## 1 Introduction  
Infertility is the inability of a mature organism to produce offspring. The inability to become pregnant after 12 months or more of frequent, unprotected sexual activity defines this condition of the male or female reproductive system [1]. It is estimated that millions of people and couples worldwide struggle with infertility. According to estimates, one in six persons who are of reproductive age are impacted [1]. The prevalence of infertility accounts for 17.5% of the worldwide population [2]. Medically assisted reproduction (MAR), or in vitro fertilization (IVF), was developed to overcome the problem of infertility and was introduced into clinical practice more than 40 years ago. MAR technologies enable the fertilization of human oocytes and the creation of human embryos in vitro. Viable embryos can then be transferred into the woman's uterus, and the development of intrauterine pregnancy begins upon successful implantation. Since the beginning of successfully applying IVF in humans, these treatments have resulted in the birth of over 10 million children already. Several countries now see more than 3% of all births as a result of MAR [3]. However, the success of MAR remains limited despite the rapid development of related technologies [4; 5; 6]. Changes in the cellular or molecular composition of the innermost lining of the uterus, known as the endometrium, are critical for IVF success; its unique structure and receptivity allow the embryo to be implanted [7; 8]. The highest receptivity of the endometrial tissue occurs during the so-called \"window of implantation\" (WOI) when optimal conditions for embryo implantation are established. Prior to the WOI or past this time, the chance of embryo implantation is scarce. Therefore, an endometrial receptivity assessment has proven to be advantageous for the success of IVF. Transcriptomic biomarkers are most commonly used to assess endometrial receptivity, as the expression levels of these biomarkers depend upon the receptivity status of the uterine tissue [9; 10]. Hence, endometrial tissue transcriptome testing allows an optimal time for the personalized embryo transfer to be chosen [11; 12]. In parallel, endometrial microbiota changes have also been shown to affect embryo implantation, which can be evaluated by tissue profiling during IVF [13; 14]. However, the most common and widely used endometrial tissue evaluation is performed according to the Noyes dating criteria for histological sections [15]. These criteria are valid to date and are used in clinical pathology: when assessing the endometrial phase, both the glandular and stromal parameters must be evaluated [16; 17]. Evaluating stromal cell changes, such as the extent of edema, is subjective, and very small-sized stromal cell nuclei cause further difficulties in their evaluation under standard light microscopy. The clinical value of histological endometrial dating, which defines the individual WOI, has recently been shown to benefit patients with recurrent pregnancy loss [18]. Nevertheless, implementation of the latest technical breakthroughs in deep learning to establish AI-powered technologies to optimize histopathology assessments of endometrial tissue for IVF is still clearly lacking. Therefore, AI-driven clinical decision support tools for endometrial tissue assessments may offer efficient, metric-driven, objective, and accurate diagnoses for endometrial receptivity evaluation. The main aim of this research is to propose a computer-aided system for the evaluation of endometrial tissues from endometrial histological slides, which can be used as an automatic algorithm for endometrial receptivity assessments and predictions. This research paper is organized into seven main sections. The state-of-the-art in the current research topic is provided in the second section. Information about the research object, the proposed AI model, and its validation are given in the third, fourth, and fifth sections accordingly. The experimental setup and results are shown in the seventh section.