## 1 Introduction  
_AI-Based Image Analysis in (Biomaterials) Research and Medicine_  
Artificial intelligence (AI) is increasingly being used in biological and biomaterials research to automate image analysis and enhance quantitative assessments. Within AI, computer vision (CV) plays a critical role in cell recognition, classification, and quantification. While traditional approaches for characterizing cell behaviour often rely on biochemical assays [1, 2], fluorescence-based marker detection [3, 4], quantitative PCR [5, 6], and advanced imaging techniques such as ToF- SIMS [7, 8], TEM [9], or histomorphometry [10], these methods typically require fixation or significant sample processing. However, the direct visualization of cell morphology remains essential for evaluating cell development and function, particularly in live-cell studies. AI-based image analysis has emerged as a powerful tool to address these limitations, enabling the real-time, non-destructive evaluation of cell behaviour. AI applications in cell research can be broadly categorized into (1) predicting cell differentiation dynamics, (2) linking morphological and molecular features, and (3) predicting cellular responses to external stimuli. However, in bone research, technical biases in single-cell datasets, the limited profiling of key bone cell types, and insufficient spatial information continue to present challenges [11]. AI has already contributed significantly to biomaterials research, assisting in material synthesis optimization [12; 13] and high-throughput drug testing for bone-related diseases [14]. Osteoclasts (OCs), primary bone-resorbing cells, pose unique challenges for automated quantification due to their large size (30-100 \(\upmu\)m) and multinucleated nature, containing up to 25 nuclei. OCs are cultured from monocytes (9-17 \(\upmu\)m in diameter) [15]. OC maturity can be directly distinguished by the size of the cells in the image--to determine whether they are monocytes (MONs)--and the number of nuclei--to determine whether they are mature OCs with three or more cell nuclei [16; 17; 18]. The importance of images for cell description is particularly high for OCs. These cells are not only large, but the size difference between the cells (mononuclear MONs [15] and the later multinucleated OCs) is also extremely large. Conventional cell counting techniques, such as Neubauer chambers or automated counters like Scepter(r) (Merck Millipore, Darmstadt, Germany), are unsuitable for OCs due to their adherence properties and sensitivity to detachment-induced morphological changes. OC characterization often relies on manual counting, which is time-consuming and prone to errors. AI-based analysis offers a viable alternative, provided it is trained with appropriate datasets and validated against established biochemical methods. Several computational methods for OC identification have been explored. Ilastik-based AI, utilizing a random forest algorithm, has been applied for cell segmentation [19; 20], while the OC-Finder employs convolutional neural networks [21; 22]. However, segmentation-based methods often struggle with overlapping cells, requiring extensive manual input. Image] (latest Version 1.54p), the most commonly used software for cell counting, also faces limitations in detecting interconnected OCs, necessitating manual segmentation before analysis. To overcome these challenges, we applied the YOLO object detection framework for OC identification and quantification. Unlike segmentation-based approaches, YOLO is well suited for recognizing overlapping cells and provides rapid, high-accuracy detection. Additionally, our model integrates ratio calculations to assess differentiation dynamics more effectively. Given the scarcity of open-access datasets for AI training in biomaterials research, we provide our trained model and annotated dataset for public use, facilitating the broader adoption of AI-based image analysis in osteoclast research.