![]() Accuracy of the algorithm was 91.9%, with a classification error of 8.1%. The area under the curve of correctly classified steatosis by the algorithm was 0.970 (95% CI 0.968-0.973), P < 0.001. We obtained liver tissue specimens from 61 NAFLD patients and 18 controls. The software can be implemented as a Java plug-in in FIJI, in which digital WSI can be processed automatically using the Pathomation extension. The resulting algorithm produces a steatosis proportionate area (SPA ratio of steatotic area to total tissue area described as percentage). We identified thresholds for size and roundness parameters by logistic regression to discriminate steatosis from surrounding liver tissue. Hematoxylin-eosin stained liver tissue slides were digitally scanned, and steatotic areas were manually annotated. We developed a digital automated quantification of steatosis on whole-slide images (WSIs) of liver tissue and performed a validation study. ![]() ![]() Accurate assessment of hepatic steatosis is a key to grade disease severity in non-alcoholic fatty liver disease (NAFLD).
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