Fujitsu, Kyoto University Develop Explainable AI Verification System


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Fujitsu Limited and a Kyoto University research group led by Professor Yasushi Okuno of the Kyoto University Graduate School of Medicine, has developed an AI verification system called "MGeND Intelligence." When the genetic mutation information of a patient is input into this system, its ability to cause disease is estimated by pathogenicity estimation AI using machine learning technology and the explanatory text of the basis of this finding is generated and displayed together with the estimated result by the explainable AI. This explanation offers a useful reference to doctors who are considering a treatment plan or genomic medical researchers. The system draws from the "Integrated Database of Clinical and Genomic Information program, managed by the Japan Agency for Medical Research and Development , and from April 2021, Kyoto University plans to offer MGeND Intelligence to joint researchers and institutions.

In addition to the pathogenicity estimation AI, the MGeND Intelligence verification system includes explainable AI that shows the basis of the system’s findings, as well as literature search AI that supports the retrieval of related articles. Working with the Integrated Database of Clinical and Genomic Information, "MGeND," which was made public by Kyoto University in 2018, Fujitsu supports the research and clinical interpretation of genetic mutations, including mutations with unknown pathogenicity, by medical professionals and researchers.

The use of this verification system ultimately offers the potential to deliver greater innovations in medical care for areas including treatment planning for genomic therapies for illnesses like cancer, accelerating the optimization of patient-centric medical care.

Background

In genomic medicine, knowledge about whether a genetic mutation in a patient has the ability to cause disease is essential. If researchers can locate a pathogenic mutation in a patient's gene, they can consider a treatment for that specific mutation. However, only a small percentage of the vast number of possible genetic mutations have been linked to disease. Genetic mutations with unknown pathogenicity present a challenge in that they do not provide useful information for treating disease.

Since November 2016, Fujitsu and Kyoto University have been participating in the Integrated Database of Clinical and Genomic Information Program promoted by the Japan Agency for Medical Research and Development, engaging in research and development to support the work of medical professionals and researchers in examining genetic mutations with AI and machine learning technologies. Fujitsu and Kyoto University have now developed an AI system that can estimate the presence or absence of disease causing potential for unknown genetic mutations and explain the basis for this finding. Kyoto University will make MGeND Intelligence available to joint researchers and collaborating institutions.

Characteristics of the MGeND Intelligence verification system

"MGeND Intelligence" is a verification system jointly developed by Fujitsu and Kyoto University, designed to support researchers studying genetic mutations. This was achieved through the application of three technologies possessed by Fujitsu Laboratories, Ltd.: machine learning technology trained on graph structure data that can even express complex events; explainable AI technology that can clarify the basis of findings; and natural language processing technology that can accurately extract relationships such as mutations and diseases from a large corpus of textual data.

When the genetic mutation information of a patient is input into the system, its ability to cause disease is estimated by the pathogenicity estimation AI using machine learning technology, and the explanatory text of the basis of this finding is generated and displayed together with the estimated result by the explainable AI. In addition, medical professionals and researchers can gather the basis of their estimation results by themselves. Information in public databases related to mutations and the certainty of estimation by AI can be displayed by multi-faceted visualization together with information on surrounding mutations, while researchers can use the literature search support AI, which leverages natural language processing technology, to search for articles related to target mutations and identify and display the descriptions.

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