Machine learning

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Connectome analysis

Machine learning improves prediction of stroke recovery

An international team of scientists led by EPFL has developed a system that combines information from the brain’s connectome – the “wiring” between neurons – and machine learning to assess…

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WHO global report

The six guiding principles for AI in healthcare

Artificial Intelligence (AI) holds great promise for improving the delivery of healthcare and medicine worldwide, but only if ethics and human rights are put at the heart of its design, deployment,…

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COVID-19

AI shortcuts could lead to misdiagnosis of

University of Washington researchers have discovered that AI models—like humans—have a tendency to look for shortcuts. In the case of AI-assisted disease detection, these shortcuts could lead to…

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Solving the “black box” problem

Companies launch video series explaining imaging AI

Three leading AI scale-ups - Aidence, ScreenPoint Medical and Thirona - have launched the informative video series “Opening the black box of AI in medical imaging”.

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AI-as-a-service

Carestream accelerates development and delivery of AI applications for medical imaging

Carestream Health is transforming and accelerating the way it develops and delivers AI applications for medical imaging that help improve patient care. The state-of-the-art initiative is based on…

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AI in cardiology

Machine learning accurately predicts cardiac arrest risk

A branch of artificial intelligence (AI), called machine learning, can accurately predict the risk of an out of hospital cardiac arrest--when the heart suddenly stops beating--using a combination of…

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Discerning good algorithms from bad ones

Medical AI evaluation is surprisingly patchy, study finds

In just the last two years, artificial intelligence has become embedded in scores of medical devices that offer advice to ER doctors, cardiologists, oncologists, and countless other health care providers. But how much do either regulators or doctors really know about the accuracy of these tools? A new study led by researchers at Stanford, some of whom are themselves developing devices, suggests…

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Shepherding medical data

Machine learning platform turns healthcare data into insights

Over the past decade, hospitals and other healthcare providers have put massive amounts of time and energy into adopting electronic healthcare records, turning hastily scribbled doctors' notes into durable sources of information. But collecting these data is less than half the battle. It can take even more time and effort to turn these records into actual insights — ones that use the learnings…

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Model analysis

Machine learning for Covid-19 diagnosis: promising, but still too flawed

Systematic review finds that machine learning models for detecting and diagnosing Covid-19 from medical images have major flaws and biases, making them unsuitable for use in patients. However, researchers have suggested ways to remedy the problem. Researchers have found that out of the more than 300 Covid-19 machine learning models described in scientific papers in 2020, none of them is suitable…

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"Alexa, do I have an irregular heart rhythm?"

AI uses smart speakers for contactless cardiac monitoring

Smart speakers, such as Amazon Echo and Google Home, have proven adept at monitoring certain health care issues at home. For example, researchers at the University of Washington have shown that these devices can detect cardiac arrests or monitor babies breathing. But what about tracking something even smaller: the minute motion of individual heartbeats in a person sitting in front of a smart…

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Potential for rapid, accurate glycan sequencing

Enormous boost for sequencing key molecules

Using a nanopore, researchers have demonstrated the potential to reduce the time required for sequencing a glycosaminoglycan — a class of long chain-linked sugar molecules as important to our biology as DNA — from years to minutes. Research to be published this week in the Proceedings of the National Academies of Sciences shows that machine-learning and image recognition software could be…

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Survival prediction

Deep learning may lead to better lung cancer treatments

Doctors and healthcare workers may one day use a machine learning model, called deep learning, to guide their treatment decisions for lung cancer patients, according to a team of Penn State Great Valley researchers. In a study, the researchers report that they developed a deep learning model that, in certain conditions, was more than 71% accurate in predicting survival expectancy of lung cancer…

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AI 'Haven' in intensive care

Machine learning to identify deteriorating hospital patients

Researchers in Oxford have developed a machine learning algorithm that could significantly improve clinicians’ ability to identify hospitalised patients whose condition is deteriorating to the extent that they need intensive care. The HAVEN system (Hospital-wide Alerting Via Electronic Noticeboard) was developed as part of a collaboration between the University of Oxford’s Institute of…

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Detecting depression, psychosis

Machine learning could aid mental health diagnoses

A way of using machine learning to more accurately identify patients with a mix of psychotic and depressive symptoms has been developed by researchers at the University of Birmingham. Patients with depression or psychosis rarely experience symptoms of purely one or the other illness. Historically, this has meant that mental health clinicians give a diagnosis of a ‘primary’ illness, but with…

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Image-based diagnosis of Covid-19

AI detects coronavirus on CT scans

In order to detect the Corona virus SARS-CoV-2, there are further methods of diagnosis apart from the globally used PCR tests (Polymerase chain reaction): The infection can also be recognised on CT scans – for which Artificial Intelligence (AI) can be used as well. An AI system can not only filter CT scan of Covid-19 patients from a data set, but also estimate, which areas of the image are of…

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'Thumbs up' for image reconstruction

Facebook AI accelerates MRI exams

Artificial intelligence (AI) image reconstruction dramatically reduces magnetic resonance imaging (MRI) scan time, according to new research. The first clinical study comparing AI-accelerated knee MRI scans with conventional scans shows that the AI scans are not only diagnostically interchangeable with conventional ones, but also produce higher quality images. Results of this interchangeability…

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Machine learning advances diagnostics and prognostics

Computerized image analysis can predict cancer outcomes

The advent of digital pathology is offering a unique opportunity to develop computerized image analysis methods to diagnose disease and predict outcomes for cancer patients from histopathology tissue sections. Such advances can help predict risk of recurrence, disease aggressiveness and long-term survival, according to a leading expert in the field, Professor Anant Madabhushi from Case Western…

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Oversight of medical software

FDA Releases Artificial Intelligence/Machine Learning Action Plan

The U.S. Food and Drug Administration released the agency’s first Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan. This action plan describes a multi-pronged approach to advance the Agency’s oversight of AI/ML-based medical software. “This action plan outlines the FDA’s next steps towards furthering oversight for AI/ML-based SaMD,”…

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Drug repurposing

AI finds new uses for existing medications

Scientists have developed a machine-learning method that crunches massive amounts of data to help determine which existing medications could improve outcomes in diseases for which they are not prescribed. The intent of this work is to speed up drug repurposing, which is not a new concept – think Botox injections, first approved to treat crossed eyes and now a migraine treatment and top cosmetic…

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"InnerEye" Artificial Intelligence

AI could help cut waiting times for cancer radiotherapy

Doctors at Addenbrooke’s Hospital in Cambridge aim to drastically cut cancer waiting times by using artificial intelligence (AI) to automate lengthy radiotherapy preparations. The AI technology, known as InnerEye, is the result of an eight-year collaboration between researchers at Cambridge-based Microsoft Research, Addenbrooke’s Hospital and the University of Cambridge.

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