چهارشنبه 13 خرداد 1405
[Archive]
دوره 24، شماره 4 - ( 2-1405 )
جلد 24 شماره 4 صفحات 286-275 |
برگشت به فهرست نسخه ها
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Ghadirkhomi E, Fatehifar A. Artificial intelligence in infertility treatment: Applications, challenges, and future directions: A narrative review. IJRM 2026; 24 (4) :275-286
URL: http://ijrm.ir/article-1-3655-fa.html
URL: http://ijrm.ir/article-1-3655-fa.html
هوش مصنوعی و درمان ناباروری: کاربردها، چالش ها و مسیرهای پیش رو: یک مقاله مروری. International Journal of Reproductive BioMedicine. 1405; 24 (4) :275-286
چکیده: (25 مشاهده)
کاربرد هوش مصنوعی (AI) در تمامی حوزههای پزشکی، از جمله درمان ناباروری، در حال گسترش است. این مقاله به بررسی کاربردهای مختلف هوش مصنوعی در مراقبتهای بهداشتی تولیدمثل میپردازد. تمرکز اصلی این مقاله بر مدلهای پیشبینی، پردازش تصاویر و استراتژیهای درمانی شخصیسازیشده است. نرمافزارهای مبتنی بر هوش مصنوعی بهطور فزایندهای برای تشخیص بیماری و پیشآگاهی بیماران بهکار گرفته میشوند و از دادههای حجیم حاصل از اطلاعات بیماران و نتایج بالینی استفاده میکنند. بنابراین، قابلیت اطمینان پیشبینی موفقیت لانهگزینی جنین در فرآیندهای لقاح مصنوعی بهطور قابل توجهی افزایش مییابد. علاوه بر این، هوش مصنوعی با استفاده از سیستمهای پردازش تصویر کامپیوتری و تکنیکهای تزریق اسپرم انتخابشده بهصورت مورفولوژیکی داخل سیتوپلاسم (IMSI)، در انتخاب جنین و ارزیابی کیفیت اسپرم تحولی ایجاد کرده و دقت و سازگاری را نسبت به روشهای سنتی وابسته به مهارت جنینشناس بهبود میبخشد. این مقاله نشان میدهد که هوش مصنوعی عامل اصلی در تدوین برنامههای درمانی ویژه هر بیمار، کاهش ریسک و افزایش نرخ موفقیت بالینی است. با وجود پتانسیل عظیم، پیادهسازی هوش مصنوعی در درمان ناباروری با چالشهای واقعی مواجه است که مسائل اخلاقی، حفظ حریم خصوصی، کیفیت و تنوع دادهها از مهمترین آنها هستند. این مقاله مطالعات فعلی را در زمینه موفقیتها و شکستهای واقعی و بالقوه استراتژیهای مبتنی بر هوش مصنوعی مرور کرده و مسیرهای پژوهشی آینده در درمان ناباروری را بررسی میکند.
