## 3 Results
### Detection of the Required Calibration Moment--Neural Networks
Figure 7 shows a variability plot indicating that the MA results exhibit increasing variability over time (rather than AoN), leading to a quality control, followed by calibration. They are marked by pairs of vertical lines, with the first indicating quality control and the second (marked with the dot on the top of line) marking the calibration of the device as a result. The remaining blue dots at the 100 level represent quality control marks that did not result in the need for calibration; hence, from the perspective of data quality, they were not essential. The aim is to capture the change that necessitates calibration, thus demonstrating the absence of the need for quality control in the remaining points. Figure 7 presents the actual values of the tested material from patients in the form of data points. The diversity of results is natural, reflecting the variety of disorders and individual characteristics. However, the assumptions of methods based on patient results suggest that despite this diversity, there should be constant characteristics describing the distribution of this diversity. On the graph, we can observe quality control points, marked as blue dots, and device recalibration, marked as red vertical lines. Statistical algorithms used for quality control on patient data include the following:  
1. AoN (Analysis of Numbers). 2. Moving average of test results in a block. 3. Moving average of natural logarithms of test results in a block. 4. Moving average of square roots of test results in a block. 5. Moving median of test results in a block. Figure 7: Quality control and calibration points in relation to AoN and MA. The pairs of vertical lines: the first indicating quality control and the second (marked with the dot on the top of line) marking the calibration of the device as a result. The remaining blue dots at the 100 level represent quality control marks that did not result in the need for calibration. Moving median of natural logarithms of test results in a block. 7. Moving median of square roots of test results in a block. Figure 8 presents the actual values of the examined material from patients as points, while various statistical characteristics are represented by dashed lines. The purpose of this chart is to magnify a short segment of data to observe the change in the curves resulting from device calibration. This change suggests that calibration affects the characteristics of the distribution of test result variability. To investigate how calibration affects parameters, individual points were labeled with 'DIFF' labels. These labels take the value of 1 when a difference is observed (Figure 8) and 0 when calibration does not alter the indicators. This labeling allows us to distinguish between the observation blocks from before and after calibration (Figure 9). As observed, the results after calibration (first two box plots) do not differ significantly--their boxes overlap. However, the values in the subsequent two plots show significant variation. Therefore, the conclusion is that observations in blocks preceding the calibration, marked as DIFF \(-1\) (i.e., necessary), are significantly different from those preceding the calibration, marked as DIFF 0 (i.e., not introducing a significant change). The analysis visualized in Figure 9 aimed to examine whether the selected characteristic, depicted here as the Moving Average of Square Roots of test results in a block, exhibits significant differences between values before and after calibration. Calibrations labeled as DIFF = 1, meaning those deemed necessary from a quality control standpoint, show a significant difference in the presented characteristic. Figure 8: The visible changes in indicators after calibration: actual values of the examined material from patients are presented as points, while various statistical characteristics are represented by dashed lines. The brown box marks the area before and after calibration (indicated with blue vertical line) where the change in indicator values is best visible.