Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2026
Automated Deep Learning Detection of Hepatic Steatosis on Noncontrast Computed Tomography Scans and Discrepancy With Scan Reports.
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Yardeni David, Liu Jianfei, Mukherjee Pritam, Shen Thomas C, Elton Daniel C, Parnell Christopher, Trang Winnie, Aleyadeh Wesam, Rodriguez Franco Gian, Lee Sungwon, Summers Ronald M, Rotman Yaron
Аннотация
BACKGROUND & AIMS: Metabolic dysfunction-associated steatotic liver disease is a major cause of liver disease that is growing in prevalence. Typically asymptomatic, is often undiagnosed. Imaging studies, including noncontrast computed tomography (CT) scans, can detect hepatic steatosis opportunistically, but the reporting rate is unknown. We hypothesized that incidental finding of steatosis on noncontrast CT is often not reported if not specifically sought in the imaging request.
METHODS: This study was a retrospective, cross-sectional, single-center analysis of abdominal noncontrast CT scans performed between 2012 and 2020 for any indication in adult subjects. Images were analyzed using an automated deep learning liver segmentation and attenuation assessment algorithm to obtain a mean volumetric liver attenuation value. Image-based steatosis was defined as mean hepatic attenuation <40 HU and compared with textual radiology reports. Manual review of a random subset of scans and reports was used to verify results.
RESULTS: A total of 3646 noncontrast CT scans from 2710 adult patients were analyzed. The mean liver attenuation derived from the deep-learning algorithm was 50.4 ± 11.8 HU. Image-based steatosis was found in 480 (13.1%) scans, with a mean liver attenuation of 29.8 ± 14 HU. Radiologists reported steatosis in only 157 (32.7%) of these low-attenuation scans. Predictors of unreported steatosis included higher average liver attenuation (even if < 40 HU), high variability of fat distribution in the liver and low body mass index.
CONCLUSIONS: We found that incidental hepatic steatosis in noncontrast CT is reported in a minority of scans. Incorporating artificial intelligence-based hepatic attenuation measurement in computed tomography scan reading may increase reporting rates.
METHODS: This study was a retrospective, cross-sectional, single-center analysis of abdominal noncontrast CT scans performed between 2012 and 2020 for any indication in adult subjects. Images were analyzed using an automated deep learning liver segmentation and attenuation assessment algorithm to obtain a mean volumetric liver attenuation value. Image-based steatosis was defined as mean hepatic attenuation <40 HU and compared with textual radiology reports. Manual review of a random subset of scans and reports was used to verify results.
RESULTS: A total of 3646 noncontrast CT scans from 2710 adult patients were analyzed. The mean liver attenuation derived from the deep-learning algorithm was 50.4 ± 11.8 HU. Image-based steatosis was found in 480 (13.1%) scans, with a mean liver attenuation of 29.8 ± 14 HU. Radiologists reported steatosis in only 157 (32.7%) of these low-attenuation scans. Predictors of unreported steatosis included higher average liver attenuation (even if < 40 HU), high variability of fat distribution in the liver and low body mass index.
CONCLUSIONS: We found that incidental hepatic steatosis in noncontrast CT is reported in a minority of scans. Incorporating artificial intelligence-based hepatic attenuation measurement in computed tomography scan reading may increase reporting rates.