According to research released on Saturday, artificial intelligence (AI) can reliably identify metabolic-associated steatotic liver disease (MASLD) in its early stages using electronic medical information.

When fat is not well controlled in the liver, it may lead to MASLD, the most prevalent chronic liver disease with a significant clinical burden worldwide. In recent years, MASLD has become more commonplace worldwide.

Additionally, it is often linked to other prevalent conditions, including obesity, type 2 diabetes, and elevated cholesterol.

Early detection is essential since the disorder may rapidly worsen into more severe types of liver disease. However, since it is difficult to diagnose in the early stages and stays asymptomatic, it often goes unnoticed until the late stages.

According to main author Ariana Stuart of the University of Washington in the United States, “a significant proportion of patients who meet criteria for MASLD go undiagnosed.” “Delays in early diagnosis raise the risk of progression to advanced liver disease, which is concerning.”

The researchers analyzed imaging results in electronic health records from three US locations using an AI system. Only 137 of the 834 individuals who satisfied the criteria for MASLD received a formal diagnosis linked to MASLD in their medical records.

Even when information in their electronic health record indicated that they satisfied the criteria for MASLD, 83% of patients remained undiagnosed.

“This study demonstrates how AI can enhance physician workflow to overcome the constraints of conventional clinical practice,” Stuart said.

The American Association for the Findings of Liver Diseases is hosting The Liver Meeting, where the findings will be presented.

AI may be used to diagnose non-alcoholic fatty liver disease (NAFLD) and identify liver fibrosis, according to earlier research. Additionally, it may aid in the diagnosis of hepatocellular carcinoma, the prognosis of chronic liver disease (CLD), the differentiation of localized liver lesions, and the advancement of transplant technologies.