Health
Use AI to predict cancer from patient data without jeopardizing personal information
A new method of predicting cancer from patient data using artificial intelligence without jeopardizing personal information has been developed by a team of medical scientists at the University of Leeds.
Artificial intelligence (AI) can analyze large amounts of data, such as images and test results, and identify patterns that are often undetectable by humans, helping to speed up disease detection, diagnosis, and treatment.
However, the use of technology in the medical setting is controversial because of the risk of accidental disclosure of data. Many systems are owned and controlled by private companies and are responsible for accessing and protecting sensitive patient data.
Researchers use a form of AI called swarm learning to try to find out if a computer can help predict cancer in medical images of a patient’s tissue sample without exposing data from the hospital. I did.
Swarm Learning trains AI algorithms to detect patterns in local hospital and university data, such as genetic changes in images of human tissues. The herd learning system then sends this newly trained algorithm to a central computer, but not local data or patient information. There, it is combined with algorithms generated by other hospitals in the same way to create optimized algorithms. It is then sent back to the local hospital where it is reapplied to the original data, which improves the detection of genetic changes thanks to its more sensitive detection capabilities.
By doing this a few times, you can improve the algorithm and create an algorithm that works for all datasets. This means that you can apply this technique without releasing the data to third-party companies or sending it across hospitals or across borders.
Cancer prediction
The team trained AI algorithms based on study data from three groups of patients in Northern Ireland, Germany, and the United States. The algorithm was tested on two large data image sets generated in Leeds and found to have successfully learned how to predict the presence of different subtypes of cancer in the images.
The study was led by Jakob Nikolas Kather, a visiting associate professor at the University of Leeds School of Medicine and a researcher at the University Hospital RWTH Aachen. The team included Professors Heike Grabsch and Phil Quirke, and Dr. Nick West of the University of Leeds School of Medicine.
Dr. Cather said: “Based on data from more than 5,000 patients, we were able to show that herd-trained AI models can predict clinically relevant genetic changes directly from images of colon tumor tissue.”
We have shown that swarm learning can be used in medicine to train independent AI algorithms for image analysis tasks. This means that educational institutions can overcome the need for data transfer without giving up secure control of their data. Creating an AI system that can perform this task will improve your ability to apply AI in the future. “
Phil Quirke, Professor of Pathology, University of Leeds School of Medicine
Source:
Journal reference:
Cerdanya, OL, et al. (2022) Herd learning for distributed artificial intelligence in histopathology of cancer. Nature Medicine. doi.org/10.1038/s41591-022-01768-5..
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