Methods of artificial intelligence, three-dimensional and finite element modeling in the diagnosis of pelvic organ prolapse based on visualization

Introduction. Visualization methods remain fundamental in modern diagnosis and treatment planning for pelvic organ prolapse (POP). Artificial intelligence (AI), three-dimensional (3D), and finite element (FEM) modeling are emerging as powerful tools with growing recognition of their results.

Aim. To summarize current data on the use of AI, 3D, and FEM technologies in the diagnosis and treatment of POP. 

Materials and methods. Using the PRISMA ScR checklist presented in the review, based on the scope of application, as a methodological framework, PubMed, Web of Science, Scopus, and the Cochrane Library were searched from January 2020 to December 2025. The review included studies applying AI algorithms to diagnostic imaging modalities (ultrasound, CT, MRI), as well as 3D and FEM. Current evidence was examined to identify measures aimed at achieving best practices.

Results. 4,652 records were retrieved, 988 relevant publications were identified, and 254 full-text articles were retained and screened based on titles and abstracts. Fifty-four articles were then assessed for inclusion criteria, and 32 articles were included in the study. Reasons for excluding 22 articles included irrelevance for visualizing POP, insufficient methodological or diagnostic detail, and publication type. The studies were based on internal datasets with limited model interpretability and a lack of external validation, so clinical implementation and outcome assessment remain understudied.

Conclusions. AI methods improve image analysis, optimize workflows, provide a personalized approach, and increase the effectiveness of POP diagnosis and treatment. FEM technologies are effective in functional computer-aided biomechanical assessment of the pelvic floor. Personalized 3D modeling enables the development of optimal surgical treatment strategies. Future studies should prioritize external validation, methodological rigor, standardization, and implementation in real-world settings to bridge the gap between experimental models and clinical utility.

1. Collins S, Lewicky-Gaupp C. Pelvic Organ Prolapse. Gastroenterol Clin North Am. 2022;51(1):177–193. https://doi.org/10.1016/j.gtc.2021.10.011.

2. Raju R, Linder BJ. Evaluation and Management of Pelvic Organ Prolapse. Mayo Clin Proc. 2021;96(12):3122–3129. https://doi.org/10.1016/j.mayocp.2021.09.005.

3. Chernyak V, Bleier J, Kobi M, Paquette I, Flusberg M, Zimmern P et al. Clinical applications of pelvic floor imaging: opinion statement endorsed by the society of abdominal radiology (SAR), American Urological Association (AUA), and American Urogynecologic Society (AUGS). Abdom Radiol. 2021;46(4):1451–1464. https://doi.org/10.1007/s00261-021-03017-8.

4. Padoa A, Braga A, Brecher S, Fligelman T, Mesiano G, Serati M. Pelvic Organ Prolapse: Current Challenges and Future Perspectives. J Clin Med. 2025;14(20):7313. https://doi.org/10.3390/jcm14207313.

5. Lipetskaia L, Gupta A, Cheung RYK, Khullar V, Ismail S, Bradley M et al. International Urogynecological Consultation Chapter 2.2: Imaging in the Diagnosis of Pelvic Organ Prolapse. Int Urogynecol J. 2025;36(4):759–781. https://doi.org/10.1007/s00192-024-05948-4.

6. Orazov MR, Radzinskiy VE, Minnullina FF. Diagnosing pelvic organ prolapse: what must a clinicist remember? Practical Medicine. 2025;23(1):37–43. (In Russ.) https://doi.org/10.32000/2072-1757-2025-1-37-43.

7. García-Mejido JA, González-Diaz E, Ortega I, Borrero C, Fernández-Palacín A, Sainz-Bueno JA. 2D ultrasound diagnosis of middle compartment prolapse: a multicenter study. Quant Imaging Med Surg. 2022;12(2):959–966. https://doi.org/10.21037/qims-21-707.

