Liver disease affects an estimated 4.5 million people in the U.S. and often shows no symptoms. Researchers at Cedars-Sinai have developed an artificial intelligence algorithm called EchoNet-Liver to facilitate faster and easier diagnosis of liver disease by analyzing videos from echocardiograms, which are standard tests for heart disease. This deep-learning model can identify high-quality liver images from over 1.5 million echocardiogram videos of nearly 25,000 patients and detect conditions like cirrhosis and steatotic liver disease.
The technology leverages existing echocardiogram data, making it cost-effective by enabling opportunistic screening without additional tests. As heart disease can often lead to chronic liver issues, the model aids doctors in distinguishing between primary liver diseases and secondary liver injuries related to heart conditions. The integration of this AI technology could improve early diagnosis, treatment, and overall patient outcomes in the healthcare field, highlighting the transformative potential of AI in medical diagnostics.