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THE ROLE OF ARTIFICIAL INTELLIGENCE IN GYNAECOLOGY AND OBSTETRICS

Yıl 2025, Cilt: 6 Sayı: 1, 57 - 67, 01.05.2025

Öz

The development of artificial intelligence attracts attention with its applications in the field of health as in many fields. Technological developments in theories and learning algorithms show great progress in information systems used in the medical field. Artificial intelligence offers healthcare professionals the opportunity to make appropriate decisions in case management and diagnosis; therefore, it is used in many areas in the field of medicine. Machine learning and deep learning methods, which are sub-branches of artificial intelligence, detect complex situations from large data sets and make predictions using these situations. Although there are various challenges, the applications of artificial intelligence in gynaecology and obstetrics show a remarkable development.
This study aims to investigate the role of artificial intelligence in gynaecology and obstetrics applications. At the same time, with the development and progress of artificial intelligence, the role of artificial intelligence in different stages of pregnancy, ultrasound diagnosis, preterm birth and postpartum period, as well as in gynaecology is discussed.

Kaynakça

  • 1. Malani S, Shrivastava D, Raka M. A comprehensive review of the role of artificial ıntelligence in obstetrics and gynecology. Cureus. 2023;15:e34891. doi: 10.7759/cureus.34891.
  • 2. Amisha Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J fam med prim care. 2019; 8:2328–2331. doi: 10.4103/jfmpc.jfmpc_440_19.
  • 3. Ashrafian H, Darzi A, Athanasiou T. A novel modification of the Turing test for artificial intelligence and robotics in healthcare. Int j med robot. 2015; 11:38–43. doi: 10.1002/rcs.1570.
  • 4. Liu PR, Lu L, Zhang JY, Huo TT, Liu SX, Ye ZW. Application of artificial intelligence in medicine. An overview curr med seci. 2021; 41:1105–1115. doi: 10.1007/s11596-021-2474-3.
  • 5. Xu J, Xue K, Zhang K. Current status and future trends of clinical diagnoses via image-based deep learning. Theranostics infant collaborative group. 2016;9:7556-7565. doi: 10.7150/thno.38065.
  • 6. Brocklehurst P. A study of an intelligent system to support decision making in the management of labour using the cardiotocograph–the ınfant study protocol. Bmc pregnancy and childbirth, 2016;16, 1-15.
  • 7. Makary MA, Daniel M. Medical error the third leading cause of death in the us. Bmj, 2016;353.
  • 8. Vickers H, Jha S. Medicolegal issues in gynaecology. Obstet gynaecol reprod med. 2020; 30:43–47.
  • 9. Williams P, Murchie P, Bond C. Patient and primary care delays in the diagnostic pathway of gynaecological cancers: a systematic review of influencing factors. J gen pract. 2019; 69:0–11.
  • 10. Amant F, Mirza MR, Creutzberg CL. Cancer of the corpus uteri. Int j gynaecol obstet. 2012; 2:110–117.
  • 11. Liu L, Jiao Y, Li X, Ouyang Y, Shi D. Machine learning algorithms to predict early pregnancy loss after in vitro fertilization-embryo transfer with fetal heart rate as a strong predictor. Comput methods programs biomed. 2020; 196:105624.
  • 12. Drukker L, Noble JA, Papageorghiou AT. Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound obstet gynecol. 2020; 56:498–505. doi: 10.1002/uog.22122.
  • 13. Jost E, Kosian P, Jimenez Cruz J, Albarqouni S, Gembruch U, Strizek B, Recker F. Evolving the era of 5d ultrasound a systematic literature review on the applications for artificial ıntelligence ultrasound imaging in obstetrics and gynecology. J clin med. 2023; 12:6833. doi: 10.3390/jcm12216833.
  • 14. Drukker L, Noble JA, Papageorghiou AT. Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound obstet gynecol. 2020; 56:498–505.
  • 15. Wang R, Pan W, Jin L, et al. Artificial intelligence in reproductive medicine. Reproduction. 2019; 158:0–54.
  • 16. Iftikhar P, Kuijpers MV, Khayyat A, Iftikhar A, Degouvia M. Artificial intelligence: a new paradigm in obstetrics and gynecology research and clinical practice. Cureus. 2020; 12:10.
  • 17. Emin EI, Emin E, Papalois A, Willmott F, Clarke S, Sideris M. Artificial intelligence in obstetrics and gynaecology: is this the way forward. In vivo. 2019; 33:1547–1551.
  • 18. Kim YH. Artificial intelligence in medical ultrasonography: driving on an unpaved road. Ultrasonography. 2021; 40:313–317.