8. van Gruting IMA, Kluivers K, Stankiewicz A, IntHout J, van Delft KWM, Thakar R, Sultan AH. Evaluation of Perineal Descent Measurements on Pelvic Floor Imaging. J Clin Med. 2025;14(2):548. https://doi.org/10.3390/jcm14020548.

9. Davila HH, Deshommes H, Paul A, Abdelhameed S, Filippi C, Bello L, MalaveHuertas D et al. Evaluation of the Pubocervical Fascia With 3-Dimensional Endovaginal Ultrasonography and Correlation With Intraoperative Findings During Robotic Sacrocervicopexy. Urology. 2021;158:81–87. https://doi.org/10.1016/j.urology.2021.05.033.

10. Pannu HK, Genadry R, Kaufman HS, Fishman EK. Computed tomography evaluation of pelvic organ prolapse. Techniques and applications. J Comput Assist Tomogr. 2003;27(5):779–785. https://doi.org/10.1097/00004728-200309000-00016.

11. Chen L, Xue Z, Wu Q. Review of MRI-Based Three-Dimensional Digital Model Reconstruction of Female Pelvic Floor Organs. J Shanghai Jiaotong Univ. 2022;42:381–386. https://doi.org/10.3969/j.issn.1674-8115.2022.03.018.

12. Jha P, Sarawagi R, Malik R, Kumar A, Pushpalatha K. Static and Dynamic Magnetic Resonance Imaging in Female Pelvic Floor Dysfunction: Correlation With Pelvic Organ Prolapse Quantification. Cureus. 2023;15(9):e44915. https://doi.org/10.7759/cureus.44915.

13. Zhang H, Wang Z, Xiao X, Wang J, Zhou B. Dynamic magnetic resonance imaging evaluation before and after operation for pelvic organ prolapse. Abdom Radiol. 2022;47(2):848–857. https://doi.org/10.1007/s00261-021-03361-9.

14. Wang X, He D, Feng F, Ashton-Miller JA, DeLancey JOL, Luo J. Multi-label classification of pelvic organ prolapse using stress magnetic resonance imaging with deep learning. Int Urogynecol J. 2022;33(10):2869–2877. https://doi.org/10.1007/s00192-021-05064-7.

15. Kämpfer C, Pieper CC. Dynamic magnetic resonance imaging of the pelvic floor: Technical aspects. Radiologie. 2023;63(11):793–798. (In German) https://doi.org/10.1007/s00117-023-01212-w.

16. Itri JN, Patel SH. Heuristics and Cognitive Error in Medical Imaging. AJR Am J Roentgenol. 2018;210(5):1097–1105. https://doi.org/10.2214/AJR.17.18907.

17. Krupinski EA. The role of perception in imaging: past and future. Semin Nucl Med. 2011;41(6):392–400. https://doi.org/10.1053/j.semnuclmed.2011.05.002.

18. Batko K, Ślęzak A. The use of Big Data Analytics in healthcare. J Big Data. 2022;9(1):3. https://doi.org/10.1186/s40537-021-00553-4.

19. Martel ME, José-Garcia A, Vens C, De Vos M, Sobanski V. Artificial intelligence for precision medicine. Therapies. 2026;81(2):171–186. https://doi.org/10.1016/j.therap.2025.10.003.

20. Pérez-Sanpablo AI, Quinzaños-Fresnedo J, Gutiérrez-Martínez J, Lozano-Rodríguez IG, Roldan-Valadez E. Transforming Medical Imaging: The Role of Artificial Intelligence Integration in PACS for Enhanced Diagnostic Accuracy and Workflow Efficiency. Curr Med Imaging. 2025;21:e15734056370620. https://doi.org/10.2174/0115734056370620250403030638.

21. Liu J, Wang C, Liu S. Utility of ChatGPT in Clinical Practice. J Med Internet Res. 2023;25:e48568. https://doi.org/10.2196/48568.

