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<p>The article explores pathophysiological foundations of reproductive medicine as well as diagnostic and therapeutic approaches, with a&#xa0;particular focus on endometrial function. Endometrial remodeling represents a&#xa0;complex coordinated process essential for embryo implantation, integrating the proliferation and differentiation of glandular and stromal cells, adjustments in immunological cell networks, and vascular modifications. An algorithm autonomously and independently analyzes these parameters, thereby increasing the objectivity of morphological assessment. These quantitative data provide insights into the temporal synchronization of endometrial compartments during the so-called window of implantation (WOI). Assisted analysis of tissue sections through machine learning (ML) offers promising approaches for capturing endometrial remodeling processes and enables the identification of specific morphological signatures that may correlate with endometrial function and receptivity as well as disrupted states such as glandular development arrest (GDA) or chronic endometritis (CE). Therapeutic interventions, such as intracavitary human chorionic gonadotropin (hCG) or platelet-rich plasma (PRP) instillation, target partial functional deficits of specific endometrial network components. The combination of machine learning and immunohistochemical analysis offers a&#xa0;promising, integrative model for assessing and optimizing endometrial function and its therapeutic management.</p>

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Endometriales Remodeling und Embryoimplantation

  • 期刊论文

  • 发表日期：

    2025年1月10日

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  • Benjamin Rösing

摘要

The article explores pathophysiological foundations of reproductive medicine as well as diagnostic and therapeutic approaches, with a particular focus on endometrial function. Endometrial remodeling represents a complex coordinated process essential for embryo implantation, integrating the proliferation and differentiation of glandular and stromal cells, adjustments in immunological cell networks, and vascular modifications. An algorithm autonomously and independently analyzes these parameters, thereby increasing the objectivity of morphological assessment. These quantitative data provide insights into the temporal synchronization of endometrial compartments during the so-called window of implantation (WOI). Assisted analysis of tissue sections through machine learning (ML) offers promising approaches for capturing endometrial remodeling processes and enables the identification of specific morphological signatures that may correlate with endometrial function and receptivity as well as disrupted states such as glandular development arrest (GDA) or chronic endometritis (CE). Therapeutic interventions, such as intracavitary human chorionic gonadotropin (hCG) or platelet-rich plasma (PRP) instillation, target partial functional deficits of specific endometrial network components. The combination of machine learning and immunohistochemical analysis offers a promising, integrative model for assessing and optimizing endometrial function and its therapeutic management.