• Using AI to detect cancer from patient d

    From ScienceDaily@1:317/3 to All on Mon Apr 25 22:30:42 2022
    Using AI to detect cancer from patient data securely

    Date:
    April 25, 2022
    Source:
    University of Leeds
    Summary:
    A new way of using artificial intelligence to predict cancer from
    patient data without putting personal information at risk has
    been developed.

    Swarm learning can be used to help computers predict cancer in
    medical images of patient tissue samples, without releasing the
    data from hospitals.



    FULL STORY ==========================================================================
    A new way of using artificial intelligence to predict cancer from patient
    data without putting personal information at risk has been developed by
    a team including University of Leeds medical scientists.


    ========================================================================== Artificial intelligence (AI) can analyse large amounts of data, such as
    images or trial results, and can identify patterns often undetectable
    by humans, making it highly valuable in speeding up disease detection, diagnosis and treatment.

    However, using the technology in medical settings is controversial because
    of the risk of accidental data release and many systems are owned and controlled by private companies, giving them access to confidential
    patient data -- and the responsibility for protecting it.

    The researchers set out to discover whether a form of AI, called swarm learning, could be used to help computers predict cancer in medical images
    of patient tissue samples, without releasing the data from hospitals.

    Swarm learning trains AI algorithms to detect patterns in data in a
    local hospital or university, such as genetic changes within images of
    human tissue.

    The swarm learning system then sends this newly trained algorithm -- but importantly no local data or patient information -- to a central computer.

    There, it is combined with algorithms generated by other hospitals in
    an identical way to create an optimised algorithm. This is then sent
    back to the local hospital, where it is reapplied to the original data, improving detection of genetic changes thanks to its more sensitive
    detection capabilities.

    By undertaking this several times, the algorithm can be improved and one created that works on all the data sets. This means that the technique
    can be applied without the need for any data to be released to third party companies or to be sent between hospitals or across international borders.

    The team trained AI algorithms on study data from three groups of patients
    from Northern Ireland, Germany and the USA. The algorithms were tested
    on two large sets of data images generated at Leeds, and were found to
    have successfully learned how to predict the presence of different sub
    types of cancer in the images.

    The research was led by Jakob Nikolas Kather, Visiting Associate Professor
    at the University of Leeds' School of Medicine and Researcher at the
    University Hospital RWTH Aachen. The team included Professors Heike
    Grabsch and Phil Quirke, and Dr Nick West from the University of Leeds'
    School of Medicine.

    Dr Kather said: "Based on data from over 5,000 patients, we were able to
    show that AI models trained with swarm learning can predict clinically
    relevant genetic changes directly from images of tissue from colon
    tumors." Phil Quirke, Professor of Pathology in the University of Leeds's School of Medicine, said: "We have shown that swarm learning can be used
    in medicine to train independent AI algorithms for any image analysis
    task. This means it is possible to overcome the need for data transfer
    without institutions having to relinquish secure control of their data.

    "Creating an AI system which can perform this task improves our ability
    to apply AI in the future."

    ========================================================================== Story Source: Materials provided by University_of_Leeds. Note: Content
    may be edited for style and length.


    ========================================================================== Journal Reference:
    1. Saldanha, O.L., Quirke, P., West, N.P. et al. Swarm learning for
    decentralized artificial intelligence in cancer histopathology. Nat
    Med, 2022 DOI: 10.1038/s41591-022-01768-5 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/04/220425121049.htm

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