22. Mienye ID, Swart TG, Obaido G, Jordan M, Ilono P. Deep Convolutional Neural Networks in Medical Image Analysis: A Review. Information. 2025;16:195. https://doi.org/10.3390/info16030195.

23. Sreelakshmi S, Malu G, Sherly E, Mathew R. M-Net: An Encoder-Decoder Architecture for Medical Image Analysis Using Ensemble Learning. Results Eng. 2023;17:100927. https://doi.org/10.1016/j.rineng.2023.100927.

24. Li J, Chen J, Tang Y, Wang C, Landman BA, Zhou SK. Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives. Med Image Anal. 2023;85:102762. https://doi.org/10.1016/j.media.2023.102762.

25. Jiao W, Hao X, Qin C. The Image Classification Method with CNN-XGBoost Model Based on Adaptive Particle Swarm Optimization. Information. 2021;12:156. https://doi.org/10.3390/info12040156.

26. Dorfman MF, Gasparov AS, Vartanyan SL, Gubanova EV, Dorfman SF. Surgical navigation and 3D modeling in the treatment of uterine fibroids. Gynecology, Obstetrics and Perinatology. 2022;21(5):21–26. (In Russ.) https://doi.org/10.20953/1726-1678-2022-5-21-26.

27. Zhao Z, Poyhonen J, Chen Cai X, Sophie Woodley Hooper F, Ma Y, Hu Y et al. Augmented reality technology in image-guided therapy: State-of-the-art review. Proc Inst Mech Eng H. 2021;235(12):1386–1398. https://doi.org/10.1177/09544119211034357.

28. Rácz K, Seregély B, Kiss RM. Performance of optically tracked instrumented pointers: a systematic literature review. Biomed Eng Online. 2025;24(1):142. https://doi.org/10.1186/s12938-025-01468-4.

29. Kolsanov AV, Zelter PM, Hobta RV, Chaplygin SS, Manukyan AA. First results of intraoperative navigation based on CT and MRI data in a patient with a ceptum pellucidum tumor. Russian Electronic Journal of Radiology. 2020;10(4):271–276. (In Russ.) https://doi.org/10.21569/2222-7415-2020-10-4-271-276.

30. Huri E, Mourad S, Bhide A, Digesu GA. 3D modeling and 3D printing in functional urology: the future perspective. Int Urogynecol J. 2020;31(10):1977–1978. https://doi.org/10.1007/s00192-020-04286-5.

31. Kolsanova AV, Katorkina ES, Kolsanov AV, Minnullina FF, Katorkin SE, Chemidronov SN. Prevention of intraoperative and mesh-associated complications after transvaginal extraperitoneal surgery for pelvic organ prolapse. Ulyanovsk Medico-Biological Journal. 2025;(2):78–93. (In Russ.) https://doi.org/10.34014/2227-1848-2025-2-78-93.

32. Shen P, Fu J, Zhang W, Wen T, Chen C, Liu P. Anatomy of the internal iliac vein around the sacral promontory based on CT three-dimensional (3D) reconstruction. Int Urogynecol J. 2023;34(9):2257–2263. https://doi.org/10.1007/s00192-023-05500-w.

33. Sato H, Kurita M, Kato T, Abe H, Otsuka S, Tsukada S. Iliac vein variation in the sacral promontory on three-dimensional computed tomography angiography: a prospective observational study before laparoscopic sacrocolpopexy. Int Urogynecol J. 2024;35(1):167–173. https://doi.org/10.1007/s00192-023-05681-4.

34. Albanesi G, Giannini A, Carbone M, Russo E, Mannella P, Ferrari V, Simoncini T. Computed-tomography image segmentation and 3D-reconstruction of the female pelvis for the preoperative planning of sacrocolpopexy: prelimi nary data. Int Urogynecol J. 2019;30(5):725–731. https://doi.org/10.1007/s00192-018-3706-3.

