Artificial intelligence in urogynecology: literature review

Introduction. Artificial intelligence (AI) is becoming a key tool in modern medicine, enhancing diagnostic accuracy, treatment personalization, and patient management. In urogynecology, the integration of AI opens new opportunities for improving medical image interpretation, urodynamic data analysis, outcome prediction, and remote patient monitoring.

Objective. To systematize current evidence on the use of AI technologies in urogynecology, assess their diagnostic and prognostic potential, and outline prospects for their implementation in clinical practice.

Materials & methods. A comprehensive search and analysis of Russian and international publications were conducted in eLIBRARY, PubMed, Scopus, and Web of Science databases for the period 2020–2025 using the keywords: urogynecology, female urology, artificial intelligence, machine learning. Studies describing the use of AI in diagnostics, treatment, prognosis, and patient monitoring were included.

Results. AI is actively applied for analyzing electronic medical records, interpreting urodynamic tests, and segmenting ultrasound and MRI images, thereby improving the accuracy of diagnosing pelvic organ prolapse and urinary incontinence. In surgical practice, computer vision and augmented reality technologies enhance the precision and safety of operations. Predictive algorithms enable assessment of postoperative complications and recurrence risks, while telemedicine and wearable AI-based systems provide continuous patient monitoring. Despite their promising potential, most models still require additional clinical validation and standardization.

Conclusions. The use of AI in urogynecology contributes to improved diagnostic accuracy, personalized treatment, and better clinical outcomes. Broader implementation requires multicenter studies, further algorithm development, and the establishment of ethical and legal frameworks for AI integration into medical practice.

1. Kurdoğlu M, Khaki A. The Use of Artificial Intelligence in Urogynecology. International Journal of Women’s Health and Reproduction Sciences. 2023;12(1):1-2. DOI: 10.15296/ijwhr.2024.6003

2. Shapiro J, Lyakhovitsky A. Revolutionizing teledermatology: Exploring the integration of artificial intelligence, including Generative Pretrained Transformer chatbots for artificial intelligence-driven anamnesis, diagnosis, and treatment plans. Clin Dermatol. 2024;42(5):492-497. DOI: 10.1016/j.clindermatol.2024.06.020

3. Johnson CM, Bradley CS, Kenne KA, Rabice S, Takacs E, Vollstedt A, Kowalski JT. Evaluation of ChatGPT for Pelvic Floor Surgery Counseling. Urogynecology (Phila). 2024;30(3):245-250. DOI: 10.1097/SPV.0000000000001459

4. Wang HS, Cahill D, Panagides J, Nelson CP, Wu HT, Estrada C. Pattern recognition algorithm to identify detrusor overactivity on urodynamics. Neurourol Urodyn. 2021;40(1):428-434. DOI: 10.1002/nau.24578

5. Hobbs KT, Choe N, Aksenov LI, Reyes L, Aquino W, Routh JC, Hokanson JA. Machine Learning for Urodynamic Detection of Detrusor Overactivity. Urology. 2022;159:247-254. DOI: 10.1016/j.urology.2021.09.027

6. Onal S, Lai-Yuen S, Bao P, Weitzenfeld A, Greene K, Kedar R, Hart S. Assessment of a semiautomated pelvic floor measurement model for evaluating pelvic organ prolapse on MRI. Int Urogynecol J. 2014;25(6):767- 773. DOI: 10.1007/s00192-013-2287-4

7. Ikryannikov E.O. Three-dimensional reconstruction of the pelvic bones on MRI scans. Digital Diagnostics. 2023;4(1S):60-61. (In Russian). DOI: 10.17816/DD430345

8. Zhang M, Lin X, Zheng Z, Chen Y, Ren Y, Zhang X. Artificial intelligence models derived from 2D transperineal ultrasound images in the clinical diagnosis of stress urinary incontinence. Int Urogynecol J. 2022;33(5):1179-1185. DOI: 10.1007/s00192-021-04859-y

9. 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. DOI: 10.1002/mp.16389

10. Aleissa M, Osumah T, Drelichman E, Mittal V, Bhullar J. Current Status and Role of Artificial Intelligence in Anorectal Diseases and Pelvic Floor Disorders. JSLS. 2024;28(2):e2024.00007. DOI: 10.4293/JSLS.2024.00007

