# Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones
## 5 Discussion and Conclusions  
The VoiceScreen algorithm was created for clinical research and can be used on both iOS and Android devices. The AVQI and its characteristics are computed on the server, and the sound waves collected from the provided smartphone are preprocessed to remove pauses and excess sound before being sent to the server for analysis. The evaluation results were found to be reproducible on different smartphone platforms with no statistical differ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**AVQI** & **AUC** & **Cut-Off** & **Sensitivity (\%)** & **Specificity (\%)** & **Youden-Index J** & **F-Score** \\ \hline AKG Perception 220 & 0.731 & 4.7 & 82.9 & 73.1 & 0.560 & 0.777 \\ IPHONE SE & 0.735 & 4.4 & 82.9 & 63.6 & 0.465 & 0.719 \\ IPHONE PRO MAX 13 & 0.732 & 4.3 & 83.7 & 63.6 & 0.473 & 0.722 \\ Huawei P50 pro & 0.721 & 4.6 & 78.0 & 63.6 & 0.416 & 0.701 \\ Samsung S22 Ultra & 0.710 & 5.8 & 82.1 & 63.6 & 0.457 & 0.716 \\ OnePlus 9 PRO & 0.725 & 5.0 & 82.1 & 63.6 & 0.457 & 0.716 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The findings of the ROC statistical analysis. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Phone** & \begin{tabular}{c} **AKG** \\ **Perception 220** \\ \end{tabular} & **iPhone SE** & \begin{tabular}{c} **iPhone PRO** \\ **MAX 13** \\ \end{tabular} & \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 220 & - & 0.6176 & 0.9319 & 0.4851 & 0.4534 & 0.5938 \\ iPhone SE & 0.6176 & - & 0.6991 & 0.1995 & 0.2888 & 0.2478 \\ iPhone PRO MAX 13 & 0.9319 & 0.6991 & - & 0.4851 & 0.4869 & 0.6649 \\ Huawei P50 pro & 0.4851 & 0.1995 & 0.4851 & - & 0.5978 & 0.7618 \\ Samsung S22 Ultra & 0.4534 & 0.2888 & 0.4869 & 0.5978 & - & 0.4128 \\ OnePlus 9 PRO & 0.5938 & 0.2478 & 0.6649 & 0.7618 & 0.4128 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A pairwise comparison of the significance of differences between AUCs was revealed in the present study. ences. Furthermore, there was a nearly perfect direct linear correlation between the AVQI results obtained from the studio microphone and different smartphones, as the individual smartphone AVQI evaluations demonstrated excellent agreement as indicated by a Cronbach alpha of 0.99. Similarly, the inter-smartphone AVQI measurements showed excellent reliability with an average intra-class correlation coefficient (ICC) of 0.9115 (ranging from 0.8885 to 0.9316). The results of the one-way ANOVA analysis did not reveal significant differences in mean AVQI scores between different smartphones, indicating that the results of the AVQI evaluation are reproducible on different smartphone platforms. Similarly, Pearson's correlation coefficients ranged from 0.976 to 0.99, indicating a nearly perfect direct linear correlation between AVQI results obtained from both the studio microphone and different smartphones. Additionally, based on the analysis of ROC curves using AVQI, it was determined that the diagnostic accuracy of different smartphones was comparable to that of a professional microphone. We determined that there are optimal AVQI cut-off values that can effectively distinguish between normal and pathological voices for each smartphone used in the study. Furthermore, all devices met the proposed 0.7 AUC threshold and demonstrated an acceptable Youden index value. These results confirmed compatible results of the diagnostic accuracy of AVQI in differentiating normal versus pathological voices when using voice recordings from studio microphones and different smartphones, and our approach was found to be a reliable tool for distinguishing between normal and pathological voices, regardless of the device used, with no statistically significant differences between the voice impairment measurements obtained from different devices.