FIBER-ML, an Open-Source Supervised Machine Learning Tool for Quantification of Fibrosis in Tissue Sections

Pathologic fibrosis isa major hallmark of tissue insult in many chronic diseases. Although the amount of fibrosis is recognized as a direct indicator of the extent of disease, there is no consentaneous method for its quantification in tissue sections. This study tested FIBER-ML, a semi-automated, open-source freeware that uses a machine-learning approach to quantify fibrosis automatically after a short user-controlled learning phase. Fibrosis was quantified in sirius red-stained tissue sections from two fibrogenic animal models: acute stress-induced cardiomyopathy in rats (Takotsubo syndrome-like) and HIV-induced nephropathy in mice (chronic kidney disease). The quantitative results of FIBER-ML software version 1.0 were compared with those of ImageJ in Takotsubo syndrome, and with those of inForm in chronic kidney disease. Intra-and inter-operator and inter-software correlation and agree-ment were assessed. All correlations were excellent (> 0.95) in both data sets. The values of discrim-inatory power between the pathologic and healthy groups were < 10(-3) for data on Takotsubo syndrome and < 10(-4) for data on chronic kidney disease. Intra-operator agreement, assessed by intra-class co-efficient correlation, was good (> 0.8), while inter-operator and inter-software agreement ranged from moderate to good (> 0.7). FIBER-ML performed in a fast and user-friendly manner, with reproducible and consistent quantification of fibrosis in tissue sections. It offers an open-source alternative to currently used software, including quality control and file management.

Automated summary

Pathologic fibrosis is a major characteristic of tissue insult in chronic diseases, but there is currently no consistent method for its quantification. This study tested a new open-source software, FIBER-ML, which can automatically quantify fibrosis after a learning phase. The software performed well and showed excellent correlation and agreement with other software. It provides a user-friendly alternative with quality control and file management.