## 4 Discussion
### The YOLO Framework, Limitations and Future Solutions  
Of course, the question of alternative AI models and frameworks arises. Factors such as open-source availability, stability, high speed, energy efficiency, high compatibility, and structural flexibility play the most important roles in the selection process. In addition, one of YOLOv5's key innovations is the introduction of an adaptive anchor box computation mechanism. Instead of using predefined fixed anchor boxes, this mechanism automatically calculates the optimal anchor box sizes based on the training dataset. This significantly enhances the model's adaptability to different datasets, performing particularly well when handling domain-specific data. While maintaining high accuracy, YOLOv5 also significantly improves inference speed, making it the ideal deep learning framework for this study. Nevertheless, there are also important limiting factors, as the quality of AI at the end is directly linked to the number of training cycles and, thus, the performance of the hardware used (especially the GPU). In addition, the fact that the image material used for the osteoclast AI has to be specialized in one's own laboratory is a limiting factor with regard to training. However, the high costs of powerful GPUs and the lack of specific datasets are challenges that we are addressing primarily because they best suit the constraints of most interested researchers who want to test this technique. In particular, the possibility of reducing the dataset's restriction in the future via joint image and label datasets is an incentive for us to disseminate this publication to a broad audience in biomaterial research. A laboratory (like ours) has a large number of publications on material characterization [34; 35; 36] and material development [37; 38], as well as cell culture development [3; 23; 39] and, consequently, cell images. These can be used as datasets to adapt the AI training material specifically to your own microscopy processes and parameters. This is particularly advantageous because, normally, the datasets of AI models are not open-source. This article offers the possibility of converting your own cell images into AI training datasets and using them as training material. As such, the training material used and specially recorded for this purpose is also made freely available to the public. The AI training to identify monocytes and osteoclasts, as described in this article, has several limitations that restrict the accuracy and usability of the analysis. One major limitation is the homogeneity of the training data used: all images for AI training were taken from the same cell batch and cell culture round. The status of this cell batch and the chosen differentiation conditions were not sufficient to cover the entire spectrum of osteocl properties. Another critical problem arises from the limited consideration of the microscope's field of view. Since the object of the AI analysis comprises only a small portion of the cells in the field of view, unevenly distributed cells lead to highly variable results. Furthermore, AI only takes the specific properties of osteoclasts into account to a limited extent. Osteoclasts are multinucleated cells, the DNA content of which depends heavily on the number of cell nuclei. The AI used in this article did not calculate the area of individual cells, as would be possible with other segmentation methods [22], nor the number of specific cell nuclei, which affects the precision of the analysis. Another obstacle is the undifferentiated cell clusters that can occur in cell culture [40,41]. These unexpected cells are not recognized by the AI as osteoclasts, although their DNA is also detected during biochemical analysis. This leads to potential discrepancies between the AI results and the biochemical analysis.