  • 19. Wu Y, Shen Y, Sun H. Intelligent algorithm-based analysis on ultrasound image characteristics of patients with lower extremity arteriosclerosis occlusion and its correlation with diabetic mellitus foot. J healthc eng. 2021;7758206.
  • 20. Gupta K, Balyan K, Lamba B, Puri M, Sengupta D, Kumar M. Ultrasound placental image texture analysis using artificial intelligence to predict hypertension in pregnancy. J matern fetal neonatal med. 2022; 35:5587–5594.
  • 21. Murillo Llorente MT, Fajardo Montañana C, Perez-Bermejo M, Tohoku J. Artificial neural network for predicting iodine deficiency in the first trimester of pregnancy in healthy women. Exp med. 2020; 252:185–191.
  • 22.Güvercin CH. Tıpta yapay zekâ ve etik. Türkiye klinikleri. 2020;7-13.
  • 23.Çolak M, Öztürk H. Ebelikte yeterlilik, yetkinlik ve teknoloji kullanımı. Türkiye klinikleri sağlık bilimleri dergisi. 2021; 6,2; 340-349. doi:10.5336/healthsci.2020-75575
  • 24. Pergialiotis V, Pouliakis A, Parthenis C, Damaskou V, Chrelias C, Papantoniou N, Panayiotides I. The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women. Public health. 2018; 164:1–6.
  • 25. Burgos Artizzu XP, Coronado Gutiérrez D, Valenzuela Alcaraz B, et al. Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the estimation of gestational age. Am j obstet gynecol mfm. 2021; 3:100462.
  • 26. Sakai A, Komatsu M, Komatsu R, et al. Medical professional enhancement using explainable artificial intelligence in fetal cardiac ultrasound screening. Biomedicines. 2022; 10:551
  • 27. Lin M, He X, Guo H, et al. Use of real-time artificial intelligence in detection of abnormal image patterns in standard sonographic reference planes in screening for fetal intracranial malformations. Ultrasound obstet gynecol. 2022; 59:304–316.
  • 28. Sun Q, Zou X, Yan Y, et al. Machine learning-based prediction model of preterm birth using electronic health record. J healthc eng. 2022; 9635526.
  • 29. Zaninovic N, Rosenwaks Z. Artificial intelligence in human in vitro fertilization and embryology. Fertil steril. 2020; 114: 914–920.
  • 30. 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;1786994.
  • 31. Goodday SM, Karlin E, Brooks A, et al. Better understanding of the metamorphosis of pregnancy (BUMP): protocol for a digital feasibility study in women from preconception to postpartum. Npj digit med. 2022; 5:40.
  • 32.Çoban N, Eryiğit T, Dülcek S, Derya Beydağ K, Ortabağ T. Hemşirelik mesleğinde yapay zeka ve robot teknolojilerinin yeri. Fenerbahçe üniversitesi sağlık bilimleri dergisi. 2022; 2,1; 378-385.
  • 33. Ueno Y, Forghani B, Forghani R, et al. Endometrial carcinoma: Mr imaging-based texture model for preoperative risk stratification A preliminary analysis. Radiology. 2017; 284:748–757.
  • 34. Dong HC, Dong HK, Yu MH, Lin YH, Chang CC. Using deep learning with convolutional neural network approach to identify the invasion depth of endometrial cancer in myometrium using mr images: a pilot study. Int j environ res public health. 2020; 17:5993.
  • 35. Malek M, Gity M, Alidoosti A, et al. A machine learning approach for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted mrı parameters. Eur j radiol. 2019; 110:203–211.
  • 36. Yanbai XUE, Yuemei ZHAO, Liuye YAO, Weitao LI, Zhiyu QIAN. Development of diffuse reflectance spectroscopy detection and analysis system for cervical cancer. Chinese journal of medical instrumentation. 2019; 43(3), 157-161.
  • 37. Sherin L, Sohail A, Shujaat S. Time dependent AI modeling of the anticancer efficacy of synthesized gallic acid analogues. Comput biol chem. 2019; 79:137–146.
  • 38. Arsalan M, Haider A, Choi J, Park KR. Detecting blastocyst components by artificial intelligence for human embryological analysis to improve success rate of in vitro fertilization. J Pers Med. 2022; 12:124.
  • 39. Siristatidis C, Stavros S, Drakeley A, et al. Omics and artificial intelligence to improve in vitro fertilization (IVF) success: A proposed protocol diagnostics (basel). 2021; 11:743.
  • 40. Xie HN, Wang N, He M, et al. Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal: AI classifies fetal brain images. Ultrasound obstet gynecol. 2020; 56:579–587.
  • 41. Siristatidis C, Stavros S, Drakeley A, et al. Omics and artificial intelligence to improve in vitro fertilization (IVF) success: A proposed protocol. Diagnostics (basel) 2021; 11:74.