35. Omouri A, Rapacchi S, Duclos J, Niddam R, Bellemare ME, Pirró N. 3D Observation of Pelvic Organs with Dynamic MRI Segmentation: A Bridge Toward Patient-Specific Models. Int Urogynecol J. 2024;35(7):1389–1397. https://doi.org/10.1007/s00192-024-05817-0.

36. Chen L, Ashton-Miller JA, DeLancey JOL. A 3D finite element model of anterior vaginal wall support to evaluate mechanisms underlying cystocele formation. J Biomech. 2009;42(10):1371–1377. https://doi.org/10.1016/j.jbiomech.2009.04.043.

37. Withagen MI, Milani AL, de Leeuw JW, Vierhout ME. Development of de novo prolapse in untreated vaginal compartments after prolapse repair with and without mesh: a secondary analysis of a randomised controlled trial. BJOG. 2012;119(3):354–360. https://doi.org/10.1111/j.1471-0528.2011.03231.x.

38. Moseley T, Hicks AJ, Cosgriff-Hernandez EM, Rausch MK, Hakim J. Finite element modeling in obstetrics and gynecology: advances, applications, and challenges. Front Med. 2025;12:1606989. https://doi.org/10.3389/fmed.2025.1606989.

39. Gordon MT, DeLancey JOL, Renfroe A, Battles A, Chen L. Development of anatomically based customizable three-dimensional finite-element model of pelvic floor support system: POP-SIM1.0. Interface Focus. 2019;9(4):20190022. https://doi.org/10.1098/rsfs.2019.0022.

40. Xue X, Wang H, Xie J, Gao Z, Shen J, Yao T. Two-dimensional biomechanical finite element modeling of the pelvic floor and prolapse. Biomech Model Mechanobiol. 2023;22(4):1425–1446. https://doi.org/10.1007/s10237-023-01729-y.

41. Xu Z, Chen N, Wang B, Yang J, Liu H, Zhang X et al. Creation of the biomechanical finite element model of female pelvic floor supporting structure based on thin-sectional high-resolution anatomical images. J Biomech. 2023;146:111399. https://doi.org/10.1016/j.jbiomech.2022.111399.

42. Liu X, Rong Q, Liu Y, Wang J, Xie B, Ren S. Relationship between high intraabdominal pressure and compliance of the pelvic floor support system in women without pelvic organ prolapse: A finite element analysis. Front Med. 2022;9:820016. https://doi.org/10.3389/fmed.2022.820016.

43. Silva MET, Bessa JNM, Parente MPL, Mascarenhas T, Natal Jorge RM, Fernandes AA. Effect of mesh anchoring technique in uterine prolapse repair surgery: A finite element analysis. J Biomech. 2021;127:110649. https://doi.org/10.1016/j.jbiomech.2021.110649.

44. Lallemant M, Shimojyo AA, Mayeur O, Ramanah R, Rubod C, Kerbage Y, Cosson M. Mobility analysis of a posterior sacrospinous fixation using a finite element model of the pelvic system. PLoS ONE. 2024;19(3):e0299012. https://doi.org/10.1371/journal.pone.0299012.

45. Page MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2021;372:n160. https://doi.org/10.1136/bmj.n160.

46. Mak S, Thomas A. An Introduction to Scoping Reviews. J Grad Med Educ. 2022;14(5):561–564. https://doi.org/10.4300/JGME-D-22-00620.1.

47. Peters MDJ, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Synth. 2020;18(10):2119–2126. https://doi.org/10.11124/JBIES-20-00167.

48. Cumpston MS, McKenzie JE, Welch VA, Brennan SE. Strengthening systematic reviews in public health: guidance in the Cochrane Handbook for Systematic Reviews of Interventions, 2nd edition. J Public Health. 2022;44(4):e588–e592. https://doi.org/10.1093/pubmed/fdac036.

49. Feng F, Ashton-Miller JA, DeLancey JOL, Luo J. Convolutional neural networkbased pelvic floor structure segmentation using magnetic resonance imaging in pelvic organ prolapse. Med Phys. 2020;47(9):4281–4293. https://doi.org/10.1002/mp.14377.

