Review
Year 2023,
Issue: 21, 1232 - 1241, 05.01.2024
Abstract
Günümüzde klinisyenler, jinekolojinin çeşitli klinik ve cerrahi uygulamalarında karar vermede ve bilgilerinin arttırılmasında giderek artan oranlarda yapay zeka (AI) teknolojilerini kullanmaktadırlar. Hastalarla ilgili çok büyük miktarda klinik, tıbbi, biyolojik veri hızlı bilgisayar ağlarında karmaşık algoritmalar kullanarak işlenmekte ve matematiksel olarak modellemeler oluşturulmaktadır. Geliştirilen bu matematiksel modellemeler jinekolojik hastalıkların tanısında karşılaşılan zorlukların üstesinden gelme, tedavi yöntemlerinin kişisel değerlendirilmesi ve hasta sonuçlarının iyileştirilmesine olan katkılarıyla umut verici bir geleceğe sahip olduğumuzu göstermektedir. Klinik jinekoloji dalında sanal AI, jinekolojik malignitelerde, yardımlı üreme tekniklerinde, ürojinekolojide teşhis, tedavi algoritmaları ve sonuç tahminine yardımcı olmak için örüntü tanımayı kullanır. Jinekolojik cerrahi dalında fiziksel AI, operasyonlarda bilgisayar destekli veya robotik platformlar biçiminde arttırılmış gerçekliği birleştirerek kullanır. AI, klinik jinekolojide hasta sonuçlarını iyileştirmek için modern tıp uygulamalarına henüz tam dahil edilmemiştir.
Keywords
References
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- 35. Tan SJ, Chen CH, Yeh SD, Lin YH, Tzeng CR. Pregnancy following robot - assisted laparoscopic partial cystectomy and gonadotropin-releasing hormone agonist treatment within three months in an infertile woman with bladder endometriosis. Taiwanese Journal of Obstetrics and Gynecology. 2018;57(1):153–156.
- 36. Chung YJ, Kang SY, Choi MR, Cho HH, Kim JH, Kim MR. Robot-assisted laparoscopic Adenomyomectomy for patients who want to preserve fertility. Yonsei Medical Journal. 2016;57(6):1531–1534.
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Year 2023,
Issue: 21, 1232 - 1241, 05.01.2024
Abstract
Clinicians have increasingly been using artificial intelligence (AI) to make decisions and to increase their knowledge in various clinical and surgical gynecological areas. A vast amount of clinical, medical, and biological patient data is processed in fast computer networks using complex algorithms to create mathematical modeling. The development of these mathematical models gives hope of a promising future with their contribution to overcoming the difficulties encountered in the diagnosis, individualization of treatment plans and improving patient outcomes. Virtual AI in clinical gynecology uses pattern recognition to aid diagnosis, plan treatment, and predict outcomes in gynecological malignancies, assisted reproductive techniques, and urogynecology. In gynecological surgery, physical AI combines augmented reality in operations in the form of computer-aided or robotic platforms. However, AI is yet to be fully incorporated into modern medical practice to improve patient outcomes in clinical gynecology.
References
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- 3. Cavalera F, Zanoni M, Merico V, et al. Neural network-based identification of developmentally competent or incompetent mouse fully-grown oocytes. Journal of Visualized Experiments. 2018;133:56668.
- 4. Goodson SG, White S, Stevans AM, Bhat S, et al. CASAnova: A multiclass support vector machine model for the classification of human sperm motility patterns. Biology of Reproduction. 2017;97(5):698–708.
- 5. Girela JL, Gil D, Johnsson M, Gomez-Torres MJ, Juan JD. Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods. Biology of Reproduction. 2013;88(4):99.
- 6. Akınsal EA, Haznedar B, Baydilli N, Kalinli A, Oztürk A, Ekmekçioğlu O. Artificial neural network for the prediction of chromosomal abnormalities in azoospermic males. Urology Journal. 2018;15(3):122-125.
- 7. Saeedi P, Yee D, Au J, Havelock J. Automatic identification of human blastocyst components via texture. IEEE Transactions on Bio-Medical Engineering. 2017;64(12):2968–2978.
- 8. Bendus AEB, Mayer JF, Shipley SK, Catherino WH. Interobserver and intraobserver variation in day 3 embryo grading. Fertility and Sterility. 2006;86(6):1608–1615.
- 9. Filho ES, Noble JA, Poli M, Griffiths T, Emerson G, Wells D. A method for semi-automatic grading of human blastocyst microscope images. Human Reproduction. 2012;27(9):2641–2648.
- 10. Singh A, Au J, Saeedi P, Havelock J. Automatic segmentation of trophectoderm in microscopic images of human blastocysts. IEEE Transactions on Bio-Medical Engineering. 2015;62(1):382–393.
- 11. Storr A, Venetis C, Cooke S, Kilani S, Ledger W.Time-lapse algorithms and morphological selection of day-5 embryos for transfer: A preclinical validation study. Fertility and Sterility. 2018;109(2):276–283.
- 12. Kaufmann SJ, Eastaugh JL, Snowden S, Smye SW, Sharma V. The application of neural networks in predicting the outcome of in-vitro fertilization. Human Reproduction. 1997;12(7):1454–1457.
- 13. Guh RS, Wu TCJ, Weng SP. Integrating genetic algorithm and decision tree learning for assistance in predicting in vitro fertilization outcomes. Expert Systems with Application. 2011;38(4):4437–4449.
- 14. Guvenir HA, Misirli G, Dilbaz S, Ozdegirmenci O, Demir B, Dilbaz B. Estimating the chance of success in IVF treatment using a ranking algorithm. Medical and Biological Engineering and Computing. 2015;53:911–920.
