Use of Artificial Intelligence Technology in the Perinatal Period

Abstract

Artificial intelligence is the ability of a machine to humans' cognitive functions such as perceiving, reasoning, problem solving, and decision making. Artificial intelligence-based applications and devices are used quite frequently in daily life. Artificial intelligence, which is a multidisciplinary field, has many classifications. As a result of using artificial intelligence types, which are seen as the focal point in the transformation of digital medicine, in the field of health, important developments have been experienced in the diagnosis, treatment, follow-up and care stages of diseases. Artificial intelligence technology, which isfrequently used in the field of women's health and in the perinatal period, has been used in the screening and management of diseases during pregnancy, remote pregnancy follow-up, pregnancy and pharmacology, fetal development, electronic monitoring and genetic screening and postpartum period, and positive results have been obtained. Artificial intelligence technology has positive aspects as well as negative aspects and ethical dilemmas. Healh professionals, who take an active role in the diagnosis, treatment and care stages of patients, are not yet at the desired level in the use of artificial intelligence technology. In this review, the use of artificial intelligence technology in women's health and obstetrics, its positive and negative aspects, its ethical dimension and the role of health professionals are focused and it is aimed to raise awareness in this emerging field.

Keywords

• perinatal
• artificial intelligence
• technology
• nursing

Perinatal Dönemde Yapay Zekâ Teknolojisinin Kullanımı

Abstract

Yapay zekâ bir makinenin insanların algılama, mantık yürütme, problem çözme ve karar verme gibi bilişsel işlevlerini taklit etme yeteneğidir. Yapay zekâ temelli uygulamalar ve cihazlar gündelik hayatta oldukça sık kullanılmaktadır. Multidisipliner bir alan olan yapay zekânın birçok sınıflaması vardır. Dijital tıbbın dönüşümünde odak nokta olarak görülen yapay zekâ çeşitlerinin sağlık alanında kullanılması ile hastalıkların tanı, tedavi, takip ve bakım aşamalarında önemli gelişmeler yaşanmıştır. Kadın sağlığı alanında ve perinatal dönemde oldukça sık kullanılan yapay zekâ teknolojisi, gebelikte hastalıkların taraması ve yönetimi, uzaktan gebelik takibi, gebelik ve farmakoloji, fetüs gelişimi, elektronik izleme, genetik tarama ve postpartum dönemde kullanılmış ve olumlu sonuçlar alınmıştır. Yapay zekâ teknolojisinin olumlu yönleri olduğu gibi bazı olumsuz yönleri ve etik ikilemleri de mevcuttur. Perinatal dönemdeki hastaların tanı, tedavi ve bakım aşamalarında aktif rol alan sağlık profesyonelleri, yapay zekâ teknolojisinin kullanımı konusunda henüz istenilen seviyede değildir. Bu derlemede, yapay zekâ teknolojisinin kadın sağlığı ve obstetride kullanımı, olumlu ve olumsuz yönleri, etik boyutu ve sağlık profesyonellerinin rolüne odaklanılmış ve yeni gelişen bu alanda farkındalık oluşturulmak amaçlanmıştır.