50. Duan L, Wang Y, Li J, Zhou N. Exploring the Clinical Diagnostic Value of Pelvic Floor Ultrasound Images for Pelvic Organ Prolapses through Deep Learning. J Supercomput. 2021;77:10699–10720. https://doi.org/10.1007/s11227-021-03682-y.

51. Feng F, Ashton-Miller JA, DeLancey JOL, Luo J. Feasibility of a deep learningbased method for automated localization of pelvic floor landmarks using stress MR images. Int Urogynecol J. 2021;32(11):3069–3075. https://doi.org/10.1007/s00192-020-04626-5.

52. Yin P, Wang H. Evaluation of Nursing Effect of Pelvic Floor Rehabilitation Training on Pelvic Organ Prolapse in Postpartum Pregnant Women under Ultrasound Imaging with Artificial Intelligence Algorithm. Comput Math Methods Med. 2022;2022:1786994. https://doi.org/10.1155/2022/1786994.

53. Szentimrey Z, Ameri G, Hong CX, Cheung RYK, Ukwatta E, Eltahawi A. Automated segmentation and measurement of the female pelvic floor from the mid-sagittal plane of 3D ultrasound volumes. Med Phys. 2023;50(10):6215–6227. https://doi.org/10.1002/mp.16389.

54. García-Mejido JA, Solis-Martín D, Martín-Morán M, Fernández-Conde C, Fernández-Palacín F, Sainz-Bueno JA. Applicability of Deep Learning to Dynamically Identify the Different Organs of the Pelvic Floor in the Midsagittal Plane. Int Urogynecol J. 2024;35(12):2285–2293. https://doi.org/10.1007/s00192-024-05841-0.

55. De Vicari D, Barba M, Cola A, Costa C, Palucci M, Frigerio M. AI-Enhanced 3D Transperineal Ultrasound: Advancing Biometric Measurements for Precise Prolapse Severity Assessment. Bioengineering. 2025;12(7):754. https://doi.org/10.3390/bioengineering12070754.

56. García-Mejido JA, Galán-Paez J, Solis-Martín D, Martín-Morán M, Borrero-Gonzalez C, Fernández-Gomez A et al. Artificial Intelligence to Determine Correct Midsagittal Plane in Dynamic Transperineal Ultrasound. J Clin Ultrasound. 2025;53(7):1470–1478. https://doi.org/10.1002/jcu.24050.

57. García-Mejido JA, Galán-Paez J, Solis-Martín D, Fernández-Palacín F, Fernández-Palacín A, Sainz-Bueno JA. Ultrasound Diagnosis of Pelvic Organ Prolapse Using Artificial Intelligence. J Clin Med. 2025;14(11):3634. https://doi.org/10.3390/jcm14113634.

58. Lai W, Wang G, Zhao Z. A radiomics nomogram utilizing T2-weighted MRI for accurate diagnosis of rectocele. Abdom Radiol. 2026;51(5):2215–2227. https://doi.org/10.1007/s00261-025-05206-1.

59. Su M, Wang S, Liu X. Predicting Postoperative Stress Urinary Incontinence After Prolapse Surgery via Machine Learning and Regression Models: Development and Validation Study. JMIR Med Inform. 2025;13:e76021. https://doi.org/10.2196/76021.

60. Wu S, Wu J, Xu Y, Tan J, Wang R, Zhang X. Automated Evaluation of Female Pelvic Organ Descent on Transperineal Ultrasound: Model Development and Validation. Int Urogynecol J. 202536(8):1693–1700. https://doi.org/10.1007/s00192-025-06211-0.

61. Yang F, Hu R, Wu H, Li S, Peng S, Luo H et al. Combining pelvic floor ultrasonography with deep learning to diagnose anterior compartment organ prolapse. Quant Imaging Med Surg. 2025;15(2):1265–1274. https://doi.org/10.21037/qims-24-772.

