## 3 Results
### Evaluation of Osteoclast Image Analysis Using Osteoclast AI  
After the AI training, the AI model was used to analyze new cell culture samples in real time. The AI model was used for the automatic counting and classification of cells based on their morphological characteristics compared with the results of biochemical analyses. Generally speaking, the AI model was used to evaluate unknown images. In this work, a total of six different culture conditions for osteoclasts were examined, with biochemical analysis carried out on days 2, 6, and 10. The main difference in this culture is the use of differentiation media in different amounts and proportions. The differentiation medium was always prepared with 25 ng/mL M-CSF, and the addition of RANKL varied. One medium contained the usual amount of 25 ng/mL RANKL as a reference. However, the amount of RANKL was also doubled to 50 ng/mL and halved to 12.5 ng/mL to examine the influence on osteoclast differentiation. In addition, a conditioned medium was used. In this case, the medium was used to culture human mesenchymal stem cells or osteoblasts and then used without additives or with the addition of 25 ng/mL M-CSF and 25 ng/mL RANKL as well as 25 ng/mL M-CSF and 12.5 ng/mL RANKL. Since the induction of osteoclast cell maturation only occurs from day 3, the measurement on day 2 should be considered a control of the initial cell number of monocytes without resorptive activity. Thus, the comparison of osteoclast maturation was based on the biochemical data from days 6 and 10. Since the examination using KI was significantly faster and gentler on the cells, further days could be analysed with ease with microscopy and AI in addition to days 6 and 10. The cell count from the DNA measurement showed significantly higher values for the standard osteoclast medium (OC-M x/y) and its varied M-CSF/RANKL ratio (Figure 7a), in contrast to the cell counts for the hMSC pre-conditioned media (HC-M x/y). This is due to the reduced nutrients resulting from the pre-cultivation of hMSC. The fact that the MONs do not adhere without M-CSF and without RANKL or die to a certain extent means they can no longer be detected after the media changes, which is also due to a lack of differentiation pressure since only the macrophage/osteoclast line promotes the adherence of the cells, while monocytes circulate in the bloodstream (normally 1-3 days) and are not necessarily adherent [24]. The absolute cell counts refer to the size of a 48-well plate,  
Figure 6: Number of cells per photo from Figure 5 after manual counting and image analysis with AI. The correctness of the osteoclasts identified by the AI is shown as a percentage in relation to the manual count. i.e., 1.1 cm\({}^{2}\). Overall, no increase in cell number was expected since MONs and OCs are post-proliferative and only fuse to form larger cells. This makes DNA a fundamentally limited diagnostic tool for osteoclast cell development. AI-based image analysis immediately reveals an advantage, as it provides additional information that enables the differentiation of cells according to their number of nuclei (Figure 7b). As can be seen, the number of cells with only 1-2 nuclei decreased over time from the osteoclast culture, and the number of MONs also decreased, with a greater decline in MONs from day 6 to day 8 in particular. Meanwhile, the number of cells with three or more nuclei increased significantly, and a certain increase in undefined objects could also be seen. It should be noted that although the cells were distinguished based on their number of nuclei  
Figure 7: (**a**) DNA measurement via biochemical analysis and (**b**) the total number of cells counted using AI, including the 4 classes of identified objects. using the AI-based evaluation, the representation reflected the number of cells, i.e., MONs or mono- and multinuclear OCs.