Computational Approaches in Pelvic Organ Prolapse: A Critical Review

Pelvic organ prolapse (POP) is a common, multifactorial condition where pelvic organs descend into or through the vaginal canal, often compromising quality of life. Despite multiple conservative and surgical options, recurrence rates remain significant, and controversies persist regarding the optimal use of mesh, surgical techniques, and the definition of “successful” outcomes. Discrepancies in patient anatomy, tissue properties, and symptom severity underscore the limitations of a “one-size-fits-all” treatment paradigm and highlight the need for truly personalized care. Computational modeling, particularly finite element analysis (FEA), has attracted increasing attention for its ability to simulate patient-specific biomechanical forces, identify high-risk zones for surgical failure, and inform the design of innovative repair strategies. However, the literature features substantial variability in defining functional versus anatomical success, as well as inconsistent imaging methods and limited in vivo data on tissue properties, all of which make it challenging to establish uniform best practices. In this evolving context yet with too heterogeneous studies, this review summarizes in a critical way the actual methodological gaps, emphasizing the importance of standardized definitions, validated clinical endpoints for patients, and more comprehensive biomechanical characterization as the critical bases for the genesis of an effective computational surgical planning system. Emerging technologies—including machine learning, augmented reality, and multi-omics—promise to enhance diagnostic accuracy and treatment planning, fostering a paradigm shift toward truly individualized, function-oriented management. Bridging mechanical insights, patient-centered metrics, and clinical decision-making may transform POP management, reducing recurrence rates and enabling truly individualized care in gynecological practice.