Performance of an automated deep learning algorithm to identify hepatic steatosis within noncontrast computed tomography scans among people with and without HIV

Abstract

AbstractPurposeHepatic steatosis (fatty liver disease) affects 25% of the world's population, particularly people with HIV (PWH). Pharmacoepidemiologic studies to identify medications associated with steatosis have not been conducted because methods to evaluate liver fat within digitized images have not been developed. We determined the accuracy of a deep learning algorithm (automatic liver attenuation region‐of‐interest‐based measurement [ALARM]) to identify steatosis within clinically obtained noncontrast abdominal CT images compared to manual radiologist review and evaluated its performance by HIV status.MethodsWe performed a cross‐sectional study to evaluate the performance of ALARM within noncontrast abdominal CT images from a sample of patients with and without HIV in the US Veterans Health Administration. We evaluated the ability of ALARM to identify moderate‐to‐severe hepatic steatosis, defined by mean absolute liver attenuation <40 Hounsfield units (HU), compared to manual radiologist assessment.ResultsAmong 120 patients (51 PWH) who underwent noncontrast abdominal CT, moderate‐to‐severe hepatic steatosis was identified in 15 (12.5%) persons via ALARM and 12 (10%) by radiologist assessment. Percent agreement between ALARM and radiologist assessment of absolute liver attenuation <40 HU was 95.8%. Sensitivity, specificity, positive predictive value, and negative predictive value of ALARM were 91.7% (95%CI, 51.5%–99.8%), 96.3% (95%CI, 90.8%–99.0%), 73.3% (95%CI, 44.9%–92.2%), and 99.0% (95%CI, 94.8%–100%), respectively. No differences in performance were observed by HIV status.ConclusionsALARM demonstrated excellent accuracy for moderate‐to‐severe hepatic steatosis regardless of HIV status. Application of ALARM to radiographic repositories could facilitate real‐world studies to evaluate medications associated with steatosis and assess differences by HIV status.