Jinekoloji ve Doğumda Yapay Zekanın Rolü

Yıl 2025, Cilt: 6 Sayı: 1, 57 - 67, 01.05.2025

Öz

Yapay zekanın gelişmesi, birçok alanda olduğu gibi sağlık alanındaki uygulamalarıyla da dikkat çekmektedir. Teorilerdeki ve öğrenme algoritmalarındaki teknolojik gelişmeler, tıbbi alanda kullanılan bilişim sistemlerinde büyük ilerleme göstermektedir. Yapay zeka, sağlık profesyonellerine vaka yönetimi ve teşhis konularında uygun karar alma imkanı sunmakta; bu nedenle tıp alanında birçok alanda kullanılmaktadır. Yapay zekanın alt dalları olan makine öğrenimi ve derin öğrenme yöntemleri, büyük veri kümelerinden karmaşık durumları tespit etmekte ve bu durumları kullanarak tahminlerde bulunmaktadır. Her ne kadar çeşitli zorluklar bulunsa da yapay zekanın jinekolojideki ve doğumdaki uygulamaları dikkate değer bir gelişme göstermektedir.
Bu çalışma, jinekoloji ve obstetri uygulamalarında yapay zekanın rolünü araştırmayı hedeflemektedir. Aynı zamanda, yapay zekanın gelişimi ve ilerlemesiyle birlikte, gebeliğin farklı aşamalarında, ultrason tanısında, erken doğum ve doğum sonrası dönemde ayrıca jinekolojide yapay zekanın rolü ele alınmaktadır.