Keywords

• perinatal
• yapay zeka
• teknoloji
• hemşirelik

References

1. 1. Adar, T., Kılıç Delice, E. (2019). A literatüre review on the use of machine learning algorithms in health. UEMK 2019 Proceedings Book, 24-25 October 2019, Gaziantep University, Turkey.
2. 2. Akalın, B., Veranyurt, Ü. (2020). Sağlıkta dijitalleşme ve yapay zekâ. SDÜ Sağlık Yönetimi Dergisi. 2(2), 131-141.
3. 3. Akazawa, M., Hashimato, K. (2020). Artificial intelligence in ovarian cancer diagnosis. Anticancer Research. 40(8), 4795-4800. doi: https://doi.org/10.21873/anticanres.14482.
4. 4. Andersson, S., Bathula, D.R., Iliadis, S.I., Walter, M., Skalkidou, A. (2021). Predicting women with depressive symptoms postpartum with machine learning methods. Scientific Reports. 11, 7877. https://doi.org/10.1038/s41598-021-86368-y.
5. 5. Betts, K.A., Kisely, S., Alati, R. (2019). Predicting common Maternal postpartum complications: leveraging health administrative data and machine learning. BJOG: An International Journal of Obstetrics & Gynaecology. 126(6), 702-709. doi: 10.1111/1471-0528.15607.
6. 6. Boland, M.R., Polubriagniof, F., Tatonetti, N.P. (2017). Development of a machine learning algorithm to classify drugs of unknown fetal effect. Scientific Reports. 7, 12839. https://doi.org/10.1038/s41598-017-12943-x .
7. 7. Caballero-Ruiz, E., Garcia-Saez, G., Rigla, M., Villaplana, M., Pons, B., Hernando, M.E. (2017). A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs. International Journal of Medical Informatics. 102, 35-49. https://doi.org/10.1016/j.ijmedinf.2017.02.014 .
8. 8. Catley, C., Frize, M., Walker, C.R., Petriu, D.C. (2006). Predicting high-risk preterm birth using artificial neural networks. IEEE Transactions on information technology in biomedicine. 10(3), 540-549.

