## 1 Introduction  
Adnexal masses, comprising ovarian, tubal, and para-ovarian lesions constitute a common clinical problem and represent a diagnostic challenge in gynecology. Fortunately, most adnexal masses are benign [1]. Benign adnexal masses can be managed conservatively with serial follow-up or surgically with tumor removal, preferentially with minimally invasive surgery by a general gynecologist [2, 3, 4]. In addition, fertility preservation is an important issue in patients of reproductive age and with childbearing intentions [5]. On the other hand, ovarian cancer is the second most common gynecological malignancy with an estimated incidence of 11.4 cases/100,000 women-year [6]. However, this cancer is the most lethal gynecological malignancy, with a 5-year overall survival of 48% [7], and there is no effective screening strategy [8]. Ovarian cancer should be treated in specialized centers, with expert surgeons and adequate resources, where surgical outcomes and patients' survival are clearly improved [9; 10]. Therefore, the precise preoperative diagnosis between benign and malignant ovarian masses is essential for selecting appropriate treatment strategies and enhancing patients' postoperative prognoses [11], necessitating the precise preoperative assessments for optimal clinical guidance and therapeutic decision-making [11]. The mainstay for diagnosing adrenal masses relies on imaging techniques, with transvaginal ultrasound being regarded as a first-line imaging tool for assessing adnexal tumors for its non-invasiveness, cost-effectiveness, and ability to provide real-time insights into pelvic pathology and because no other imaging technique has been proven to be superior in terms of diagnostic performance [11; 12]. However, the accuracy of ultrasound imaging interpretation is contingent upon both the proficiency of the operator and the available technology [13; 14]. In fact, a randomized trial demonstrated how the operator's experience has a measurable impact on the correct management of adnexal masses [15]. In this context, the importance of accurate preoperative diagnosis through transvaginal ultrasound cannot be overstated, as it significantly influences the subsequent clinical management of patients with adnexal masses [16; 17]. Because this technique depends on the operator's experience and due to their limited access to expert imaging assessments, to complement their evaluation, numerous pattern-based methodologies and logistical models leveraging image characteristics have been proposed to improve the precision of diagnoses for adnexal masses. For years many attempts have been made to develop scoring systems and classifications that would allow less-experienced examiners achieve good results [18; 19; 20; 21; 22]. However, as technological landscapes continue to evolve, the integration of artificial intelligence (AI) has emerged as a revolutionary paradigm in medical diagnostics [23; 24]. Artificial intelligence is described as the ability of a computer program to perform processes associated with human intelligence, that can learn and interact [25]. Machine learning is a branch of AI, defined by the ability to learn from data without being explicitly programmed. Machine learning can be understood as a statistical method that gradually improves as it is exposed to more data, by extracting patterns from the data [25]. Deep learning (DL), a subset of machine learning, that entails the creation of convolutional neural networks that emulate the human brain's capacity to learn and make decisions. 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. With the exponential growth of medical data, this tool has shown promising results in enhancing the accuracy and efficiency of adnexal mass characterization.