- 15. Amant F, Mirza MR, Koskas M, Creutzberg CL. Cancer of the corpus uteri. Int J Gynaecol Obstet. 2018;143(2):37-50.
- 16. Hanahan D, Weinberg RA. Hallmarks of cancer: The next generation. Cell. 2011;144(5):646-674.
- 17. Redekop WK, Mladsi D. The faces of personalized medicine: A framework for understanding its meaning and scope. Value Health. 2013;16(6):4-9.
- 18. Wahab CA, Jannot AS, Bonaffini PA, et al. Diagnostic algorithm to differentiate benign atypical leiomyomas from malignant uterine sarcomas with diffusion-weighted MRI. Radiology. 2020;297(2):361-371.
- 19. Sideris M, Emin EI, Abdullah Z,et al. The role of kras in endometrial cancer: A mini-review. Anticancer Res. 2019;39(2):533-539.
- 20. Ford CE, Henry C, Llamosas E, Djordjevic A, Hacker N. Wnt signalling in gynaecological cancers: A future target for personalised medicine? Gynecol Oncol. 2016;140(2):345-351.
- 21. Enshaei A, Robson CN, Edmondson RJ. Artificial ıntelligence systems as prognostic and predictive tools in ovarian cancer. Ann Surg Oncol. 2015;22(12):3970-3975.
- 22. Kyrgiou M, Pouliakis A, Panaiyotitler JG, et al. Personalised management of women with cervical abnormalities using a clinical decision support scoring system. Gynecol Oncol. 2016;141(1):29-35.
- 23. Using Artificial Intelligence to Detect Cervical Cancer. http://directorsblog.nih.gov/2019/01/17/using-artificial-intelligence-to-detect-cervical-cancer/. 2019. Jan;2020.
- 24. Hu L, Bell D, Antani S, et al. Learning and automated evaluation of cervical images for cancer screening. J Natl Cancer Inst. 2019;111(9):923-932.
- 25. Ajao MO, Clark NV, Kelil T, Cohen SL, Einarsson JI. Case report: Three-dimensional printed model for deep infiltrating endometriosis. J Minim Invasive Gynecol. 2017;24:1239–1242.
- 26. Waran V, Narayanan V, Karuppiah R, Owen SL, Aziz T. Utility of multimaterial 3D printers in creating models with pathological entities to enhance the training experience of neurosurgeons. J Neurosurg. 2014;120:489–492.
- 27. Song E, Yu F, Liu H, et al. A novel endoscope system for position detection and depth estimation of the ureter. J Med Syst. 2016;40:266.
- 28. Tan SJ, Lin CK, Fu PT, et al. Robotic surgery in complicated gynecologic diseases: Experience of Tri-Service General Hospital in Taiwan. Taiwanese Journal of Obstetrics and Gynecology. 2012;51(1):18–25.
- 29. Estes SJ, Waldman I, Gargiulo AR. Robotics and reproductive surgery. Seminars in Reproductive Medicine. 2017;35(4):364–377.
- 30. Dirie NI, Wang Q, Wang S. Two-dimensional versus three-dimensional laparoscopic systems in urology: A systematic review and meta-analysis. J Endourol. 2018;32:781–790.
- 31. Advincula AP, Xu X, Goudeau St, Ransom SB. Robot-assisted laparoscopic myomectomy versus abdominal myomectomy: a comparison of short-term surgical outcomes and immediate costs. Journal of Minimally Invasive Gynecology. 2007;14(6):698–705.
- 32. Barakat EE, Bedaiwy MA, Zimberg S, Nutter B, Nosseir M, Falcone T. Robotic-assisted, laparoscopic, and abdominal myomectomy: A comparison of surgical outcomes. Obstetrics and Gynecology. 2011;117(2):256–266.
- 33. Bedient CE, Magrina JF, Noble BN, Kho RM. Comparison of robotic and laparoscopic myomectomy. American Journal of Obstetrics and Gynecology. 2009;201(6):566.e1–5.
- 34. Nezhat C, Lewis M, Kotikela S, et al. Robotic versus standard laparoscopy for the treatment of endometriosis. Fertility and Sterility. 2010;94(7):2758–2760.
- 35. Tan SJ, Chen CH, Yeh SD, Lin YH, Tzeng CR. Pregnancy following robot - assisted laparoscopic partial cystectomy and gonadotropin-releasing hormone agonist treatment within three months in an infertile woman with bladder endometriosis. Taiwanese Journal of Obstetrics and Gynecology. 2018;57(1):153–156.
- 36. Chung YJ, Kang SY, Choi MR, Cho HH, Kim JH, Kim MR. Robot-assisted laparoscopic Adenomyomectomy for patients who want to preserve fertility. Yonsei Medical Journal. 2016;57(6):1531–1534.
- 37. Scheib SA, Fader AN. Gynecologic robotic laparoendoscopic single-site surgery: Prospective analysis of feasibility, safety, and technique. American Journal of Obstetrics and Gynecology. 2015;212(2):179.e1-8.
- 38. Bogliolo S, Ferrero S, Cassani C, et al. Single-site Versus multiport robotic hysterectomy in benign gynecologic diseases: A retrospective evaluation of surgical outcomes and cost analysis. Journal of Minimally Invasive Gynecology. 2016;23(4):603–609.
There are 38 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Clinical Sciences |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | January 8, 2024 |
| Publication Date | January 5, 2024 |
| Acceptance Date | December 5, 2023 |
| Published in Issue | Year 2023 Issue: 21 |
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)