## 4 Discussion
### Al for Osteoclast Identification and Determination of Maturation
The six conditions used in this study to cultivate osteoclasts were used in the first round of cell culture to collect photos of cells for use in AI training. In the second round of cell culture, cells were used for biochemical analysis, and photos were collected for the AI test. Finally, the results of the AI analysis and the biochemical analysis were compared. It is important to note that the photos used for the AI test did not come from the AI training or were used for it and were, therefore, 'unknown' to the AI. This enabled an objective evaluation of the results of the AI testing. The results show that the biochemical analytics TRAP 5b and CAII are closely related to the results of the AI analysis. The observed correlation between intracellular TRAP 5b activity and the proportion of cells with three or more nuclei suggests that AI-based classification aligns well with established biochemical markers of osteoclast presence. Given that TRAP 5b is considered a specific marker for osteoclasts but does not directly reflect their resorptive activity [25], this finding supports the validity of AI-based counting in identifying mature osteoclast populations. The other cell classifications (MON, one or two cell nuclei, and undefined) do not show this correlation and are not suitable for assessing the state of maturation. In contrast, the relationship between carbonic anhydrase II (CA II) expression and the number of mature osteoclasts appears less direct. While the initial comparison suggests weak similarities, the delayed correlation--particularly the alignment between CA II levels on day 6 and osteoclast counts on day 10--raises the possibility that biochemical markers may precede or influence morphological changes detectable by AI. This temporal offset warrants further investigation to determine whether CA II serves as an early indicator of osteoclast maturation. These results emphasize the need for a multi-faceted approach to osteoclast characterization, integrating AI-based morphological analysis with established biochemical methods. It should be noted that the proposed comparisons have not yet been validated. In our opinion, it is necessary for different laboratories to use their own osteoclast images and cultivation conditions to assess the AI we have provided or train their own AI using our Demo_OC_Identifier.pt AI model ([[https://doi.org/10.25532/OPARA-730](https://doi.org/10.25532/OPARA-730)]([https://doi.org/10.25532/OPARA-730](https://doi.org/10.25532/OPARA-730)); [31]) and to test it in the same way. With the publication of this paper, the OC_Identifier.exe for object recognition and AI models for osteoclasts [32] are now available alongside the segmentation algorithm of the OC_Finder ([[https://bit.ly/OC_Finder](https://bit.ly/OC_Finder)]([https://bit.ly/OC_Finder](https://bit.ly/OC_Finder))) [22], thus expanding the possibilities for osteoclast analysis. The similarities and differences explained in our case are mainly due to the fact that biochemical analysis evaluates all cells as a whole, while the photos used in AI analysis only show a small part of the culture dish, i.e., only a few cells in the field of view of the microscope. In addition, [PERSON] et al. demonstrated that the number of nuclei of the multinucleated tartrate-resistant acid phosphatase (TRAP)-positive cells was, on average, 6.92 nuclei per cell, with a median of four [33]. In this case, 47% of the cells had 5 or fewer nuclei, and only 11% had more than 10 nuclei. Furthermore, a correlation was found between the number of nuclei and the volume of the resorption pits using confocal laser microscopy, with the volume resorbed per nucleus decreasing with the increasing number of nuclei per osteoclast.