## 3 Experiment
### Virus Detection Algorithm
[PERSON]'s coefficient of the cell counting algorithm was found to be 0.9807, indicating a very strong positive correlation between the manual counts by the experts and the machine's output, compared to similar studies, such as one using an algorithm for viral plaque assays (Pearson coefficient of 0.9221) [27]; this study demonstrates superior performance with coefficients of 0.9807 and 0.9886 for cell and virus detection, respectively. These results highlight the improved accuracy and robustness of the proposed approach, supporting its potential for broader application in virology research. To further confirm the significance of this correlation, a \(p\)-value was calculated. In this case, the \(p\)-value was less than 0.05, suggesting that the correlation between the expert

Figure 7: Linear regression graphs show the results of the linearity, correlation coefficient, and R-squared values. (**a**) Linear regression graph comparing the cell counting algorithm with expert manual counts, and (**b**) linear regression graph comparing the virus counting algorithm with expert manual counts. and machine counts is statistically significant [37], meaning the observed relationship is unlikely to be due to random chance. This reinforces the reliability of the algorithm in replicating expert-level performance. Similarly, the virus detection algorithm exhibited an even stronger correlation, with a Pearson's coefficient of 0.9886, further underscoring the accuracy and reliability of the algorithm in detecting viral counts. #### 4.1.2 Bland-Altman Plot

A Bland-Altman plot is a statistical method used to evaluate the agreement between two measurement techniques [38]. In this case, the Bland-Altman plot was applied to compare the expert manual counts and the cell counting algorithm's performance, as shown in Figure 8a. The plot visualizes the differences between the two measurements (expert and algorithm) against their averages. The mean bias is \(-21.51\), indicating that the algorithm slightly underestimates cell counts compared to the expert counts. The limits of agreement, which range from \(-133.28\) to \(90.26\), represent the boundaries within which 95% of the differences are expected to fall. Most of the data points lie within these limits, suggesting that the algorithm's discrepancies are within acceptable boundaries (\(\pm 2\) standard deviations). Therefore, the Bland-Altman analysis confirms that the algorithm performs with reasonable accuracy and consistency. Similarly, the Bland-Altman plot for the virus detection algorithm in Figure 8b shows a mean bias of \(-4.84\), indicating a slight underestimation of virus counts. The limits of agreement for this algorithm are narrower, ranging from \(-76.47\) to \(66.79\), demonstrating

Figure 8: (**a**) Bland–Altman plot for the cell counting algorithm, and (**b**) Bland–Altman plot for the virus counting algorithm, illustrating the agreement between the algorithm and expert counts. less variability compared to the cell counting algorithm. As with the cell counting algorithm, most of the data points fall within these limits, reinforcing the precision and reliability of the virus detection algorithm in replicating expert-level performance. Together, these Bland-Altman analyses confirm that both algorithms perform with high accuracy and consistency across a range of cell and virus counts. #### 4.1.3 Box Plot

The box plot for the cell counting algorithm, shown in Figure 9a, provides a visual summary of the distribution of cell counts as measured by three experts, with the algorithm's performance overlaid to assess its alignment with expert counts. The box plot is constructed using key statistical measures, including the 1 st quartile (25 th percentile) and 3 rd quartile (75 th percentile), which represent the middle 50% of the expert data. The box itself covers this interquartile range (IQR), while the whiskers extend to values within two times the IQR, capturing most of the data's spread. Any points outside this range are considered outliers [39].