Kaynakça

  • 1. Malani S, Shrivastava D, Raka M. A comprehensive review of the role of artificial ıntelligence in obstetrics and gynecology. Cureus. 2023;15:e34891. doi: 10.7759/cureus.34891.
  • 2. Amisha Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J fam med prim care. 2019; 8:2328–2331. doi: 10.4103/jfmpc.jfmpc_440_19.
  • 3. Ashrafian H, Darzi A, Athanasiou T. A novel modification of the Turing test for artificial intelligence and robotics in healthcare. Int j med robot. 2015; 11:38–43. doi: 10.1002/rcs.1570.
  • 4. Liu PR, Lu L, Zhang JY, Huo TT, Liu SX, Ye ZW. Application of artificial intelligence in medicine. An overview curr med seci. 2021; 41:1105–1115. doi: 10.1007/s11596-021-2474-3.
  • 5. Xu J, Xue K, Zhang K. Current status and future trends of clinical diagnoses via image-based deep learning. Theranostics infant collaborative group. 2016;9:7556-7565. doi: 10.7150/thno.38065.
  • 6. Brocklehurst P. A study of an intelligent system to support decision making in the management of labour using the cardiotocograph–the ınfant study protocol. Bmc pregnancy and childbirth, 2016;16, 1-15.
  • 7. Makary MA, Daniel M. Medical error the third leading cause of death in the us. Bmj, 2016;353.
  • 8. Vickers H, Jha S. Medicolegal issues in gynaecology. Obstet gynaecol reprod med. 2020; 30:43–47.
  • 9. Williams P, Murchie P, Bond C. Patient and primary care delays in the diagnostic pathway of gynaecological cancers: a systematic review of influencing factors. J gen pract. 2019; 69:0–11.
  • 10. Amant F, Mirza MR, Creutzberg CL. Cancer of the corpus uteri. Int j gynaecol obstet. 2012; 2:110–117.
  • 11. Liu L, Jiao Y, Li X, Ouyang Y, Shi D. Machine learning algorithms to predict early pregnancy loss after in vitro fertilization-embryo transfer with fetal heart rate as a strong predictor. Comput methods programs biomed. 2020; 196:105624.
  • 12. Drukker L, Noble JA, Papageorghiou AT. Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound obstet gynecol. 2020; 56:498–505. doi: 10.1002/uog.22122.
  • 13. Jost E, Kosian P, Jimenez Cruz J, Albarqouni S, Gembruch U, Strizek B, Recker F. Evolving the era of 5d ultrasound a systematic literature review on the applications for artificial ıntelligence ultrasound imaging in obstetrics and gynecology. J clin med. 2023; 12:6833. doi: 10.3390/jcm12216833.
  • 14. Drukker L, Noble JA, Papageorghiou AT. Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound obstet gynecol. 2020; 56:498–505.
  • 15. Wang R, Pan W, Jin L, et al. Artificial intelligence in reproductive medicine. Reproduction. 2019; 158:0–54.
  • 16. Iftikhar P, Kuijpers MV, Khayyat A, Iftikhar A, Degouvia M. Artificial intelligence: a new paradigm in obstetrics and gynecology research and clinical practice. Cureus. 2020; 12:10.
  • 17. Emin EI, Emin E, Papalois A, Willmott F, Clarke S, Sideris M. Artificial intelligence in obstetrics and gynaecology: is this the way forward. In vivo. 2019; 33:1547–1551.
  • 18. Kim YH. Artificial intelligence in medical ultrasonography: driving on an unpaved road. Ultrasonography. 2021; 40:313–317.
  • 19. Wu Y, Shen Y, Sun H. Intelligent algorithm-based analysis on ultrasound image characteristics of patients with lower extremity arteriosclerosis occlusion and its correlation with diabetic mellitus foot. J healthc eng. 2021;7758206.
  • 20. Gupta K, Balyan K, Lamba B, Puri M, Sengupta D, Kumar M. Ultrasound placental image texture analysis using artificial intelligence to predict hypertension in pregnancy. J matern fetal neonatal med. 2022; 35:5587–5594.
  • 21. Murillo Llorente MT, Fajardo Montañana C, Perez-Bermejo M, Tohoku J. Artificial neural network for predicting iodine deficiency in the first trimester of pregnancy in healthy women. Exp med. 2020; 252:185–191.
  • 22.Güvercin CH. Tıpta yapay zekâ ve etik. Türkiye klinikleri. 2020;7-13.
  • 23.Çolak M, Öztürk H. Ebelikte yeterlilik, yetkinlik ve teknoloji kullanımı. Türkiye klinikleri sağlık bilimleri dergisi. 2021; 6,2; 340-349. doi:10.5336/healthsci.2020-75575
