AI Automates Liver Fat Detection

An automated AI algorithm that analyzes CT scans for signs of hepatic steatosis could make it possible to perform opportunistic screening for liver disease. In a study in AJR, researchers described their tool and the optimal CT parameters it needs for highest accuracy. 

Hepatic steatosis (fatty liver) is a common condition that can represent non-alcoholic fatty liver disease (NAFLD), also known as metabolic dysfunction-associated steatotic liver disease (MASLD). Imaging is the only noninvasive tool for detecting steatosis and quantifying liver fat, with CT having an advantage due to its widespread availability. 

Furthermore, abdominal CT data acquired for other clinical indications could be analyzed for signs of fatty liver – the classic definition of opportunistic screening. Patients could then be moved into treatment or intervention.

But who would read all those CT scans? Not who, but what – an AI algorithm trained to identify hepatic steatosis. To that end, researchers from the US, UK, and Israel tested an algorithm from Nanox AI that was trained to detect moderate hepatic steatosis on either non-contrast or post-contrast CT images. (Nanox AI was formed when Israeli X-ray vendor Nanox bought AI developer Zebra Medical Vision in 2021.)

The group’s study population included 2,777 patients with portal venous phase CT images acquired for different indications. AI was used to analyze the scans, and researchers noted the algorithm’s performance for detecting moderate steatosis under a variety of circumstances, such as liver attenuation in Hounsfield units (HU). 

  • The AI algorithm’s performance was higher for post-contrast liver attenuation than post-contrast liver-spleen attenuation difference (AUC=0.938 vs. 0.832)
  • Post-contrast liver attenuation at <80 HU had sensitivity for moderate steatosis of 77.8% and specificity of 93.2%
  • High specificity could be key to opportunistic screening as it enables clinicians to rule out individuals who don’t have disease without requiring diagnostic work-up that might lead to false positives

The authors point out that opportunistic screening would make abdominal CT scans more cost-effective by using them to identify additional pathology at minimal additional cost to the healthcare system. 

The Takeaway

This study represents another step forward in showing how AI can make opportunistic screening a reality. AI algorithms can comb through CT scans acquired for a variety of reasons, identifying at-risk individuals and alerting radiologists that additional work-up is needed. The only question is what’s needed to put opportunistic screening into clinical practice. 

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