Objective: This study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics. Study Design: A systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed. For each anatomic system/organ, the major outcomes described in the scientific literature are presented and the most important aspects are highlighted. Results: The vast majority of ANN applications are related to cervical cytopathology, specifically for the ANN-based, semiautomated commercial diagnostic system PAPNET. For cervical cytopathology, there is a plethora of studies relevant to the diagnostic accuracy; in addition, there are also efforts evaluating cost-effectiveness and applications on primary, secondary, or hybrid screening. For the rest of the anatomical sites, such as the gastrointestinal system, thyroid gland, urinary tract, and breast, there are significantly less efforts relevant to the application of ANNs. Additionally, there are still anatomical systems for which ANNs have never been applied on their cytological material. Conclusions: Cytopathology is an ideal discipline to apply ANNs. In general, diagnosis is performed by experts via the light microscope. However, this approach introduces subjectivity, because this is not a universal and objective measurement process. This has resulted in the existence of a gray zone between normal and pathological cases. From the analysis of related articles, it is obvious that there is a need to perform more thorough analyses, using extensive number of cases and particularly for the nonexplored organs. Efforts to apply such systems within the laboratory test environment are required for their future uptake.

Predicting the outcome of in-vitro fertilization (IVF) treatment is an extremely semantic issue in reproductive medicine. Discrepancies in results among reproductive centres still exist making the construction of new systems capable to foresee the desired outcome a necessity. As such, artificial neural networks (ANNs) represent a combination of a learning, self-adapting, and predicting machine. In this review hypothesis paper we summarize the past efforts of the ANNs systems to predict IVF outcomes. This will be considered together with other statistical models, such as the ensemble techniques, Classification And Regression Tree (CART) and regression analysis techniques, discriminant analysis, and case based reasoning systems. We also summarize the various inputs that have been employed as parameters in these studies to predict the IVF outcome. Finally, we report our attempt to construct a new ANN architecture based on the Learning Vector Quantizer promising good generalization: a system filled by a complete data set of our IVF unit, formulated parameters most commonly used in similar studies, trained by a network expert, and evaluated in terms of predictive power.

Τhe importance of the gut microbiome and its functions has only recently been recognized and researched in greater depth. The establishment of the human gut microbiome begins in utero, forming its adult-like phenotype in the first 2–3 years of life. Several factors affect and alter the gut microbiome composition and its metabolic functions, such as early onset of breastfeeding, mode of delivery, antibiotic administration, or exposure to chemical substances, among others. Existing data support the important connection between health status and gut microbiome homeostasis. In cases when this balance is disturbed, several disorders may arise, such as inflammatory reactions that lead to atopy, eczema, or allergic asthma. The so-called gut-brain axis refers to the complex biochemical pathways between the central nervous system and the gastrointestinal system. One of the most fascinating areas of ongoing research is the broad spectrum of neurodevelopmental disorders (NDDs) and how gut health may be associated with such disorders. The prevalence of NDDs, such as autism spectrum disorder or attention deficit hyperactivity disorder, has increased over recent years. Whether gut microbiota homeostasis plays a role in these disorders is not yet fully understood. The aim of this narrative review is to provide an account of current knowledge on how gut health is linked with these disorders. We performed a literature review in order to identify and synthesize available data that highlights the potential association between NDDs and a balanced gut microbiome in terms of composition and proper function. The connection between the gut microbiome and NDDs offers promising new opportunities for future research.

Purpose: We investigated the potential value of morphometry and artificial intelligence tools to discriminate between benign and malignant lower urinary tract lesions.Materials and Methods:...