Machine learning

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Deep Learning vs. Machine Learning

Radiomics strengthens breast imaging

The field of AI-enhanced imaging provides radiologists with an unprecedented opportunity to shape patient care, a leading Austrian radiologist explained at ECR 2020. Workflow with radiomics starts with image acquisition and segmentation. When the region of interest is defined, radiomics analysis enables extraction of a large quantity of imaging features, and then to select, reduce, classify and…

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Brain tumor treatment network

'Federated learning' AI approach allows hospitals to share patient data privately

To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study…

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Glioma grading

AI enhances brain tumour diagnosis

A new machine learning approach classifies a common type of brain tumour into low or high grades with almost 98% accuracy, researchers report in the journal IEEE Access. Scientists in India and Japan, including from Kyoto University’s Institute for Integrated Cell-Material Sciences (iCeMS), developed the method to help clinicians choose the most effective treatment strategy for individual…

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Identification of skin cancer

Machine learning challenge on melanoma classification

The Society for Imaging Informatics in Medicine (SIIM) and the International Skin Imaging Collaboration (ISIC) are working together to host a machine learning challenge on melanoma classification, using the ISIC archive which contains the largest publicly available collection of quality controlled dermoscopic images of skin lesions. Image contributors include: Hospital Clínic de Barcelona,…

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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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Corona-induced coughing

New app listens to the 'sounds of COVID-19'

A new app, which will be used to collect data to develop machine learning algorithms that could automatically detect whether a person is suffering from COVID-19 based on the sound of their voice, their breathing and coughing, has been launched by researchers at the University of Cambridge. The COVID-19 Sounds App is now available as a web app for Chrome and Firefox browsers. Versions for Android…

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Oncology breakthrough

Blood test detects 50+ cancer types, often before symptoms show

Researchers have developed the first blood test that can accurately detect more than 50 types of cancer and identify in which tissue the cancer originated, often before there are any clinical signs or symptoms of the disease. In a paper published in the leading cancer journal Annals of Oncology, the researchers show that the test, which could eventually be used in national cancer screening…

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Machine learning in intensive care

AI can predict circulatory failure in ICU

Researchers at ETH Zurich and Inselspital, Bern University Hospital, have developed a method for predicting circulatory failure in patients in intensive care units (ICU) – enabling clinicians to intervene at an early stage. Their approach uses machine learning methods to evaluate an extensive body of patient data. Patients in a hospital’s ICU are kept under close observation: clinicians…

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The science of sleep

AI could enhance diagnosis and treatment of sleep disorders

Artificial intelligence (AI) has the potential to improve efficiencies and precision in sleep medicine, resulting in more patient-centered care and better outcomes, according to a new position statement from the American Academy of Sleep Medicine. Published in the Journal of Clinical Sleep Medicine, the position statement was developed by the AASM’s Artificial Intelligence in Sleep Medicine…

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Blood test & AI power

Early brain tumour detection – within minutes

A simple blood test coupled with artificial intelligence (AI) analysis could help spot the signs of a brain tumour sooner in patients. Brain tumour diagnosis is difficult: patients often see their family doctor (GP) several times before referral for a scan. However, research presented at the 2019 National Cancer Research Institute (NCRI) Cancer Conference in Glasgow last November suggests the…

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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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