Could machine learning (ML) help to predict sudden cardiac death (SCD)? According to Dr Sanjiv Narayan, Professor of Medicine at Stanford University, California, many exciting studies are using ML to predict sudden death in ways not previously possible. ‘Complex data, such as MRI geometry, very large electronic health records or continuous data streams from wearables, are difficult to probe…
El laboratorio de Genómica Biomédica del IRB Barcelona (Institute for Research in Biomedicine) ha desarrollado un método computacional que identifica las mutaciones causantes del cáncer para cada tipo de tumor.
The Biomedical Genomics laboratory at the Institute for Research in Biomedicine (IRB) Barcelona has developed a computational tool that identifies cancer driver mutations for each tumour type. This and other developments produced by the same lab seek to accelerate cancer research and provide tools to help oncologists choose the best treatment for each patient. The study has been published in the…
A research team at Universitat Autònoma de Barcelona (UAB), in collaboration with the Hospital de Mataró, developed a new machine learning-based model that predicts the risk of mortality of intensive care unit patients according to their characteristics. The research was published in the latest edition of the journal Artificial Intelligence in Medicine, with a special mention as a…
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 and predict the outcome of stroke victims. When blood flow to the brain is somehow reduced or restricted, a person can suffer what we know as a stroke (from “ischemic stroke” in medical jargon).…
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, and use, according to new WHO guidance.
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 diagnostic errors if deployed in clinical settings.
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”.
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 Hewlett Packard Enterprise’s (HPE) GreenLake for Machine Learning Operations (ML Ops). The machine-learning-optimized cloud service infrastructure makes it easier and faster to get started with ML/AI…
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 timing and weather data, finds research published online in the journal Heart. Machine learning is the study of computer algorithms, and based on the idea that systems can learn from data and identify…
Robotics, AI, and machine learning can make spinal surgery more accurate, efficient, and safer, thereby reducing costs, patient recovery time, and radiation exposure.
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
Predicting cancer outcome could help with a clinical decision regarding a patient’s treatment. In his keynote speech during the online ‘7th Digital Pathology and AI Congress: Europe’, Johan Lundin, Research Director at the Institute for Molecular Medicine Finland (FIMM) at the University of Helsinki and Professor of Medical Technology at Karolinska Institute, discussed ‘Outcome and…
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…
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,”…
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…
