## 4 Discussion
The authors proposed two different algorithms, based on colocalization and pattern recognition, both with high correlation with stereology, but with a minimal underestimation of the second one. The mean bias reported was close to zero, with the LoA was around \(\pm\)10%. In the study by [PERSON] et al. [38], the commercially available software tested, which identifies fibrosis based on color analysis, overestimated CVF by 3 \(\pm\) 6.7%, with LoA of up to 16.3%. The LoA identified in the present study for the combined method, compared to stereology, is in line with those mentionedin the previous studies. Another method proposed by [PERSON] et al. [39] for the quantification of myocardial fibrosis assumed infrared spectroscopic imaging combined with machine learning algorithms, and was able to generate virtual images stained with picrosirius red. Multiphoton microscopy has also proven its valuable contribution in this field, being able to visualize the microscopic structure of the myocardium on sustained sections [40]. The aim of this study was to validate a facile, semi-automatic method to quantify myocardial fibrosis and to compare it with stereological and ImageJ evaluation. In the case of this paper, the manual identification of fibrosis using histopathologic images was performed by two pathologists, and the user of the software application selected the mini-region, taking into account the manual markings. Moderate performances were observed for the proposed methods; however, performance was superior in the case of the combined method. Another limitation was caused by the examination of digital images, and not of the slide as a whole, which is why the calculated percentages cannot refer to the entire tissue fragment [41]. In a real-world scenario, there is a possibility of over- or underestimation of fibrosis [42,43]. The semi-automatic character of the algorithm is also subjected to intra- and inter-observer variability, as the ROI must be selected by an expert in the field that can distinguish between normality and abnormality. Thereby, the algorithm can be an aid to the pathologist, not a substitute. In a real-world scenario, the pathologist will only need to identify a mini-region with fibrosis in a histopathological image, and the algorithm will automatically identify fibrosis in the whole image, calculate the percentage of fibrosis tissue in the whole image and represent this fibrosis tissue with a different color. All these features reduce the time needed for pathologists to manually evaluate histopathological images to detect fibrosis in real life. The differences observed between the methods can also be caused by the fact that a special stain for collagen was not used, a fact that predisposes results to poor contrast [37]. Among the variety of staining methods for collagen emphasis, it appears that the most suitable is picrosirius red, combined with subsequent examination in polarized light [44]. It also has an affinity for the fine collagen type III fibers, unlike [PERSON] or Masson trichrome stainings [44]. Therefore, the results presented may be underestimated. Regardless of the stain chosen, color variation is a problem in digital pathology methods, being attributed to the staining (different staining conditions, different reagents) and scanning slides of under different conditions [45]. Significant qualitative differences have been reported, even among slides stained in the same laboratory [16]. An advantage of the methods analyzed in the present study is that they showed a strong correlation with ImageJ quantification and stereology, although the database included diverse images in terms of color variability, contrast and artifacts encountered. New deep learning methods can generate virtual images with certain special stains, starting from H&E [10], with the potential to generate more comparable images. It is also important to consider the thickness of the sections because the area of collagen deposition is proportional to it [42].