Analysis of liver diseases is proving to be challenging from both a research and clinical perspective. Histological features like fibrosis and inflammation are currently quantified with manual, time-consuming methods, often affected by intra– and inter-observer variability.
The rise in incidence of these liver diseases warrants a need for new, more precise tools for diagnostic and research purposes.
How does Aiforia enable liver research?
Aiforia’s deep learning AI platform has successfully enabled the automation of a versatile, multi-layered range of analyses in a number of areas and sample types such as in primary sclerosing cholangitis (PSC), non-alcoholic steatohepatitis (NASH), nonalcoholic fatty liver disease (NAFLD), and their related inflammation.
With Aiforia you can automate the detection of relevant regions of interest based on morphology, use external ground-truths to find these regions in precise locations, and visualize the feature of interest.
Watch the video to see how Aiforia Create can be used for consistent and fast quantification of fat accumulation in the liver.
Fat accumulation and fibrosis amount (see Figure 1)
Of spatial and morphological metrics
Distance measurements between fat droplets and portal areas
In inflammation and steatosis
Of fibrotic Collagen I (IHC), CK7 IHC
Tissue architecture, for example: portal areas, hepatocytes, parenchyme, capsule, biliary ducts, veins
Regression models predicting continuous values
IHC staining of intensity and other end-points