## 4 Discussion
### Strengths and Limitations  
The main strength of our meta-analysis is that, to the best of our knowledge, this is the first reported comparison that is specifically between the performance of DL models and that of expert examiners. Certainly, there are other recent meta-analyses addressing the performance of artificial intelligence approaches to the differential diagnosis of adnexal masses [24; 70; 71]. However, our results cannot be compared with these meta-analyses. [PERSON] et al. reported a meta-analysis including 19 studies using ultrasound-based artificial intelligence models [70]. They included four that used DL (all four of these are also included in our study) and fifteen more using other models. They estimated pooled sensitivity and specificity mixing data from all 19 studies, not discriminating among different approaches such as machine learning, deep learning or artificial neural networks, and did not compare them to expert evaluation. We think that mixing all these data can be misleading, since different approaches may perform differently. [PERSON] et al. reported a meta-analysis assessing the performance of logistic models and \"artificial intelligence\" models [24]. They included 18 studies, but none addressing the role of DL. Therefore, the results of this meta-analysis cannot be compared to ours. Finally, [PERSON] et al. reported a meta-analysis including 11 studies, three of them using ultrasound-based approaches [71]. Two of them used DL, but did not compare this to expert examination and it was not included in our meta-analysis [36]. Again, the results of this meta-analysis cannot be compared to ours. As limitations, the inclusion of a relatively small dataset, consisting only of four studies and mostly of a few hundred data points, imposes limitations on the potential advantages afforded by DL models and has to be considered when evaluating the obtained results. On the other hand, the heterogeneity observed among the included studies was high, and with regards to sensitivity it can be explained by a difference in the prevalence, as stated above. Furthermore, all included investigations were retrospective, using patients' data derived from medical records. This study design has an inherent risk of bias. This observation manifests the need to promote the development of prospective AI research in forthcoming endeavors to enhance study validity. We should be cautious regarding the interpretation of our results. In fact, another potential limitation to be considered is that all four studies included in our meta-analysis used different DL models. [PERSON] et al. used three different deep neural networks (VGG-16, Resnet 50 and MobileNet) [40]. They reported the diagnostic performance of each model and the combination of all three. We used the latter for our calculations. [PERSON] et al. used two different deep neural networks (Resnet 18 and Resnet 50) [42]. The diagnostic performance was slightly better for the model Resnet 18 and we used these data for our calculations. [PERSON] et al. used the LKResnet 18 model, which is a combination of Resnet 18 and LKNet deep neural networks [43]. And finally, [PERSON] et al. used a single deep convolutional neural network (DenseNet-121) [44]. All these models are, certainly, convolutional neural network architectures, but there are differences among them. For example, ResNet18 consists of 18 layers, whereas Resnet50 is composed of 50 layers. On the other hand, LKResnet and DenseNet-121 are combinations of two convolutional neural network architectures. If we look at Figure 2A, we can observe that not all models perform similarly, so some questions arise. Can we use distinct DLs for the same purpose? Are these data generalizable? Can we use these models in different populations? In fact, there is some evidence that these architectures do not perform similarly when compared [72; 73; 74]. Another issue is the selection of the images and the region of interest (ROI) within the image for training the DL model.