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    Home > Biochemistry News > Biotechnology News > Nat Med uses artificial intelligence to safely detect cancer from patient data

    Nat Med uses artificial intelligence to safely detect cancer from patient data

    • Last Update: 2022-05-13
    • Source: Internet
    • Author: User
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    Artificial intelligence (AI) can analyze large amounts of data, such as images or test results, and identify patterns that are often undetectable by humans, making it extremely valuable in accelerating disease detection, diagnosis and treatment
    .

    However, the use of the technology in a healthcare setting is controversial because of the risk of accidental data breaches and the fact that many systems are owned and controlled by private companies that have access to confidential patient data and a responsibility to protect it
    .

    Researchers are beginning to investigate whether a type of artificial intelligence known as swarm learning can help computers predict cancer in medical images of patient tissue samples without releasing hospital data
    .

    Swarm learning trains artificial intelligence algorithms to detect patterns in local hospital or university data, such as genetic changes in images of human tissue
    .
    The swarm learning system then sends this newly trained algorithm — but importantly, without local data or patient information — to a central computer

    .
    There, it combines with algorithms generated in the same way by other hospitals to form an optimized algorithm

    .
    The data is then sent back to the local hospital, where it is reapplied to the original data, improving detection of genetic changes due to its more sensitive detection capabilities

    .

    By doing this multiple times, the algorithm can be improved and one can be created that works on all datasets
    .
    This means that the technology can be applied without any data being released to third-party companies, or transmitted between hospitals or across international borders

    .

    The team trained the AI ​​algorithm on study data from three groups of patients in Northern Ireland, Germany and the United States
    .
    The algorithm was tested on two sets of large data images generated in Leeds and found that it successfully learned how to predict the presence of different subtypes of cancer in the images

    .

    The research was led by Jakob Nicholas Kaiser, Visiting Associate Professor at the University of Leeds School of Medicine and Research Fellow at RWTH Aachen University Hospital
    .
    The research team included Professors Heike grabch and Phil Quirke, and Dr Nick West from the University of Leeds Medical School

    .

    "Based on data from more than 5,000 patients, we were able to demonstrate that an AI model trained with swarm learning could predict clinically relevant genetic changes directly from images of colon tumor tissue," said Dr.
    Kaiser

    .

    Phil Quark, Professor of Pathology at the University of Leeds School of Medicine, said: "We have shown that, in medicine, swarm learning can be used to train stand-alone AI algorithms for any image analysis task
    .
    It is possible to overcome the need for data transfer without the security control of its data

    .

    "Creating an AI system that can accomplish this task could improve our ability to apply AI in the future
    .
    "

    Journal Reference :

    1. Saldanha, OL, Quirke, P.
      , West, NP et al.
      Swarm learning for decentralized artificial intelligence in cancer histopathology .
      Nat Med , 2022 DOI: 10.
      1038/s41591-022-01768-5


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