9. 9. Chavez-Badiola, A., Farias, A.F.S., Mendizabal-Ruiz, G., Garcia-Sanchez, R., Drakeley, A.J., Garcia-Sandoval, J.P. (2020). Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning. Scientific Reports. 10, 4394. https://doi.org/10.1038/s41598-020-61357-9.
10. 10. Çetin, B., Eroğlu, N. (2020). Hemşirelik bakımında teknolojinin yeri ve inovasyon. Acta Medica Nicomedia. 3(3), 120-126.
11. 11. Davidson, L., Boland, M.R. (2020). Enabling pregnant women and their physicians to make informed medication decision using artificial intellgence. Journal of Pharmacokinetics and Pharmacodynamics. 47, 305-318. https://doi.org/10.1007/s10928-020-09685-1 .
12. 12. Davidson, L., Boland, M.R. (2021). Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes. Briefings in Bioinformatics, 22(5), 1-29. https://doi.org/10.1093/bib/bbaa369 .
13. 13. Delanerolle, G., Yang, X., Shetty, S., Raymont, V., Shetty, A., Phiri, P., et al. (2021). Artificial intelligence: a rapid case for advancement in the personalization of gynaecology/obstetric and mental health care. Women’s Health. 17, 1-20. https://doi.org/10.1177/17455065211018111 .
14. 14. Demirhan, A., Kılıç, Y.A., Güler, İ. (2010). Tıpta yapay zekâ uygulamaları. Yoğun Bakım Dergisi. 9(1), 31-41.
15. 15. Dilbaz, B., Kaplanoğlu, M., Kaya Kaplanoğlu, D. (2020). Teletıp ve telesağlık: geçmiş, bugün ve gelecek. Eurasian Journal of Health Tecnology Assessment: EHTA. 4(1), 40-56.
16. 16. Ekrem, Ö., Salman, O.K.M., Aksoy, B., İnan, S.A. (2020). Yapay zekâ yöntemleri kullanılarak kalp hastalığının tespiti. Mühendislik Bilimleri ve Tasarım Dergisi. 8(5), 241-254. doi: 10.21923/jesd.824703. 17. Emin, E.I., Emin, E., Papalois, A., Willmott, F., Clarke, S., Sideris, M. (2019). Artificial intelligence in obstetrics and gynaecology: ıs this the way forward? In vivo. 33, 1547-1551. doi: 10.21873/invivo.11635. 18. Gümüş, E., Uysal Kasap, E. (2021). Hemşirelik mesleğinin geleceği: robot hemşireler. Journal of Artificial Intelligence in Health Sciences. 1(2), 20-25. doi: 10.52309/ja.2021.10.
17. 19. Iftikhar, P., Kuijpers, M.V., Khayyat, A., Iftikhar, A., Sa, M.D.D. (2020). Artificial intelligence: A new paradigm in obstetrics and Gynecology Research and clinical practice. Cureus. 12(2), e7124. doi:10.7759/cureus.71224.
18. 20. Jeong, G.H. (2020). Artificial intelligence, machine learning, and deep learning in women’s health nursing. Korean Journal of Women Health Nursing. 26(1), 5-9. doi: https://doi.org/10.4069/kjwhn.2020.03.11.
19. 21. Kayhan Tetik, B., Çolak, C. (2019). Myoma uteri ile ilişkili faktörlerin yapay sinir ağı modeli ile tahmini. 4. Uluslararası Sağlık Bilimleri ve Aile Hekimliği Kongresi, 07-09 Şubat 2019, 330-333.
20. 22. Kazantev, A., Ponomareva, J., Kazantev, P., Digilov, R., Huang, P. (2012). Development of e-health network for in-home pregnancy surveillance based on artificial intelligence. In IEEE-EMBS International Conference on Biomedical and Health Informatics, Hong Kong and Shenzhen, China, 2-7 January 2012.
21. 23. Keskinbora, K.H. (2019). Medical ethics considerations on artificial intelligence. Journal of Clinical Neuroscience. 64, 277-282. https://doi.org/10.1016/j.jocn.2019.03.001 .
22. 24. Manna, C., Nanni, L., Lumini, A., Pappalardo, S. (2013). Artificial intelligence techniques for embryo and oocyte classification. Reproductive BioMedicine Online. 26, 42-49. https://doi.org/10.1016/j.rbmo.2012.09.015 .
23. 25. Matheny, M.E., Whicher, D., Israni, S.T. (2020). Artifical intelligence in health care. The Journal of American Medical Association. 323(6), 509-510. doi:10.1001/jama.2019.21579.
24. 26. Maylawati, D.S., Ramdhani, M.A., Zulfikar, W.B., Taufik, I., Darmalaksana, W. (2017). Expert System for Predicting the Early Pregnancy with Disorders using Artificial Neural Network. 2017 5th International Conference on Cyber and IT Service Management (CITSM). doi: 10.1109/CITSM.2017.8089243.
25. 27. Menendez, A.L., Juez, F.J.C., Lasheras, F.S., Riesgo, J.A.A. (2010). Artificial neural networks applied to cancer detection in an breast screening programme. Mathematical and Computer Modelling. 52, 983-991. https://doi.org/10.1016/j.mcm.2010.03.019 .
26. 28. Moreira, M. W. L., Rodrigues, J. J. P. C., Oliveira, A. M. B., Saleem, K., Neto, A. V. (2016). "An inference mechanism using bayes-based classifiers in pregnancy care," 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom). doi: 10.1109/HealthCom.2016.7749475.