11. 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. DOI: 10.1155/2022/1786994

12. Mascarenhas M, Alencoão I, Carinhas MJ, Martins M, Cardoso P, Mendes F, Fernandes J, Ferreira J, Macedo G, Zulmira Macedo R. Artificial Intelligence and Colposcopy: Automatic Identification of Cervical Squamous Cell Carcinoma Precursors. J Clin Med. 2024;13(10):3003. DOI: 10.3390/jcm13103003

13. Negassi M, Suarez-Ibarrola R, Hein S, Miernik A, Reiterer A. Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects. World J Urol. 2020;38(10):2349-2358. DOI: 10.1007/s00345-019-03059-0

14. Daykan Y, O’Reilly BA. The role of artificial intelligence in the future of urogynecology. Int Urogynecol J. 2023;34(8):1663-1666. DOI: 10.1007/s00192-023-05612-3

15. Sheyn D, Ju M, Zhang S, Anyaeche C, Hijaz A, Mangel J, Mahajan S, Conroy B, El-Nashar S, Ray S. Development and Validation of a Machine Learning Algorithm for Predicting Response to Anticholinergic Medications for Overactive Bladder Syndrome. Obstet Gynecol. 2019;134(5):946-957. DOI: 10.1097/AOG.0000000000003517

16. Mascagni P, Alapatt D, Sestini L, Altieri MS, Madani A, Watanabe Y, Alseidi A, Redan JA, Alfieri S, Costamagna G, Boškoski I, Padoy N, Hashimoto DA. Computer vision in surgery: from potential to clinical value. NPJ Digit Med. 2022;5(1):163. DOI: 10.1038/s41746-022-00707-5

17. Serban N, Kupas D, Hajdu A, Török P, Harangi B. Distinguishing the Uterine Artery, the Ureter, and Nerves in Laparoscopic Surgical Images Using Ensembles of Binary Semantic Segmentation Networks. Sensors (Basel). 2024;24(9):2926. DOI: 10.3390/s24092926

18. Kitaguchi D, Harai Y, Kosugi N, Hayashi K, Kojima S, Ishikawa Y, Yamada A, Hasegawa H, Takeshita N, Ito M. Artificial intelligence for the recognition of key anatomical structures in laparoscopic colorectal surgery. Br J Surg. 2023;110(10):1355-1358. DOI: 10.1093/bjs/znad249

19. Siff LN, Mehta N. An Interactive Holographic Curriculum for Urogynecologic Surgery. Obstet Gynecol. 2018;132 Suppl 1:27S-32S. DOI: 10.1097/AOG.0000000000002860

20. Dedeene L, Van Elslande J, Dewitte J, Martens G, De Laere E, De Jaeger P, De Smet D. An artificial intelligence-driven support tool for prediction of urine culture test results. Clin Chim Acta. 2024;562:119854. DOI: 10.1016/j.cca.2024.119854

21. Goździkiewicz N, Zwolińska D, Polak-Jonkisz D. The Use of Artificial Intelligence Algorithms in the Diagnosis of Urinary Tract InfectionsA Literature Review. J Clin Med. 2022;11(10):2734. DOI: 10.3390/jcm11102734

22. Jelovsek JE, Chagin K, Brubaker L, Rogers RG, Richter HE, Arya L, Barber MD, Shepherd JP, Nolen TL, Norton P, Sung V, Menefee S, Siddiqui N, Meikle SF, Kattan MW; Pelvic Floor Disorders Network. A model for predicting the risk of de novo stress urinary incontinence in women undergoing pelvic organ prolapse surgery. Obstet Gynecol. 2014;123(2 Pt 1):279-287. DOI: 10.1097/AOG.0000000000000094

23. Sabadell J, Salicrú S, Montero-Armengol A, Rodriguez-Mias N, GilMoreno A, Poza JL. External validation of de novo stress urinary incontinence prediction model after vaginal prolapse surgery. Int Urogynecol J. 2019;30(10):1719-1723. DOI: 10.1007/s00192-018-3805-1

