A Three-Dimensional Data-Based Pelvic Floor Support Structure Model and Modeling Method for Surgical Planning of Pelvic Organ Prolapse
Journal: Journal of Clinical Medicine Research DOI: 10.32629/jcmr.v7i2.5295
Abstract
Female pelvic organ prolapse (POP) is a prevalent pelvic floor dysfunction that severely affects the quality of life of middle-aged and elderly women. The main cause of high recurrence is the lack of direct and reliable preoperative visualization of pelvic floor ligament morphology and mechanical state. Traditional evaluation methods rely on two-dimensional imaging and clinical experience, leading to inconsistencies between preoperative planning and intraoperative conditions. This study aimed to develop and validate a patient-specific three-dimensional (3D) physical model of the pelvic floor support structure based on personalized MRI data, together with a standardized modeling procedure. The model consists of a pelvic bony framework, visceral organ modules, and elastic ligament units with biomechanical simulation functions. A total of 10 participants underwent standardized multi-planar MRI scanning, and 1:1 physical models were constructed and verified. The results showed that the mean error of uterosacral ligament length was 1.2±0.4 mm, the maximum stress of the uterosacral ligament was 1.510±0.086 MPa, and the maximum displacement was 19.46±2.37 mm. The 3D model accurately reproduces the in vivo anatomical structure and mechanical properties of the pelvic floor, enables intuitive preoperative simulation, helps identify defect locations, and supports personalized surgical design. This technology provides a novel and effective tool for precise diagnosis and treatment of POP, and has significant value in reducing surgical recurrence and improving clinical outcomes.
Keywords
pelvic floor support; 3D model; pelvic organ prolapse; uterosacral ligament; biomechanical simulation; MRI-based modeling
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[2] Nana A, Ozyurt B, Yilmaz H. Quality of life in women with pelvic organ prolapse. Int Urogynecol J. 2020;31(2):241-247.
[3] Barber MD, Brubaker L, Jelovsek JE, Norton PA, Gantz MG. Outcomes of pelvic organ prolapse surgery. Obstet Gynecol. 2018;132(3):713-722.
[4] Dietz HP. Three-dimensional pelvic floor imaging for pelvic organ prolapse planning. Int Urogynecol J. 2019;30(8):1327-1336.
[5] DeLancey JO. Anatomic aspects of vaginal eversion after hysterectomy. Am J Obstet Gynecol. 2005;192(6):1715-1723.
[6] Margulies SS, Liu X, Smith A, et al. Biomechanical characteristics of uterosacral ligament in POP patients: an in vitro and MRI-derived study. Am J Obstet Gynecol. 2022;227(3):345.e1-345.e9.
[7] Chen L, Zhang Y, Wang H, et al. 3D printed patient-specific pelvic floor model for preoperative simulation of apical suspension. J Minim Invasive Gynecol. 2021;28(5):821-828.
[8] Sung VW, Humphreys MR. Standardized workflow for patient-specific pelvic floor 3D reconstruction based on dynamic MRI. Female Pelvic Med Reconstr Surg. 2020;26(7):421-426.
[9] Wong V, Lee JH. Individualized pelvic floor physical model optimizes preoperative planning of apical prolapse repair. Urology. 2022;165:156-162.
[10] Sheikh M, Podrazka R, Stone A. Patient preparation protocol for dynamic pelvic floor MRI with Valsalva maneuver. J Magn Reson Imaging. 2021;54(2):589-596.
[11] Grzegorz K, Karolina S. Dynamic MRI protocol optimization for pelvic organ prolapse quantitative measurement. Eur J Radiol. 2020;129:109057.
[12] Miller ST, Cooke AR. Modular 3D printed pelvic specimen model for resident surgical training. J Obstet Gynaecol Can. 2022;44(9):832-838.
[13] Zhang H, Liu B. Finite element analysis combined with physical 3D model for pelvic ligament biomechanical research. Comput Methods Programs Biomed. 2023;231:107425.
[14] Rodriguez M, Ferreira R. Clinical outcome analysis of preoperative 3D model-assisted pelvic prolapse surgery. Int Urogynecol J. 2023;34(5):987-993.
[15] Wilson M, Carter D. Five-point Likert scoring for clinical evaluation of anatomical simulation models in pelvic reconstructive surgery. J Surg Educ. 2019;76(6):e121-e125.
Copyright © 2026 Xiaoge Li, Qiya Chu, Chunyan Song, Jinping Ma, Yuancui Xiang, Shuli Wang, Haoran Li, Xiaona Tian
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