How to Use the Osteoclast Identifier Software

The OC_Identifier software is programmed at the Max Bergmann Center for Biomaterials to make a low-threshold cell culture analysis available. This is a user manual for the OC_Identifier software. This software is used to classify and detect four different cell types based on the developmental stages of osteoclast maturation. The software uses AI models for this purpose, but these can be selected and changed without programming knowledge for flexible adaptation to new AI models and training data. This also makes it easy to compare different AI models, such as those based on different training data or training cycles, etc. In addition, the software calculates the percentage of each cell type among the total number of detected cells and displays detailed test results, including the position and confidence value of the detected cells. With this software and the instructions provided, we hope to enable a broad community to perform the AI-based image analysis of osteoclasts and their development from monocytes, and we hope for future expansion into co- and triple-culture models, for example. This should enable biomaterial characterisation based on a better morphological cell evaluation and replace time-consuming and costly biochemical and, if necessary, PCR analyses with AI image analysis.

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Journal
Applied Sciences
Published
2025-04-11
DOI
https://doi.org/10.3390/app15084208
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.00

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article

How to Use the Osteoclast Identifier Software

Benjamin Kruppke, Guikun Lv, Hans‐Peter Wiesmann
Applied Sciences
AI in cancer detection
article

How to Use the Osteoclast Identifier Software

Benjamin Kruppke, Guikun Lv, Hans‐Peter Wiesmann
article en

Abstract

The OC_Identifier software is programmed at the Max Bergmann Center for Biomaterials to make a low-threshold cell culture analysis available. This is a user manual for the OC_Identifier software. This software is used to classify and detect four different cell types based on the developmental stages of osteoclast maturation. The software uses AI models for this purpose, but these can be selected and changed without programming knowledge for flexible adaptation to new AI models and training data. This also makes it easy to compare different AI models, such as those based on different training data or training cycles, etc. In addition, the software calculates the percentage of each cell type among the total number of detected cells and displays detailed test results, including the position and confidence value of the detected cells. With this software and the instructions provided, we hope to enable a broad community to perform the AI-based image analysis of osteoclasts and their development from monocytes, and we hope for future expansion into co- and triple-culture models, for example. This should enable biomaterial characterisation based on a better morphological cell evaluation and replace time-consuming and costly biochemical and, if necessary, PCR analyses with AI image analysis.

Applied SciencesVol. 15(8)
Technische Universität Dresden (DE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 97%
AI in cancer detection
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How to Use the Osteoclast Identifier Software — Benjamin Kruppke, Guikun Lv, et al. · Applied Sciences (2025) | TGRS Research Map | TGRS