  • 24. Pergialiotis V, Pouliakis A, Parthenis C, Damaskou V, Chrelias C, Papantoniou N, Panayiotides I. The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women. Public health. 2018; 164:1–6.
  • 25. Burgos Artizzu XP, Coronado Gutiérrez D, Valenzuela Alcaraz B, et al. Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the estimation of gestational age. Am j obstet gynecol mfm. 2021; 3:100462.
  • 26. Sakai A, Komatsu M, Komatsu R, et al. Medical professional enhancement using explainable artificial intelligence in fetal cardiac ultrasound screening. Biomedicines. 2022; 10:551
  • 27. Lin M, He X, Guo H, et al. Use of real-time artificial intelligence in detection of abnormal image patterns in standard sonographic reference planes in screening for fetal intracranial malformations. Ultrasound obstet gynecol. 2022; 59:304–316.
  • 28. Sun Q, Zou X, Yan Y, et al. Machine learning-based prediction model of preterm birth using electronic health record. J healthc eng. 2022; 9635526.
  • 29. Zaninovic N, Rosenwaks Z. Artificial intelligence in human in vitro fertilization and embryology. Fertil steril. 2020; 114: 914–920.
  • 30. 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;1786994.
  • 31. Goodday SM, Karlin E, Brooks A, et al. Better understanding of the metamorphosis of pregnancy (BUMP): protocol for a digital feasibility study in women from preconception to postpartum. Npj digit med. 2022; 5:40.
  • 32.Çoban N, Eryiğit T, Dülcek S, Derya Beydağ K, Ortabağ T. Hemşirelik mesleğinde yapay zeka ve robot teknolojilerinin yeri. Fenerbahçe üniversitesi sağlık bilimleri dergisi. 2022; 2,1; 378-385.
  • 33. Ueno Y, Forghani B, Forghani R, et al. Endometrial carcinoma: Mr imaging-based texture model for preoperative risk stratification A preliminary analysis. Radiology. 2017; 284:748–757.
  • 34. Dong HC, Dong HK, Yu MH, Lin YH, Chang CC. Using deep learning with convolutional neural network approach to identify the invasion depth of endometrial cancer in myometrium using mr images: a pilot study. Int j environ res public health. 2020; 17:5993.
  • 35. Malek M, Gity M, Alidoosti A, et al. A machine learning approach for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted mrı parameters. Eur j radiol. 2019; 110:203–211.
  • 36. Yanbai XUE, Yuemei ZHAO, Liuye YAO, Weitao LI, Zhiyu QIAN. Development of diffuse reflectance spectroscopy detection and analysis system for cervical cancer. Chinese journal of medical instrumentation. 2019; 43(3), 157-161.
  • 37. Sherin L, Sohail A, Shujaat S. Time dependent AI modeling of the anticancer efficacy of synthesized gallic acid analogues. Comput biol chem. 2019; 79:137–146.
  • 38. Arsalan M, Haider A, Choi J, Park KR. Detecting blastocyst components by artificial intelligence for human embryological analysis to improve success rate of in vitro fertilization. J Pers Med. 2022; 12:124.
  • 39. Siristatidis C, Stavros S, Drakeley A, et al. Omics and artificial intelligence to improve in vitro fertilization (IVF) success: A proposed protocol diagnostics (basel). 2021; 11:743.
  • 40. Xie HN, Wang N, He M, et al. Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal: AI classifies fetal brain images. Ultrasound obstet gynecol. 2020; 56:579–587.
  • 41. Siristatidis C, Stavros S, Drakeley A, et al. Omics and artificial intelligence to improve in vitro fertilization (IVF) success: A proposed protocol. Diagnostics (basel) 2021; 11:74.
Toplam 41 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Ebelik (Diğer)
Bölüm İnceleme Makalesi
Yazarlar

Nesrin Çörekçioğlu 0000-0002-0436-4210

Aysenur Menekşe 0009-0001-0821-4316

Gönderilme Tarihi 30 Mayıs 2024
Kabul Tarihi 17 Kasım 2024
Yayımlanma Tarihi 1 Mayıs 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 6 Sayı: 1

Kaynak Göster

Vancouver Çörekçioğlu N, Menekşe A. Jinekoloji ve Doğumda Yapay Zekanın Rolü. TSAD. 2025;6(1):57-6.