27. 29. Moreira, M.W.L., Rodigues, J.P.C., Al-Muhtadi, J., Korotaev, V.V., Albuquerque, V.H.C. (2018). Neuro-fuzzy model for HELLP syndrome prediction in mobile cloud computing environments. Concurrency and Computation: Practice and Experience. 33(7), e4651. https://doi.org/10.1002/cpe.4651 .
28. 30. Moreira, M.W.L., Rodrigues, J.J.P.C., Kumar, N., Al-Muhtadi, J., Korotaev, V. (2018). Evolutionary radial basis function network for gestational diabetes data analytics. Journal of Computer Science. 27, 410-417. https://doi.org/10.1016/j.jocs.2017.07.015 .
29. 31. Mysona, D.P., Kapp, D.S., Rohatgi, A., Lee, D., Mann, A.K., Tran, P., et al. (2021). Applying artificial intelligence to gynecologic oncology: a review. Obstetrical and Gynecological Survey. 76(5), 292-301. doi: 10.1097/OGX.0000000000000902.
30. 32. Paydar, K., Niakan Kalhori, S.R., Akbarian, M., Sheikhtaheri, A. (2017). A clinical decision support system for prediction of pregnancy outcome in pregnant women with systemic lupus erythematosus. International Journal of Medical Informatics. 97, 239–246. https://doi.org/10.1016/j.ijmedinf.2016.10.018 .
31. 33. Peleg, M., Shahar, Y., Quaglini, S., Broens, T., Budasu, R., Fung, N., ve ark. (2017). Assessment of a personalized and distributed patient guidance system. International Journal of Medical Informatics. 101, 108-130. https://doi.org/10.1016/j.ijmedinf.2017.02.010 .
32. 34. Rodriguez-Ruiz, A., Krupinski, E., Mordang, J.J., Schilling, K., Heywang-Köbrunner, S.H., Sechopoulos, I., vd., (2019). Detection of breast cancer with mammography: Effect of an artificial intelligence support system. Radiology. 290(2), 1-10. https://doi.org/10.1148/radiol.201818137.
33. 35. Sarı, O. (2020). Yapay zekanın sebep olduğu zararlardan doğan sorumluluk. Türkiye Barolar Birliği Dergisi. 147, 251-213.
34. 36. Schwalbe, N., Wahl, B. (2020). Artificial intelligence and the future of global health. Lancet. 395, 1579-1589. doi: https://doi.org/10.1016/S0140-6736(20)30226-9 .
35. 37. Shang Z. A (2021). Concept Analysis on the Use of Artificial Intelligence in Nursing Cureus. 13(5), e14857. doi:10.7759/cureus.14857.
36. 38. Shtar, G., Rokach, L., Shapira, B., Kohn, E., Berkovitch, M., Berlin, M. (2020). Treating COVID-19 during pregnancy: using artificial intelligence to evaluate medication safety. Reproductive Toxicology. 97, 3-4.
37. 39. Sucu, İ. (2019). Yapay zekanın toplum üzerindeki etkisi ve yapay zekâ (A.I) filmi bağlamında yapay zekaya bakış. Uluslararası Ders Kitapları ve Eğitim Materyalleri Dergisi. 2(2), 203-215.
38. 40. Şendir, M., Şimşekoğlu, N., Kaya, A., Sümer, K. (2019). Geleceğin teknolojisinde hemşirelik. Sağlık Bilimleri Üniversitesi Hemşirelik Dergisi. 1(3), 209-214.
39. 41. Tejera, E., Joseareias, M., Rodrigues, A., Ramõa, A., Manuelnieto-Villar, J., Rebelo, I. (2011). Artificial neural network for normal, hypertensive, and preeclamptic pregnancy classification using maternal heart rate variability indexes. The Journal of Maternal-Fetal & Neonatal Medicine. 24(9), 1147–1151. https://doi.org/10.3109/14767058.2010.545916.
40. 42. Topakkaya, A., Eyibaş, Y. (2019). Yapay zekâ ve etik ilişkisi. Felsefe Dünyası Dergisi. 70, 81-89.
41. 43. Topol, E.J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine. 25, 44-56.
42. 44. Uzun, T. (2020). Yapay zekâ ve sağlık uygulamaları. İzmir Kâtip Çelebi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 3(1), 80-92.
43. 45. Wahl, B., Cossy-Gantner, A., Germann, S., Schwalbe, N.R. (2018). Artificial intelligence (AI) and global health: how can AI contribute to health in resourcepoor settings? BMJ Global Health. 3, e000798. doi:10.1136/ bmjgh-2018-000798.
44. 46. Wang, S., Pathak, J., Zhang, Y. (2019). Using electronic health records and machine learning to predict postpartum depression. MEDINFO 2019; Health and Wellbeing e-Networks for all. 264, 888-892. doi:10.3233/SHTI190351.
45. 47. Warrick, P.A., Hamilton, E.F., Precup, D., Kearney, R.E. (2010). Classification of normal and hypoxic fetuses from systems modeling of intrapartum cardiotocography. IEEE Transactions on Bio-medical Engineering. 57(4), 771-779. doi: 10.1109/TBME.2009.2035818. PMID: 20659819.
46. 48. Zare, S., Thomsen, M.R., Nanyga, R.M., Goudie, A. (2021). Use of machine learning to determine the information value of a BMI screening program. American Journal of Preventive Medicine. 60(3), 425-433. https://doi.org/10.1016/j.amepre.2020.10.015 .