# Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones
## 4 Results
### AVQI Evaluation Outcomes
To compare the means of the recorded samples from the reference microphone with the means from the five cellphones, we utilized Bland-Altman analysis. Both an absolute number and a percentage of the complete range of the relevant parameter, as determined by the reference microphone, are used to express the amount of the important difference (random error). The results show the crucial difference  
Figure 11: Bland–Altman plot for comparison of AVQI scores obtained from each smartphone device and the reference microphone. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Microphones** & **Criterion** & **iPhone SE** & **iPhone** & **Pro Max 13** & \begin{tabular}{c} **Huawei P50** \\ **Pro** \\ \end{tabular} & \
\begin{tabular}{c} **Samsung** \\ **S22 Ultra** \\ \end{tabular} &
\begin{tabular}{c} **OnePlus 9** \\ **PRO** \\ \end{tabular} \\ \hline AKG & & & & & & \\ Perception & r & 0.9937 & 0.9904 & 0.9764 & 0.9777 & 0.9895 \\
220 & & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 \\  & n & 134 & 134 & 134 & 134 & 134 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Correlations of AVQI scores obtained with a studio microphone and different smartphones. calculated from studio microphone measurements as a percentage of the parameter's total range and in absolute numbers. For the combined male and female samples, bias and critical difference were computed. In order to determine if gender has any impact on overall bias and crucial difference, the Bland-Altman plots display data points designated by gender. The 95% confidence interval was used in significance testing for bias values, as was previously noted. The correlation between AVQI results obtained from studio using AKG Perception 220 microphone and different smartphones with a 95% confidence interval is illustrated in Figure 12.  
The experimental evaluation continued with the analysis of receiver operating characteristic (ROC) curves using AVQI to determine the diagnostic accuracy of different smartphones compared to a professional microphone (see Figure 13). ROC curves are representations of the relationship between the sensitivity and specificity of a diagnostic test at various cutoff points. They were used to evaluate the performance of diagnostic tests and to determine the optimal cutoff scores for diagnostic accuracy to compare the diagnostic accuracy of different smartphones with a professional microphone. The diagnostic accuracy of different smartphones was comparable to that of a professional microphone. This suggests that smartphones were able to distinguish between normal and pathological voices with accuracy similar to that of a professional microphone. To determine the optimal cutoff scores for diagnostic precision, the ROC curves obtained from the studio microphone and different smartphones were visually inspected according to the general interpretation guidelines [66]. This allowed one to identify the best balance between sensitivity and specificity for each smartphone, and the cutoff score was determined accordingly. Figure 12: Scatterplot illustrating the correlation between AVQI results obtained from studio using AKG Perception 220 microphone and different smartphones with a 95% confidence interval. Furthermore, the AUC statistics analysis demonstrated that AVQI had a high level of precision in distinguishing between normal and pathological voices, as evidenced by the suggested AUC = 0.7 threshold. The findings of the ROC statistical analysis are provided in Table 4.  
We also analyzed the area under the curve (AUC) statistics to determine the accuracy of AVQI in distinguishing between normal and pathological voices. This statistical measure was used to evaluate the performance of a diagnostic test. It represents the ability of the test to correctly classify individuals into normal and pathological categories. An AUC of 0.5 indicates that the test is no better than random guessing, whereas an AUC of 1.0 indicates perfect classification.