24. Jelovsek JE, Chagin K, Lukacz ES, Nolen TL, Shepherd JP, Barber MD, Sung V, Brubaker L, Norton PA, Rahn DD, Smith AL, Ballard A, Jeppson P, Meikle SF, Kattan MW; NICHD Pelvic Floor Disorders Network. Models for Predicting Recurrence, Complications, and Health Status in Women After Pelvic Organ Prolapse Surgery. Obstet Gynecol. 2018;132(2):298- 309. DOI: 10.1097/AOG.0000000000002750

25. Rhodes S, Sahmoud A, Jelovsek JE, Bretschneider CE, Gupta A, Hijaz AK, Sheyn D. Validation and Recalibration of a Model for Predicting SurgicalSite Infection After Pelvic Organ Prolapse Surgery. Int Urogynecol J. 2025;36(2):431-438. DOI: 10.1007/s00192-024-06025-6

26. Galkin A.V., Galkina N.G., Kaganov O.I., Karamysheva N.S., Kalinina E.A., Shapovalov I.S. Artificial neural network in prediction of pelvic organ prolapse. Aspirantskiy Vestnik Povolzhiya. 2020;20(5-6):132- 137. (In Russian). DOI: 10.17816/2072-2354.2020.20.3.132-137

27. Bentaleb J, Larouche M. Innovative use of artificial intelligence in urogynecology. Int Urogynecol J. 2020;31(7):1287-1288. DOI: 10.1007/s00192-020-04243-2

28. Kuru K, Ansell D, Jones M, De Goede C, Leather P. Feasibility study of intelligent autonomous determination of the bladder voiding need to treat bedwetting using ultrasound and smartphone ML techniques : Intelligent autonomous treatment of bedwetting. Med Biol Eng Comput. 2019;57(5):1079-1097. DOI: 10.1007/s11517-018-1942-9

29. Kim ES, Eun SJ, Kim KH. Artificial Intelligence-Based Patient Monitoring System for Medical Support. Int Neurourol J. 2023;27(4):280-286. DOI: 10.5213/inj.2346338.169

30. Eun SJ, Lee JY, Jung H, Kim KH. Personalized Urination Activity Management Based on an Intelligent System Using a Wearable Device. Int Neurourol J. 2021;25(3):229-235. DOI: 10.5213/inj.2142276.138

31. Nyström E, Söderström L, Samuelsson E. Self-management of incontinence using a free mobile app: factors associated with improvement. Int Urogynecol J. 2022;33(4):877-885. DOI: 10.1007/s00192-021-04755-5

32. Han MN, Grisales T, Sridhar A. Evaluation of a Mobile Application for Pelvic Floor Exercises. Telemed J E Health. 2019;25(2):160-164. DOI: 10.1089/tmj.2017.0316

33. Basu K, Sinha R, Ong A, Basu T. Artificial Intelligence: How is It Changing Medical Sciences and Its Future? Indian J Dermatol. 2020;65(5):365-370. DOI: 10.4103/ijd.IJD_421_20

34. Seval MM, Varlı B. Current developments in artificial intelligence from obstetrics and gynecology to urogynecology. Front Med (Lausanne). 2023;10:1098205. DOI: 10.3389/fmed.2023.1098205

35. Lucas GM, Gratch J, King A, Morency L. It’s only a computer: Virtual humans increase willingness to disclose. Comput. Hum. Behav. 2014;37:94-100. DOI: 10.1016/j.chb.2014.04.043

36. Kogan M.I., Ivanov S.N. “Catch Me If You Can”. ChatGPT today: artificial intelligence able to write a scientific paper for us or is it a game of imitation? Urology Herald. 2023;11(3):10-15. (In Russian). DOI: 10.21886/2308-6424-2023-11-3-10-15

37. Shchamkhalova K.K., Merinov D.S., Artemov A.V., Gurbanov Sh. Sh. Artificial intelligence and neural networks in urology. Experimental and Clinical Urology. 2023;16(2):32-37. (In Russian). DOI: 10.29188/2222-8543-2023-16-2-32-37