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Digital Twin

Modeling the human liver

Colleague Chloé Audigier is developing a digital twin of the liver. We met up with her to get some insights into this project and learn more about Chloé’s motivation to come to work every day.
8min
Romy Albrecht
Published on December 20, 2022

Liver conditions like tumors or fatty liver disease are affecting an increasing number of people. Liver cancer alone is the third deadliest form of cancer worldwide with only a 16% five-year survival rate. But there are many stages in liver disease and patients could be treated if they were diagnosed early enough. So there is a need for both early diagnosis and better therapy options. Chloé wants to do her part in meeting this need.

What’s the five-year survival rate?

The percentage of people in a study or treatment group who are alive five years after they were diagnosed with or started treatment for a disease, such as cancer. The disease may or may not have come back. (Source: www.cancer.gov)
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Picture of Chloé Audigier

I think it’s very rewarding to try and apply science to build solutions that could help patients live longer and healthier lives.

Chloé Audigier, Senior AI Research Scientist at Siemens Healthineers

Based in Switzerland, Chloé is one of our Senior AI Research Scientists. Currently, she’s working on a project developing a digital twin of the human liver: “Digital twins are computer models that aim to represent for example an organ of a patient in an individualized, accurate and virtual way,” she explains. “Such models could help clinicians simulate or test several therapy options virtually before performing them on the actual patient.”

Changing the way of providing care

Yet that’s not how most patients are treated today, as Chloé explains:
Chloé’s model considers different liver conditions, like tumors or fatty liver disease. In the case of liver cancer, for example, an option is to remove the cancerous cells through surgery. The surgeons could use a digital twin to virtually perform the operation and assess the patient’s remaining liver function, which is a crucial to understanding whether it’s safe to perform the surgery on the patient.

Creating a digital twin

In order to accurately represent a patient’s liver, a lot of data has to go into its digital twin:

Utilizing math and computational modeling to create better healthcare

Chloé’s background is in mathematics and computational modeling: “After my master, I wanted to continue working in science research where I could have a tangible positive impact. So for me it was a very natural step to find a PhD with a focus on healthcare,” she explains. “When I found this PhD position at INRIA in collaboration with Siemens Healthineers, I knew it would be the perfect match. During my post doc, I also specialized in physiological modeling and deep learning.” 

In her daily work, Chloé collaborates with a lot of different people: “I really like that we work in an international team where everyone has different backgrounds,” she says. “And I like the interaction because we learn so much from each other.” Chloé enjoys reading the latest research in science and medicine, and likes to try and apply new algorithms to develop new ideas and technology that could help clinicians and patients.

And what’s the most rewarding part of her work?

What is INRIA?

INRIA is the French national research institute for digital science and technology.
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Want to learn more about Chloé’s work? Then tune in to our latest podcast episode where we take a deep dive into digital twins in cancer care:

Digital twins in cancer care
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Digital twins in cancer care
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Find out how digital twins could transform the entire cancer care path – from early detection to treatment and follow-up care. You’ll also learn how a digital twin of an organ is created. And you’ll hear what digital twin technology could mean for patients, clinicians, and the future of oncology.
Romy Albrecht
Romy Albrecht
By Romy Albrecht
Romy Albrecht is a digital editor and multimedia content creator at Siemens Healthineers, specializing in technology and innovation topics.

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  1. The concepts and information presented here are based on research results that are not commercially available. Further commercial availability cannot be guaranteed.

  2. The persons present at the shooting were fully vaccinated against COVID-19 or tested negative for SARS-CoV-2 coronavirus infections.