## 3 Results
### Quantitative Synthesis  
Considering the DL models' performance for the diagnosis of ovarian adnexal masses, the pooled sensitivity was 86.0% (95% CI, 64.0-96.0%). Whereas, the estimated pooled specificity was 90.0% (95% CI, 84.0-93.0%). Considering experts' performance, the pooled sensitivity was 86.0% (95% CI 60.0-96.0%). The estimated pooled specificity was 89.0% (95% CI, 87.0-91.0%). When comparing the diagnostic performance of the two approaches, we did not find any statistical difference (\(p=0.9883\)). The heterogeneity observed--meaning the presence of variation in the true effect sizes underlying the different studies--was high for all calculations (Figure 2A,B). The observed \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Training SET** & \begin{tabular}{c} **Internal** \\ **Validation Set** \\ \end{tabular} & **Test Set** & \begin{tabular}{c} **External** \\ **Validation Set 1** \\ \end{tabular} & \begin{tabular}{c} **External** \\ **Validation Set 2** \\ \end{tabular} \\ \hline [PERSON] [40] & & & & & \\ \hline Histology & 314 B/194 M & 60 B/40 M & 75 B/75 M & & \\ \hline Images per & \multirow{2}{*}{NR} & \multirow{2}{*}{NR} & \multirow{2}{*}{NR} & \multirow{2}{*}{NR} & \multirow{2}{*}{ND} & \multirow{2}{*}{ND} \\  & histology & & & & \\ \hline [PERSON] [42] & & & & & \\ \hline Histology & 213 B/83 M & 30 B/11 M & 61 B/24 M & ND & ND \\ \hline  & \multirow{2}{*}{NR} & \multirow{2}{*}{NR} & \multirow{2}{*}{NR} & \multirow{2}{*}{NR} & \multirow{2}{*}{} \\  & histology & & & & \\ \hline [PERSON] [43] & & & & & \\ \hline  & Histology & 882 B/217 M & 389 B/71 M & ND & 147 B/51 M & 231 B/33 M \\ \hline  & \multirow{2}{*}{\
\begin{tabular}{c} Images per \\ histology \\ \end{tabular} } & \multirow{2}{*}{2227 B/2270 M} & \multirow{2}{*}{941 B/276 M} & \multirow{2}{*}{ND} & \multirow{2}{*}{329 B/161 M} & \multirow{2}{*}{634 B/127 M} \\  & histology & & & & \\ \hline Gao [44] & & & & & \\ \hline  & Histology & 101,777 B/3755 M & 602 B/266 M & ND & 268 B/67 M & 723 B/166 M \\ \hline  & \multirow{2}{*}{
\begin{tabular}{c} Images per \\ histology \\ \end{tabular} } & \multirow{2}{*}{541,442 B/344,488 M} & \multirow{2}{*}{5385 B/3031 M} & \multirow{2}{*}{ND} & \multirow{2}{*}{933 B/486 M} & \multirow{2}{*}{5257 B/1253 M} \\  & histology & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Distribution of histology and the number of images used in the training, internal validation, test and external validation sets for all the studies included in this meta-analysis. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Year** & \begin{tabular}{c} **Patient** \\ **Selection** \\ \end{tabular} & **Index Test** & \
\begin{tabular}{c} **Reference** \\ **Standard** \\ \end{tabular} &
\begin{tabular}{c} **Flow and** \\ **Timing** \\ \end{tabular} \\ \hline [PERSON] [40] & 2021 & High risk & Low risk & Low risk & High risk \\ [PERSON] [42] & 2022 & High risk & Low risk & Low risk & Low risk \\ [PERSON] [43] & 2022 & High risk & Low risk & Low risk & Unclear \\ [PERSON] [44] & 2022 & High risk & Low risk & Low risk & Unclear \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Risk of bias of the studies included in this meta-analysis. heterogeneity in sensitivity is explained by a difference in the prevalence of the included studies, which was confirmed by meta-regression (\(p\) = 0.0001). The sHROC curves for the diagnostic performances of the DL models and the experts to diagnose ovarian masses are shown in Figure 3A and Figure 3B, respectively. The area under the curve regarding the DL models is 0.94 (95% CI 0.91-0.95). For expert evaluation, the area under the curve is 0.91 (95% CI 0.88-0.93). Figure 2: (**A**) Forest plot for sensitivity and specificity for DL models. (**B**) Forest plot for sensitivity and specificity for expert assessment [40; 42; 43; 44]. Figure 3: (**A**) Summary ROC curves for the DL models. (**B**) Summary ROC curves for the expert examiners. [PERSON]'s nomograms for DL and expert evaluation are shown in Figure 4A and Figure 4B, respectively.