نوع مطالعه: Review article |
فهرست منابع
1. Ibitoye BO, Akadiri O, Ibitoye FO, Fasasi OA. WHO fact sheet on infertility (definition of infertility), published in Global Reproductive Health (2021). Glob Reprod Health 2023; 8: e60. [DOI:10.1097/GRH.0000000000000060]
2. Ghadirkhomi E, Angaji SA, Khosravi M, Mashayekhi MR. Association of novel single nucleotide polymorphisms of genes involved in cell functions with male infertility: A study of male cases in Northwest Iran. J Reprod Infertil 2021; 22: 258-266. [DOI:10.18502/jri.v22i4.7651] [PMID] [PMCID]
3. Ghadirkhomi E, Angaji SA, Khosravi M, Mashayekhi MR. Association study of novel single nucleotide polymorphisms of androgen receptor and estrogen receptor-α genes with male infertility in Northwest of Iran: A case-control study. Int J Reprod BioMed 2022; 20: 501-512. [DOI:10.18502/ijrm.v20i6.11446] [PMID] [PMCID]
4. The Eshre Guideline Group On Ovarian Stimulation, Bosch E, Broer S, Griesinger G, Grynberg M, Humaidan P, et al. ESHRE guideline: Ovarian stimulation for IVF/ICSI. Hum Reprod Open 2020; 2020: hoaa009. [DOI:10.1093/hropen/hoaa067] [PMID] [PMCID]
5. Allan S, Balaban B, Banker M, Buster J, Horton M, Miller K, et al. International Federation of Fertility Societies' Surveillance (IFFS) 2019: Global trends in reproductive policy and practice. Glob Reprod Health 2019; 4: e12. [DOI:10.1097/GRH.0000000000000029]
6. Medenica S, Zivanovic D, Batkoska L, Marinelli S, Basile G, Perino A, et al. The future is coming: Artificial intelligence in the treatment of infertility could improve assisted reproduction outcomes- the value of regulatory frameworks. Diagnostics 2022; 12: 2979. [DOI:10.3390/diagnostics12122979] [PMID] [PMCID]
7. Abdullah KAL, Atazhanova T, Chavez-Badiola A, Shivhare SB. Automation in ART: Paving the way for the future of infertility treatment. Reprod Sci 2023; 30: 1006-1016. [DOI:10.1007/s43032-022-00941-y] [PMID] [PMCID]
8. Ghadirkhomi E, Angaji SA, Beikzadeh B, Mashayekhi MR, Ghadirkhomi A. Correlation of single nucleotide polymorphisms of PRM1, PRM2, PYGO2, and DAZL genes with male infertility in northwest of Iran. Turk J Urol 2022; 48: 315-321. [DOI:10.5152/tud.2022.22032] [PMID] [PMCID]
9. Ghadirkhomi E, Angaji SA, Khosravi M, Mashayekhi MR. Correlation of novel single nucleotide polymorphisms of USP26, TEX15 and TNP2 genes with male infertility in northwest of Iran. Int J Fertil Steril 2022; 16: 10-16.
10. Esteves SC. Evolution of the World Health Organization semen analysis manual: Where are we? Nat Rev Urol 2022; 19: 439-446. [DOI:10.1038/s41585-022-00593-2] [PMID]
11. Abou Ghayda R, Cannarella R, Calogero AE, Shah R, Rambhatla A, Zohdy W, et al. Artificial intelligence in andrology: From semen analysis to image diagnostics. World J Mens Health 2024; 42: 39-61. [DOI:10.5534/wjmh.230050] [PMID] [PMCID]
12. Onofre J, Geenen L, Cox A, Van Der Auwera I, Willendrup F, Andersen E, et al. Simplified sperm testing devices: A possible tool to overcome lack of accessibility and inconsistency in male factor infertility diagnosis. An opportunity for low- and middle- income countries. Facts Views Vis Obgyn 2021; 13: 79-93. [DOI:10.52054/FVVO.13.1.011] [PMID] [PMCID]
13. Gonzalez D, Narasimman M, Best JC, Ory J, Ramasamy R. Clinical update on home testing for male fertility. World J Mens Health 2021; 39: 615-625. [DOI:10.5534/wjmh.200130] [PMID] [PMCID]
14. Valencia J, Alzate EA, Gómez G, Yeste M, Henao FJ. Semen analysis of boars under intertropical conditions reveals the relevance of proximal and distal cytoplasm droplets for sperm functional integrity. Reprod Domest Anim 2021; 56: 905-914. [DOI:10.1111/rda.13933] [PMID]
