idw – Informationsdienst Wissenschaft

Nachrichten, Termine, Experten

Grafik: idw-Logo
Science Video Project
idw-Abo

idw-News App:

AppStore

Google Play Store



Instance:
Share on: 
07/06/2023 13:05

Fighting bowel cancer with AI

Anja Stübner Pressestelle
Technische Universität Dresden

    The DECADE research project is the first to use swarm learning in cancer research. The German Cancer Aid is funding the joint project of several university hospitals with around 1.5 million euros.

    Bowel cancer is one of the most common and deadly types of cancer in Germany. Every year, around 58,000 people are diagnosed with the disease. If detected early, bowel cancer is easily curable. However, despite significant advances in screening and treatment, doctors still face challenges in diagnosis and prognosis. This is where DECADE - Decentralized artificial intelligence for diagnosis, prognosis and response prediction in Colorectal Cancer - comes in. Several German university hospitals are working together to investigate how the use of artificial intelligence (AI) and swarm learning (SL) can significantly improve the care and treatment of colorectal cancer patients in both early and advanced stages. AI is already capable of analyzing large amounts of data and recognizing certain patterns. The insights gained from this can help to better predict the course of the disease or to make more individualized diagnoses. The aim of this research project is to use AI and SL to significantly improve the treatment of colorectal cancer patients.

    Prof. Jakob N. Kather, project leader and Professor of Clinical Artificial Intelligence at the Else Kröner Fresenius Centre for Digital Health at the TU Dresden and the University Hospital Dresden, said at the start of the project: "AI tools have so far only been used hesitantly in routine clinical practice. One reason is that data exchange between hospitals is severely restricted by legal and ethical hurdles, especially in Germany. One solution to this problem is swarm learning. With swarm learning, several institutions can jointly train medical AI models without exchanging data. By using decentralized artificial intelligence and swarm learning, hope to improve diagnosis, prognosis and treatment planning for colorectal cancer patients.”

    Training AI with decentralized patient data

    In cancer research, privacy laws and ethical hurdles make it difficult to share sensitive patient data between different research institutions, even though many patients are in principle in favor of their data being used for research purposes. Swarm learning makes it easier to meet privacy requirements. Swarm learning is a special form of machine learning in which models are trained without exchanging actual data between participants. The coordination and merging of models is done via a blockchain, eliminating the need for a central instance. The DECADE project builds on this method to use SL-based AI technology to solve real-world clinical problems related to colorectal cancer. "The legal requirements for protecting sensitive health data are high. This innovative method of swarm learning allows the benefits of collaboration and knowledge transfer between different research institutions to be realized without violating privacy regulations. In this way, AI models in cancer research can be further developed and improved to enable better diagnosis, prognosis and personalized treatment approaches for cancer patients," said Prof. Tom Lüdde, Director of the Department of Gastroenterology, Hepatology and Infectious Diseases at the University Hospital Düsseldorf. The project partners will use SL to develop AI algorithms for diagnosing and subtyping colorectal cancer and predicting disease progression. In doing so, they are setting a precedent for the use of SL in medicine that can serve as a template for any AI system in the healthcare sector. After all, more powerful AI systems could help doctors detect bowel cancer at an earlier stage and treat it more effectively. This could support medical staff and improve the care and treatment of colorectal cancer patients.

    Background information:
    The research project DECADE - Decentralized artificial intelligence for diagnosis, prognostication and response prediction in Colorectal Cancer - is funded by the German Cancer Aid with approximately 1.5 million euros over three years (2023-2026). The project partners are the university hospitals in Bonn, Dresden, Düsseldorf, Heidelberg and Mainz. The aim of the research project is to use artificial intelligence and swarm learning to improve the treatment of colorectal cancer patients.

    Project partners:
    • Else Kröner Fresenius Center for Digital Health, TU Dresden, University Hospital Dresden
    • University Hospital Heidelberg, Institute of Pathology, Department of Applied Tumor Biology
    • University Hospital Mainz, Institute of Pathology
    • University Hospital Bonn, Medical Clinic and Polyclinic 1, National Center for Hereditary Tumor Syndromes
    • University Hospital Düsseldorf, Department of Gastroenterology, Hepatology and Infectious Diseases

    Contact:
    EKFZ for Digital Health
    Public Relations
    Anja Stübner
    0351 458 11 379
    anja.stuebner@ukdd.de
    www.digitalhealth.tu-dresden.de


    Images

    Prof. Jakob N. Kather
    Prof. Jakob N. Kather

    EKFZ


    Criteria of this press release:
    Journalists
    Information technology, Medicine
    transregional, national
    Research projects, Science policy
    English


     

    Prof. Jakob N. Kather


    For download

    x

    Help

    Search / advanced search of the idw archives
    Combination of search terms

    You can combine search terms with and, or and/or not, e.g. Philo not logy.

    Brackets

    You can use brackets to separate combinations from each other, e.g. (Philo not logy) or (Psycho and logy).

    Phrases

    Coherent groups of words will be located as complete phrases if you put them into quotation marks, e.g. “Federal Republic of Germany”.

    Selection criteria

    You can also use the advanced search without entering search terms. It will then follow the criteria you have selected (e.g. country or subject area).

    If you have not selected any criteria in a given category, the entire category will be searched (e.g. all subject areas or all countries).