AI with swarm intelligence

May 26, 2021

Prof. Joachim Schultze, Director of Systems Medicine at the DZNE and professor at the Life & Medical Sciences Institute (LIMES) at the University of Bonn. (c) DZNE/Frommann

 

Novel technology for cooperative analysis of big data

Communities benefit from sharing knowledge and experience among their members. Following a similar principle - called "swarm learning" - an international research team has trained artificial intelligence algorithms to detect blood cancer, lung diseases and COVID-19 in data stored in a decentralized fashion.

Cluster member Prof. Joachim Schultze from the DZNE and LIMES Institute is lead author of this study.

This approach has advantage over conventional methods since it inherently provides privacy preservation technologies, which facilitates cross-site analysis of scientific data. Swarm learning could thus significantly promote and accelerate collaboration and information exchange in research, especially in the field of medicine. Experts from the DZNE, the University of Bonn, the information technology company Hewlett Packard Enterprise (HPE) and other research institutions report on this in the scientific journal "Nature".

Analyzing the resulting volumes of information - known as "big data" - is considered a key to better treatment options. "Medical research data are a treasure. They can play a decisive role in developing personalized therapies that are tailored to each individual more precisely than conventional treatments," said Joachim Schultze, Director of Systems Medicine at the DZNE and professor at the Life & Medical Sciences Institute (LIMES) at the University of Bonn.

"It's critical for science to be able to use such data as comprehensively and from as many sources as possible." However, the exchange of medical research data across different locations or even between countries is subject to data protection and data sovereignty regulations. In practice, these requirements can usually only be implemented with significant effort. In addition, there are technical barriers: For example, when huge amounts of data have to be transferred digitally, data lines can quickly reach their performance limits. In view of these conditions, many medical studies are locally confined and cannot utilize data that is available elsewhere.

In light of this, a research collaboration led by Joachim Schultze tested a novel approach for evaluating research data stored in a decentralized fashion. The basis for this was the still young "Swarm Learning" technology developed by HPE. In addition to the IT company, numerous research institutions from Greece, the Netherlands and Germany - including members of the "German COVID-19 OMICS Initiative" (DeCOI) - participated in this study.

Only algorithms and parameters are shared - in a sense, lessons learned. "Swarm Learning fulfills the requirements of data protection in a natural way," Joachim Schultze emphasized. Unlike "federated learning", in which the data also remains locally, there is no centralized command center, the Bonn scientist explained. "Swarm Learning happens in a cooperative way based on rules that all partners have agreed on in advance. This set of rules is captured in a blockchain."

The researchers are now providing practical proof of this approach through the analysis of X-ray images of the lungs and of transcriptomes: The latter are data on the gene activity of cells. In the current study, the focus was specifically on immune cells circulating in the blood - in other words, white blood cells. "Data on the gene activity of blood cells are like a molecular fingerprint. They hold important information about how the organism reacts to a disease," Schultze said. "Transcriptomes are available in large numbers just like X-ray images, and they are highly complex. This is exactly the kind of information you need for artificial intelligence analysis. Such data is perfect for testing Swarm Learning."

The research team addressed a total of four infectious and non-infectious diseases: two variants of blood cancer (acute myeloid leukemia and acute lymphoblastic leukemia), as well as tuberculosis and COVID-19. The data included a total of more than 16,000 transcriptomes.

The current study was just a test run. In the future, we intend to apply this technology to Alzheimer's and other neurodegenerative diseases," Schultze said. "Swarm Learning has the potential to be a real game changer and could help make the wealth of experience in medicine more accessible worldwide. Not only research institutions but also hospitals, for example, could join together to form such swarms and thus share information for mutual benefit."

Find here the german and english press release.

Publication:
S. Warnat-Herresthal et al.: Swarm Learning for decentralized and confidential clinical machine learning. Nature

Contact:

Prof. Dr. Joachim L. Schultze
German Center for Neurodegenerative Diseases; LIMES Institute at the University of Bonn
Phone: +49 228 43302-410
Email: joachim.schultze@dzne.de