62. Zhu S, Zhu X, Zheng B, Wu M, Li Q, Qian C. Building a pelvic organ prolapse diagnostic model using vision transformer on multi-sequence MRI. Med Phys. 2025;52(1):553–564. https://doi.org/10.1002/mp.17441.

63. Howick J, Chalmers I, Glasziou P, Greenhalgh T, Heneghan C, Liberati A et al. The 2011 Oxford CEBM Levels of Evidence (Introductory Document). Oxford Centre for Evidence-Based Medicine; 2011. Available at: https://www.cebm.ox.ac.uk/files/levels-of-evidence/cebm-levels-of-evidence-introduction-2-1.pdf.

64. Dice LR. Measures of the Amount of Ecologic Association Between Species. Ecology. 1945;26(3):297–302. https://doi.org/10.2307/1932409.

65. Riley RD, Collins GS, Ensor J, Archer L, Booth S, Mozumder SI et al. Minimum sample size calculations for external validation of a clinical prediction model with a time-to-event outcome. Stat Med. 2022;41(7):1280–1295. https://doi.org/10.1002/sim.9275.

66. Hosseinzadeh Taher MR, Haghighi F, Gotway MB, Liang J. Large-scale benchmarking and boosting transfer learning for medical image analysis. Med Image Anal. 2025;102:103487. https://doi.org/10.1016/j.media.2025.103487.

67. Azad R, Kazerouni A, Heidari M, Aghdam EK, Molaei A, Jia Y et al. Advances in medical image analysis with vision Transformers: A comprehensive review. Med Image Anal. 2024;91:103000. https://doi.org/10.1016/j.media.2023.103000.

68. Ponce-Bobadilla AV, Schmitt V, Maier CS, Mensing S, Stodtmann S. Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development. Clin Transl Sci. 2024;17(11):e70056. https://doi.org/10.1111/cts.70056.

69. Yang M, Chen C, Wang Z, Long J, Huang R, Qi W, Shi R. Finite element analysis of female pelvic organ prolapse mechanism: current landscape and future opportunities. Front Med. 2024;11:1342645. https://doi.org/10.3389/fmed.2024.1342645.

70. Lallemant M, Vega A, Chambert J, Jacquet E, Ramanah R. Biomechanical interests of supra-cervical hysterectomy with sacrocolpopexy: first study using finite element modeling. Int Urogynecol J. 2021;32(6):1599–1602. https://doi.org/10.1007/s00192-020-04664-z.

71. Xuan R, Yang M, Gao Y, Ren S, Li J, Yang Z et al. A Simulation Analysis of Maternal Pelvic Floor Muscle. Int J Environ Res Public Health. 2021;18(20):10821. https://doi.org/10.3390/ijerph182010821.

72. Diallo MN, Mayeur O, Lecomte-Grosbras P, Patrouix L, Witz JF, Lesaffre F et al. Simulation of the mobility of the pelvic system: influence of fascia between organs. Comput Methods Biomech Biomed Engin. 2022;25(10):1073–1087. https://doi.org/10.1080/10255842.2021.2001460.

73. Li S, Yao TQ, Wang HF, Wen XW, Lin H, Gao ZH et al. Two-dimensional equivalent mechanical modeling and finite element analysis of normal female pelvic floor system. Chinese Journal of Stomatology. 2022;102(28):2189–2195. (In Chinese) https://doi.org/10.3760/cma.j.cn112137-20211108-02478.

74. Silva METD, Bessa JNM, Rynkevic R, Parente MPL, Saraiva MTDQECM, Natal Jorge RM, Fernandes AA. Simulation of vaginal uterosacral ligament suspension damage, mimicking a mesh-augmented apical prolapse repair. Proc Inst Mech Eng H. 2022;236(4):573–582. https://doi.org/10.1177/09544119221074567.

