Tag: opportunistic screening

Opportunistic Screening’s AI Milestone

Posted on by Brian Casey

A new study lays the groundwork for AI-based opportunistic screening – the detection of disease using medical images acquired for other indications. In a paper in AJR, researchers show how their homegrown AI algorithm was able to analyze abdominal CT scans and link body composition measurements to the presence of disease.

Opportunistic screening is a sort of holy grail for radiology, with the potential to help radiologists find pathology from scans ordered for other clinical indications. 

  • Some researchers specifically are focusing on analysis of body composition characteristics derived from CT scans like muscle, fat, and bone that could be biomarkers for hidden pathology – and AI is key because it can process mountains of patient data without getting tired.

In the new paper, researchers from the NIH and the University of Wisconsin tested the concept of AI-based body composition analysis on a massive database of 118k patients who got abdominal CT scans from 2000 to 2021. 

  • They analyzed the scans with their own internally developed AI tool that measures 13 features of body composition, from volume and attenuation in different organs to area of subcutaneous adipose tissue. 

Their goal was to correlate the AI measurements with actual presence of disease, as well as other factors that could affect body composition like age and sex. They found …

  • AI-based body composition metrics varied by age and sex, confirming previous studies.
  • AI metrics also correlated with the four systemic diseases studied, specifically cancer, cardiovascular disease, diabetes mellitus, and cirrhosis.
  • The predictive power of different metrics varied by disease, from a high of 13 measures for diabetes to a low of nine for cancer. 

What’s the real-world impact of the study? 

  • In addition to validating the concept of AI-based opportunistic screening on a broad scale, the findings could be used to establish a set of normal values for body composition that also take into account the impact of systemic disease on these measurements.

The Takeaway

The new study is a bit technical, but it’s an important milestone on the path to opportunistic screening. It not only demonstrates the concept’s feasibility, but also begins to establish the normal values needed to actually implement screening programs in the real world.

AI Automates Liver Fat Detection

Posted on by Brian Casey

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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