## 4 Discussion
### Interpretation of Findings in Clinical Context  
Currently, ultrasound is considered the first-choice imaging technique for discriminating between benign and malignant adnexal masses [48]. Expert examiner subjective impression has been shown as the best diagnostic approach [49]. However, this approach has inherent limitations such as the need for adequate training and experience [13; 14]. In fact, a randomized trial demonstrated that examiner experience had a measurable clinical impact in the management of women diagnosed as having an adnexal mass [15]. For this reason, scoring systems and logistic models, such as the Risk of Malignancy index, IOTA simple rules, Risk Assessment based on Simple rules, LR1 model, LR2 model, ADNEX model, GI-RADS and O-RADS classification systems were developed in an attempt to overcome this limitation [21; 22; 50; 51; 52; 53; 54]. No doubt, these approaches have produced a significant advance in the ultrasound discrimination of adnexal masses, allowing less-experienced examiners achieve good results in terms of diagnostic performance [55; 56; 57; 58; 59; 60; 61; 62; 63]. However, the expert examiner is still the most cost-effective approach [49]. Artificial intelligence is becoming a relevant issue in medical practice, particularly for diagnosis. In fact, the use of AI models in the differential diagnosis of adnexal masses for detecting ovarian cancer is increasing, as recently described [64; 65]. AI models in medical imaging may be based on data and/or image processing [25]. In deep learning models, the input and output are connected by multiple layers of hidden connections, also known as convolutional neural networks [25]. Deep learning involves learning from vast amounts of data and performs especially well in pattern recognition within data; therefore, it can be particularly helpful in medical imaging. The use of these models is attractive because it avoids problems that derive from the interpretation of an image by a human observer, such as lack of experience, availability, or subjectivity in interpretation, making the diagnosis more generalizable and reproducible. The use of artificial neural networks for discriminating benign from malignant tumors is not a new topic [66; 67; 68; 69]. Nevertheless, the performance of these early prediction models was limited. The main reason for this limited performance is that these models were built with few data. However, in the era of big data and with the progressive integration of DL in medical imaging involving the automatic assessment of digital images by the software, there is an ongoing active assessment of models to ascertain their diagnostic accuracy in the context of adnexal masses. DL represents the latest evolution in the field of ML, characterized by the utilization of multiple layers of neural networks that contribute increased computational power through heightened neuronal complexity. This advancement, however, comes with a caveat, as deep learning methods are inherently more susceptible to overfitting. Consequently, these methods often necessitate larger datasets to ensure optimal performance. However, it is necessary to determine whether these DL models actually perform better than human beings, particularly expert examiners. The present study compares the performance of DL systems using ultrasound images for detecting and differentially diagnosing adnexal masses against expert evaluations. As stated above, our analysis revealed that there were no significant differences between the two methods of assessment. The conclusion to be drawn may be influenced by the study design and the limited sample size; however, it is evident that these DL models can be applied to ultrasound imaging in situations where expert opinions are unavailable or inaccessible. Furthermore, although it was not our objective, it is interesting to note that the pooled diagnostic performance of DL (sensitivity 86%, specificity 90%) does not seem to be better than other simpler logistic models, such as the ADNEX model or the IOTA-LR2 model.