# Detecting the Absence of Lung Sliding in Lung Ultrasound Using Deep Learning
## 3 Experimental Results
### Automated M-Mode Classification Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**NoF** &
\begin{tabular}{c} **Slice** \\ **Sel.** \\ \end{tabular} & **Bal. Acc.** & \(taa_{75}\) & **Sens.** & **Spec.** & **P-Pair** & **P-Frame** \\ \hline
8 &
\begin{tabular}{c} **lung** \\ centroid \\ \end{tabular} & 0.78 & 0.57 & 0.70 & 0.86 & 0.049 & 0 \\ \hline
16 &
\begin{tabular}{c} **lung** \\ **centroid** \\ \end{tabular} & 0.88 & 0.63 & 0.88 & 0.88 & 0.290 & 0.168 \\
32 &
\begin{tabular}{c} **lung** \\ **centroid** \\ \end{tabular} & 0.86 & 0.70 & 0.87 & 0.91 & 0.085 & 0.223 \\ \hline
64 &
\begin{tabular}{c} **lung** \\ **centroid** \\ \end{tabular} & 0.91 & 0.71 & 0.89 & 0.93 & 0.210 & - \\ \hline
128 &
\begin{tabular}{c} **lung** \\ **centroid** \\ \end{tabular} & 0.81 & 0.69 & 0.69 & 0.94 & 0.600 & 0.003 \\
256 &
\begin{tabular}{c} **lung** \\ **centroid** \\ \end{tabular} & 0.70 & 0.68 & 0.52 & 0.87 & 0.222 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average video classification performance metrics of automated M-mode classification with different numbers of input frames and different slice selection methods. (NoF: Number of frames; Slice sel. : Slice selection method according to Section 2.2.2; Bal. Accuracy: Balanced accuracy; \(taa_{75}\): Thresholded aggregated accuracy according to Section 2.3; Sens. : Sensitivity; Spec. : Specificity; P-pair: \(p\)-value of the paired two-sided permutation test for slice selection method; P-frame: \(p\)-value of two-sided permutation test against the best classifier. )hypothesis (zero difference in balanced accuracy) is true. For each \(p\)-value, 5000 reshuffles of the lung slices selection and centroid slice selection classifier accuracies were performed. Visualisation of the paired permutation \(t\)-tests is in Figure 6. While there is difference in a balanced accuracy mean between 8-frame classifiers for lung slice selection and centroid slice selection at 5% significance level, 16-to-256-frame classifiers for lung slice selection and centroid slice selection pairs do not exhibit differences in the balanced accuracy mean at the 5% significance level. Therefore, for our final hypothesis testing, we preferred classifiers with centroid slice selection, which require only three slices per volume to make volume aggregation, when the differences between classifiers were statistically insignificant. If there was a significant difference between lung and centroid slice selection, we preferred the classification method with a higher balanced accuracy. In Table 3, in row slice selection, we highlight the chosen slice selection method. Visualisation of permutation \(t\)-tests between the best performing 64-frame classifier and other classifiers with different numbers of input frames and chosen slice selection methods can be found in Figure 7. The null hypothesis is that the balanced accuracy means between the best classifier and the rest of the classifiers are equal. The alternative hypothesis is that the balanced accuracy means are not equal. We observe that the balanced accuracy means are significantly worse for the 8-, 128-, and 256-frame classifiers than for the 64-frame one. There is no significant difference between the 16- and 32-frame classifiers and the best-performing 64-frame classifier. Furthermore, we highlight the performance of three classifiers on metric \(taa_{75}\) in 32-, 64-, and 128-frame versions that achieve \(taa_{75}\) above 70% with at least one of the slice selection methods. Figure 6: The paired mean difference of balanced accuracy for five classifiers are shown above in the Cumming estimation plot. The raw data is plotted on the upper axes; each paired set of observations is connected by a line. On the lower axes, each paired mean difference is plotted as a bootstrap sampling distribution. Mean differences are depicted as dots; 95% confidence intervals are indicated by the ends of the vertical error bars. (f.: frames; ctr. : centroid slice selection; lung: lung slice selection.)