75. Chen J, Zhang J, Wang F. A finite element analysis of different postures and intra-abdominal pressures for the uterine ligaments in maintaining the normal position of uterus. Sci Rep. 2023;13(1):5082. https://doi.org/10.1038/s41598-023-32368-z.

76. Chen J, Zhang J, Yu C. A 3D finite element model of uterus support to evaluate mechanisms underlying uterine prolapse formation. Comput Methods Biomech Biomed Engin. 2023;26(15):1930–1939. https://doi.org/10.1080/10255842.2022.2159759.

77. Bai H, Shen L, Sun X, Fu J, Liu P, Chen C. 3D MRI Biomarkers for Predicting Pelvic Organ Prolapse Risk and Severity. Int Urogynecol J. 2026;37(3):731–740. https://doi.org/10.1007/s00192-025-06338-0.

78. Bai H, Shen L, Liu P, Chen C. Development of an anatomically accurate cavity model for female pelvic organs based on MRI 3D reconstruction: A modeling study. Medicine. 2025;104(45):e45931. https://doi.org/10.1097/MD.0000000000045931.

79. Silva AT, Ferreira NM, Vaz F, Parente M, Fernandes A, Silva ME. Simulation of Pelvic Organ Prolapse Repair Surgery Using Computational Models of Biodegradable Implants Mimicking Uterosacral Ligaments. J Biomech Eng. 2025;147(12):121002. https://doi.org/10.1115/1.4069708.

80. Shen L, Bai H, Sun X, Liu P, Chen C. Correlation between Plane Assessment of Prolapse Degree and Pelvic Organ Prolapse Quantification Scores after Three-Dimensional Reconstruction of Female Pelvic Organ Prolapse. Gynecol Obstet Invest. 2026;91(1):65–75. https://doi.org/10.1159/000546464.

81. Shen L, Bai H. Impact of 3D Reconstruction Combined with Engineering Software Analysis of Pelvic Floor Muscle Morphology on Sexual Dysfunction in Patients with Pelvic Organ Prolapse. Urol Int. 2025;109(6):582–589. https://doi.org/10.1159/000545009.

82. Wang R, Liu G, Jing L, Zhao T, Qian X. Finite element-based prioritization of pelvic floor muscles for rehabilitation to maintain urinary and fecal control in elderly women. Front Physiol. 2025;16:1663545. https://doi.org/10.3389/fphys.2025.1663545.

83. Pinto-Coelho L. How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications. Bioengineering. 2023;10(12):1435. https://doi.org/10.3390/bioengineering10121435.

84. Huang X, Wang D, Li S, Yang L, Zhao J, Guo D. Advancements in artificial intelligence for pelvic floor ultrasound analysis. Am J Transl Res. 2024;16(4):1037–1043. https://doi.org/10.62347/JXQQ5395.

85. Botoncea M, Molnar C, Butiurca VO, Nicolescu CL, Molnar-Varlam C. Artificial Intelligence in the Diagnosis and Imaging-Based Assessment of Pelvic Organ Prolapse: A Scoping Review. Medicina. 2025;61(8):1497. https://doi.org/10.3390/medicina61081497.

86. Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. 2017, pp. 4768–4777. https://doi.org/10.48550/arXiv.1705.07874.

87. Taylor M, Liu X, Denniston A, Esteva A, Ko J, Daneshjou R, Chan AW; SPIRITAI and CONSORT-AI Working Group. Raising the Bar for Randomized Trials Involving Artificial Intelligence: The SPIRIT-Artificial Intelligence and CONSORT-Artificial Intelligence Guidelines. J Invest Dermatol. 2021;141(9):2109–2111. https://doi.org/10.1016/j.jid.2021.02.744.

88. Du H, Yang M, Qi X, Yang L, Wang Z, Yang T et al. Comprehensive Analysis of the Biomechanical Research of Pelvic Organ Prolapse: A Scientometric Approach. J Multidiscip Healthc. 2025;18:1249–1268. https://doi.org/10.2147/JMDH.S473196.