All over the world, a slow, insidious change is taking place in the composition of the livers of more than a billion people.
While the presence of fat in a normal, healthy liver is negligible, many adults and even children have livers where fat exceeds 5 percent or even 10 percent of the organ’s total weight. Its unnatural presence causes inflammation, cell damage, and the formation of scar tissue known as fibrosis, all hallmarks of fatty liver disease, a condition that now impacts approximately 30 percent of adults worldwide.
If left to linger, progressive fat accumulation can ultimately lead to liver failure, and it has been linked to an increased risk of cardiovascular disease and various cancers. But the condition typically develops without noticeable symptoms, so it is rarely detected at an early and treatable stage. Even in cases of cirrhosis or advanced scarring of the liver, three-quarters of people are only diagnosed once their condition has become life-threatening.
As a result, growing numbers of specialists are investigating how AI might help. Jeffrey Lazarus, a professor at the CUNY Graduate School of Public Health and Health Policy, suggests that AI tools could be used to comb through vast numbers of electronic health records and pinpoint the people most likely to have accrued worrying amounts of liver fat.
“AI can retrospectively go through massive numbers of hospital visits and lab reports,” says Lazarus. “You can use that to really prioritize who’s at most risk.”
If fatty liver disease can be identified early, much of the damage is highly reversible. In the initial stages, lifestyle changes such as reducing alcohol intake, losing weight through dietary improvements and exercise, and even drinking more coffee have all been shown to be capable of reversing scarring and inflammation. Even for people with moderate to advanced liver scarring, novel treatments such as the GLP-1 medication semaglutide and a drug called resmetirom have been shown to be highly effective therapeutics.
“The liver is a very versatile and interesting organ because it can regenerate, fibrosis can be reversed, and you can be completely healthy again,” says Lazarus. “But traditionally, we’ve focused more on late-stage care and trying to see how long we can keep a patient alive rather than finding them early and preventing the condition from advancing.”
One of the frustrations for Lazarus and others is that while simple, noninvasive means of assessing liver health exist, they are rarely used, even in people known to be at a much higher risk of the consequences of fatty liver disease, such as those with obesity and type 2 diabetes.
The Fib-4 index, for example, rates a person’s risk of advanced liver fibrosis through computing a score of between 0 and 6, based on their age, their levels of two liver enzymes, and their blood-clotting ability. This requires physicians to run a liver blood test, something which is often carried out as part of an annual medical checkup in the US. Doctors also have access to a more accurate, second-line blood test known as the enhanced liver fibrosis test, which measures the levels of two proteins involved in creating scar tissue and an enzyme which inhibits the clearance of scars in the liver.
Using both of these tests in patients with worrying amounts of liver fat has been shown to improve the diagnosis of those with advanced fibrosis by four-fold. But for physicians facing a growing workload and administrative burden, simply adding more testing to the workflow is not felt to be a sustainable option.
“You have to have something that can run in the background or it’s easy to just hit a button and do it,” says Jonathan Dranoff, a professor of medicine at Yale University.
As a result, Dranoff and Lazarus both foresee a role for AI in taking existing data from routine blood testing and automating the calculation of Fib-4 scores, making it easier for primary care physicians to identify the right patients to refer to a liver specialist.
AI could also do this using x-ray images. Last year, scientists at Osaka Metropolitan University in Japan published a study using an AI model capable of ingesting and analyzing routine chest x-ray scans. While primarily intended to examine the lungs and the heart, these images also pick up parts of the liver. The researchers found that their model was capable of identifying people with fatty liver disease with an accuracy of 82 percent.
Lazarus suggests that AI-powered algorithms could be incorporated into all routine x-ray analyses where the liver happens to be scanned alongside other organs. “The AI could pick up cases of excess liver fat, check for other risk factors such as if the person is overweight, has high cholesterol or type 2 diabetes, and then make a recommendation to the doctor,” says Lazarus. “It could tell them, ‘This might not have been what you were looking for, but this is what was picked up, and you should refer to hepatology or endocrinology who can do further tests.’”
Training AI algorithms on routine blood tests is also beginning to yield better diagnostics for progressive fatty liver disease. While Fib-4 is a quick and low-cost tool, it is less accurate in certain age groups, such as adolescents and seniors, and studies have raised concerns about rates of false positives and unnecessary referrals unless combined with additional testing.
As a result, the Danish health tech startup Evido has developed an AI-powered algorithm called LiverPRO which can assess a patient’s risk of liver fibrosis based on their age and nine routine blood-based biomarkers. Now being commercialized in partnership with the pharmaceutical company Roche, it has been shown to outperform Fib-4 in predicting risk of serious liver problems in more than 470,000 middle-aged people.
Other AI-based tests could also help doctors select the most appropriate patients for resmetirom treatment without requiring them to undergo an invasive liver biopsy. Earlier this year, an international collective of hepatologists published the results of a study evaluating an AI model called ALADDIN—another machine learning algorithm based on routine blood tests—that showed it performed better than Fib-4 and other risk scores in identifying the patients who could benefit most from the drug.
“These tools won’t completely replace biopsies or imaging,” says Paul Brennan, a specialty registrar in gastroenterology, hepatology, and internal medicine at the University of Dundee. “But they could fix the bottlenecks in primary care where most fibrosis goes undetected. I expect them to be adopted as a smarter first pass, catching the moderate-risk patients that blunter tools may miss, and reducing unnecessary referrals to hepatologists.”
So far, the use of AI in liver care has largely been confined to research, but Lazarus is optimistic that this will start to change soon. He points to research carried out in Denmark which found that informing people that they have liver fibrosis makes them more likely to commit to dietary and exercise regimes. “We’re always looking to improve adherence to eating well and doing more physical activity,” says Lazarus. “Telling someone that they might have or do have liver disease is one way to do that.”
For health care systems grappling with a rising burden of chronic disease, identifying and treating patients in the earlier stages of fatty liver disease would save vast amounts of money.
“I would love to say, let’s go back through the electronic medical records across the various US health systems and find people before they have cirrhosis,” says Lazarus. “Liver transplants are extraordinarily expensive in any country, but especially the US. So there’s a lot of good humane and economic reasons to find people earlier on.”
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