyse data from individual human cells could be a step-change for diagnosing some of the most devastating diseases, including cancer and autoimmune disease. By combining single cell analysis techniques with machine learning algorithms, a team led by researchers at the Garvan Institute of Medical Research has developed a method to ‘fingerprint’ human cells.…

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Grant for AI and genomic analysis

AI help for better diagnosis and treatment of prostate cancer

Prostate cancer is the second cause of cancer-related death in men. Currently, its diagnosis occurs via imaging and must be confirmed by biopsy. Simona Turco from the Eindhoven University of Technology (TU/e) wants to improve prostate cancer diagnosis by using machine learning algorithms to localize tumors and thereby replace entirely the necessity for biopsies. Besides, by combining this with…

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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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"Umbrella" trial

Urine test detects acute kidney transplant rejection

Early non-invasive detection of kidney rejection after transplantation was the central aim of a collaboration between Prof. Dr. Bernhard Banas, Chairman of Nephrology at the University Hospital Regensburg (UKR) and the medical diagnostics company, numares. The results of their joint clinical trial “UMBRELLA” were just published in EBioMedicine and presented at the American Society of…

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Backup from BARDA

Sepsis early detection algorithm receives development funding

Beckman Coulter, Inc. announced that it has initially been awarded a contract of $1.25 million, with potential to be awarded an additional $6.5 million if all contract options are exercised, from the DRIVe (Division of Research, Innovation, and Ventures) established by the Biomedical Advanced Research and Development Authority (BARDA), under the Office of the Assistant Secretary for Preparedness…

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Better image quality with fewer sensors

Machine learning improves biomedical imaging

Scientists at ETH Zurich and the University of Zurich have used machine learning methods to improve optoacoustic imaging. This relatively young medical imaging technique can be used for applications such as visualizing blood vessels, studying brain activity, characterizing skin lesions and diagnosing breast cancer. However, quality of the rendered images is very dependent on the number and…

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RiskCardio

Using machine learning to estimate risk of cardiovascular death

Humans are inherently risk-averse: We spend our days calculating routes and routines, taking precautionary measures to avoid disease, danger, and despair. Still, our measures for controlling the inner workings of our biology can be a little more unruly. With that in mind, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with a new system for better…

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AI in diagnostics

Learn like a human, deduce like a machine

Artificial Intelligence (AI) is like a huge blanket that can cover anything from innocuous chess computers to robots which, depending on your viewpoint, could save, oppress or obliterate humanity. However, not every jar labelled AI contains AI. So what is intelligence and can it be created artificially, synthesised like a nature-identical flavouring substance?

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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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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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