# How to Use the Osteoclast Identifier Software
## 3 Results--The User Interface
### Adjusting Confidence Threshold  
OC_Identifier provides an interactive confidence threshold adjustment feature, allowing users to fine-tune the confidence level via a slider (Figure 1A) to optimize the osteoclast detection results. Adjusting the confidence threshold appropriately helps balance false negatives (FN) and false positives (FP), ensuring accurate and reliable detection. The choice of the confidence threshold has a significant influence on the accuracy and sensitivity of cell identification in the OC_Identifier. A low threshold leads to more potential cells being recognised, but carries the risk of an increased number of false positives. On the other hand, if the threshold is set too high, the precision of the classification increases. However, at the same time, relevant cells may not be detected.  
The following instruction for handling should be followed: if the number of correctly detected cells is too low, the confidence threshold should be lowered to increase sensitivity and reduce false negatives. If, on the other hand, the same cell is detected by several boundaries (boxes) of different classes, it is recommended that the threshold is raised in order to improve the specificity of the classification and to avoid overlapping detections. After starting the software and loading the image, the confidence threshold is adjusted using the slider. Moving the slider to the left lowers the threshold and causes more cells to be detected, but this can increase the number of false positives. Moving the slider to the right raises the confidence threshold, reducing the number of false positives but increasing the risk that some cells will not be detected. The effects of different confidence values are illustrated in Figure 7 by comparing different settings (1%, 25%, 80% and 90%).  
Figure 5: Exemplary representation of a labelled cell image after AI analysis (red box). Here, the cells of class **1 or 2 cell nuclei** are marked with a red frame, those of **3 or more cell nuclei** are marked with a pink frame, those of **monocytes** are marked with a yellow frame and **undefined cells** are marked with an orange frame.  
If the confidence threshold is too high, it is possible that no cells will be detected. In this case, a warning message appears in the console window, advising the user to 'Try  
Figure 6: The user interface after completion of the AI image analyses. Results are given for each image as absolute and relative numbers (red box (1) as well as stored as image and tct-file (red boxes (2) and (3)).  
Figure 7: The comparison of the different confidence intervals at (**a**) 1%, (**b**) 25%, (**c**) 80%, and (**d**) 90% highlighted with red boxes shows that the number of identified cells decreases with an increasing confidence value.  
lowering the confidence threshold'. This situation is illustrated in Figure 8, where no cells were detected and the corresponding message is displayed.  
The precision of our publicly available **Demo_OC_Identifier.pt** model is _0.927_ for 1_or_2_Nuclei and 0.921 for 3_or_more_Nuclei., when running val.py for evaluation. In practical applications, precision is highly influenced by the quality of cell images. Image resolution and clarity play a crucial role in detection accuracy. High-resolution, clear cell images where cell boundaries and nuclei are well defined allow the model to accurately identify osteoclasts, thereby improving precision