Liver disease is treatable when detected early, but it often remains unnoticed until advanced stages. A recent study has shown that an artificial intelligence (AI)-powered algorithm can effectively identify early-stage metabolic-associated steatotic liver disease (MASLD) using electronic health records.
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A significant proportion of patients who meet criteria for MASLD go undiagnosed. This is concerning because delays in early diagnosis increase the likelihood of progression to advanced liver disease.
Ariana Stuart MD, Study Lead Author and Resident, Internal Medicine Residency Program, University of Washington
Researchers utilized an AI algorithm to examine imaging data within electronic health records from three University of Washington Medical System sites to identify cases of MASLD, the most prevalent liver disease, affecting 4.5 million adults in the US. Out of 834 patients meeting MASLD criteria, only 137 had an official diagnosis in their records, leaving 83 % undiagnosed despite their health data indicating the condition.
Stuart added, “People should not interpret our findings as a lack of primary care training or management. Instead, our study shows how AI can complement physician workflow to address the limitations of traditional clinical practice.”
MASLD develops when the liver fails to properly manage fat and is frequently linked to conditions like obesity, Type-2 diabetes, and abnormal cholesterol levels. Early detection is crucial, as the disease can rapidly advance to more severe liver conditions. However, diagnosis is often difficult because many individuals in the early stages show no symptoms.
Ariana Stuart, MD, will present the study, “Artificial Intelligence for Early MASLD Identification in the Electronic Medical Record,” abstract 2360,on Saturday, Nov. 16 at 8 a.m. PST.