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Artificial Intelligence Homepage

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Dr. Mathias Goyen

Tackling liver complications with the help of artificial intelligence and advanced technology

By Dr. Mathias Goyen

Since ancient times, humans have been captivated by one of the body’s most complex and fundamental organs: the liver. In fact, the Greeks considered this vital organ intrinsically linked to the divine presence.

The fascination is understandable; the liver performs more than 500 major bodily functions, including breaking down food, protecting the body from toxins, and filtering blood. A strong liver is critical to our overall health, which is why recent global trends are disturbing.
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With the rise of obesity, approximately one in three adults in the developed world suffers from nonalcoholic fatty liver disease, which can lead to liver cirrhosis or even cancer. In the United States between 1999 and 2016, deaths due to liver cirrhosis rose 65 percent and liver cancer mortality rates doubled.

But these trends don’t have to continue.

Today, Artificial Intelligence (AI) – the technology that mimics the human brain – is changing the way doctors navigate the liver and helping patients manage their liver disease. By interpreting petabytes of hospital data that previously went unanalyzed, AI has the potential to see the unseen, answer questions that have never even been asked, and consume information previously impossible for clinicians to digest.

Take diagnostics, for example. Until recently, the best tests to diagnose and assess liver disease and cancer severity were blood tests and painful liver biopsies, where a clinician inserts a small needle into the liver to collect a tissue sample. Now, ultrasound allows clinicians to assess the liver in a non-invasive and a non-ionizing radiation way to characterize tissue. Hepatologists and radiologists can qualitatively and quantitatively assess and identify liver stiffness – a key indicator of liver disease. In addition, AI technology eliminates the need for focal zones, so that the entire image is always in focus throughout the exam. With this improved image quality, clinicians can detect tiny lesions in the liver that they may not have seen previously. This could give clinicians improved diagnostic confidence, which could lead to an earlier diagnosis and better outcomes for the patient.

Additionally, Computed Tomography (CT) is growing faster in image processing, thanks to AI computing platforms. They are expected to deliver better clinical outcomes in liver lesion detection because of this speed – potentially reducing the need for unnecessary follow-ups, benefitting patients with compromised renal function, and reducing non-interpretable scans with Gemstone Spectral Imaging Metal Artifact Reduction (GSI MAR).
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