AI

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Identificación de mutaciones tumorales

El aprendizaje automático impulsa la medicina personalizada del cáncer

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. Este y otros desarrollos del mismo laboratorio buscan acelerar la investigación oncológica y ofrecer herramientas para que los oncólogos puedan elegir el mejor tratamiento para cada…

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Tool to identify tumour mutations

Machine learning fuels personalised cancer medicine

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…

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Software solution

Using AI to match cancer patients to early phase clinical trials

Cancer informatics and digital pathology provider Inspirata announced that King’s Health Partners ECMC and Guy’s and St Thomas’ NHS Foundation Trust will pilot its Trial Navigator software as part of an evaluation the organisations are conducting into how artificial intelligence based automation can improve the identification and efficiency of matching patients with cancer to early phase…

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Industry analysis

AI and cloud technology crucial for medical imaging & informatics market

The healthcare sector’s emphasis on meeting its Quadruple Aim goal—cost reduction, clinical outcomes, enhance the patient and caregiver experience, and improve the work-life of healthcare providers—encourages the medical imaging and informatics industry to use advanced technologies to tap into growth prospects. Frost & Sullivan’s recent analysis, Global Medical Imaging &…

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Intensive care & AI

Machine learning model predicts ICU patients' mortality risk

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…

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Personalizing treatment

AI can help improve precision radiotherapy

The Netherlands Cancer Institute, University of Amsterdam (UvA), and Elekta will collaborate on the development of new AI strategies for the further improvement of precision radiotherapy. This concerns the personalization of treatment by improving the quality of imaging used during treatment, predicting and accounting for changes in the patient’s anatomy over time, and automatically adapting…

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CT, MRI, X-ray, AI, PACS, endoscopy, ultrasound

Fujifilm Healthcare acquires Hitachi Diagnostic Imaging, presents new portfolio

At a virtual European event, Fujifilm Healthcare Europe presented a complete and integrated portfolio of diagnostic products and services, including CT, MRI, X-ray, AI, PACS, endoscopy and ultrasound systems. This launch follows the completion of Fujifilm's acquisition and takeover of Hitachi's Diagnostic Imaging-related business on 31 March 2021 for 179 billion yen (€1.3 billion).

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Screening, early detection, treatment optimisation

AI techniques advancing oncology care

Cancer care and the treatment clinicians can offer patients is being increasingly enhanced by Artificial Intelligence (AI). The technology has a role in diagnosis, with algorithms trained to design and deliver patient care, can match patients to clinical trials they may benefit from, and even help predict outcomes and those at greatest risk.

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Heard at SIIM 2021

AI in radiology: unexpected benefits, unintended consequences

Artificial intelligence (AI) could match the impact of PACS on radiology. Covid-19 stimulated the development and testing of AI diagnostic-aiding tools in radiology, an unintended consequence of the pandemic. More image data sets have been created to train AI software – an unexpected benefit for radiology research. The Samuel Dwyer Memorial Lecture at the virtual 2021 Society of Imaging…

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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 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).…

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Deep learning vs Aids

AI app could help diagnose HIV more accurately

Pioneering technology developed by University College London (UCL) and Africa Health Research Institute (AHRI) researchers could transform the ability to accurately interpret HIV test results, particularly in low- and middle-income countries. Academics from the London Centre for Nanotechnology at UCL and AHRI used deep learning (artificial intelligence/AI) algorithms to improve health workers’…

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CADU vs cancer

Detecting oesophageal cancer with AI

Experts at University College London (UCL) and spinout company Odin Vision working with clinicians at UCLH have used artificial intelligence (AI) to help detect early signs of oesophageal cancer. The first procedure in the world using the AI technology was performed at University College Hospital by UCLH consultant gastroenterologist Dr Rehan Haidry. The system, called CADU, uses AI to support…

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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, and use, according to new WHO guidance. The report, Ethics and governance of artificial intelligence for health, is the result of 2 years of consultations held by a panel of international experts…

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Preprogrammed bias?

AI and the gender gap: Data holds a legacy of discrimination

Technologies based on artificial intelligence (AI) are considered the epitome of progress. However, the data AI algorithms use to draw their conclusions is outdated. It ignores the existence of biological sex and socio-cultural gender and their effects on individual health and disease states. Thus, the ‘thinking machines’ not only reproduce discriminating bias and prejudices but also produce…

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Study on devices and implants

AI could improve speech recognition in hearing aids

In noisy environments, it is difficult for hearing aid or hearing implant users to understand their conversational partner because current audio processors still have difficulty focusing on specific sound sources. In a feasibility study, researchers from the Hearing Research Laboratory at the University of Bern and the Inselspital are now suggesting that artificial intelligence (AI) could solve…

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Detecting Coronavirus

AI vs. Covid-19: ‘Barcode’ brings quicker test results

When patients are admitted to a hospital emergency room (ER) it is immediately vital to determine whether s/he has Covid-19. However, with a regular PCR test a result can take up to a few hours. Thus, initially, the patient must be isolated. During the height of the corona pandemic last year, researcher Ruben Deneer from Eindhoven University of Technology (TU/e) and clinical chemist Arjen-Kars…

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Workflow optimisation

The potential of AI in breast imaging efficiency

The contribution of Artificial intelligence (AI) has great potential in breast imaging efficiency, Professor Linda Moy MD told attendees at the 2021 Society of Breast Imaging/American College of Radiology (SBI/ACR) Breast Imaging Symposium this April. AI models for breast imaging have focused mainly on the diagnostic classification and detection of breast cancer.

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AI-assisted analysis

Prediciting viral infections with microscopy & deep learning

When viruses infect cells, changes in the cell nucleus occur, and these can be observed through fluorescence microscopy. Using fluoresence images from live cells, researchers at the University of Zurich have trained an artificial neural network to reliably recognize cells that are infected by adenoviruses or herpes viruses. The procedure also identifies severe acute infections at an early stage.

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Medication development platform

Smart biomarkers to find new drugs against brain diseases

Dr. Hayder Amin and Dr. Caghan Kizil from the DZNE’s Dresden site aim to speed up developing drugs against brain diseases through cutting-edge technology. To this end, they are generating an innovative technology platform, termed “i3D-Markers”, based on high-density microelectrode arrays and 3-dimensional networks of human neurons. Compounds to be tested will be dripped onto this setup, and…

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