15. Kanakasabapathy MK, Sadasivam M, Singh A, Preston C, Thirumalaraju P, Venkataraman M, et al. An automated smartphone-based diagnostic assay for point-of-care semen analysis. Sci Transl Med 2017; 9: eaai7863. [DOI:10.1126/scitranslmed.aai7863] [PMID] [PMCID]
16. Douglas C, Parekh N, Kahn LG, Henkel R, Agarwal A. A novel approach to improving the reliability of manual semen analysis: A paradigm shift in the workup of infertile men. World J Mens Health 2021; 39: 172-185. [DOI:10.5534/wjmh.190088] [PMID] [PMCID]
17. Marzano G, Chiriacò MS, Primiceri E, Dell'Aquila ME, Ramalho-Santos J, Zara V, et al. Sperm selection in assisted reproduction: A review of established methods and cutting-edge possibilities. Biotechnol Adv 2020; 40: 107498. [DOI:10.1016/j.biotechadv.2019.107498] [PMID]
18. Daneshmand S, Richter KS, Callies HL, Kokjohn S. Evaluation of oocyte fertilization and in vitro embryo development after sperm preparation using Zymōt® prior to ICSI and IVF. Fertil Steril 2022; 118: e151. [DOI:10.1016/j.fertnstert.2022.08.441]
19. Riegler MA, Stensen MH, Witczak O, Andersen JM, Hicks SA, Hammer HL, et al. Artificial intelligence in the fertility clinic: Status, pitfalls and possibilities. Hum Reprod 2021; 36: 2429-2442. [DOI:10.1093/humrep/deab168] [PMID]
20. Gupta M, Kumar R, Lu Z. Transforming Gender-Based Healthcare with AI and Machine Learning. 1st Ed. USA: CRC Press; 2024. [DOI:10.1201/9781003473435]
21. Ghadirkhomi E, Ghdirkhomi A, Angaji SA. Cytogenetic studies in primary amenorrhoea cases. J Hum Reprod Sci 2022; 15: 187-190. [DOI:10.4103/jhrs.jhrs_13_22] [PMID] [PMCID]
22. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med 2019; 25: 24-29. [DOI:10.1038/s41591-018-0316-z] [PMID]
23. Moral P, Mustafi D, Mustafi A, Sahana SK. CystNet: An AI driven model for PCOS detection using multilevel thresholding of ultrasound images. Sci Rep 2024; 14: 25012. [DOI:10.1038/s41598-024-75964-3] [PMID] [PMCID]
24. Hassan MM, Mirza T. Comparative analysis of machine learning algorithms in diagnosis of polycystic ovarian syndrome. Int J Comput Appl 2020; 975: 42-53. [DOI:10.5120/ijca2020920688]
25. Nguyen TL, Rossetti D, Ferrero S. Deep learning for MRI detection of endometriosis: A comparative study with radiologists. J Magn Reson Imaging 2022; 56: 987-995.
26. Loewke KE, Nutting VI, Cho JH, Barash OO, Weckstein LN. A machine learning approach for forecasting follicle growth and predicting the optimal day of trigger during ovarian stimulation. Fertil Steril 2021; 116: 101-102. [DOI:10.1016/j.fertnstert.2021.07.284]
27. Bansod AR, Jha RK, More A, Fadnavis RA. Artificial intelligence in reproductive medicine. J Datta Meghe Inst Med Sci Univ 2022; 17: 499-503. [DOI:10.4103/jdmimsu.jdmimsu_279_22]
28. Henarejos-Castillo I, Aleman A, Martinez-Montoro B, Gracia-Aznárez FJ, Sebastian-Leon P, Romeu M, et al. Machine learning-based approach highlights the use of a genomic variant profile for precision medicine in ovarian failure. J Pers Med 2021; 11: 609. [DOI:10.3390/jpm11070609] [PMID] [PMCID]
29. Kirkegaard K, Ahlström A, Ingerslev HJ, Hardarson T. Choosing the best embryo by time lapse versus standard morphology. Fertil Steril 2015; 103: 323-332. [DOI:10.1016/j.fertnstert.2014.11.003] [PMID]
30. Kragh MF, Karstoft H, Kesmodel US. Artificial intelligence-based prediction of live birth from blastocyst images. Hum Reprod 2021; 36: 1125-1136.
31. Tran D, Cooke S, Illingworth PJ, Gardner DK. Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer. Hum Reprod 2019; 34: 1011-1018. [DOI:10.1093/humrep/dez064] [PMID] [PMCID]
32. VerMilyea M, Hall JMM, Diakiw SM, Johnston A, Nguyen T, Perugini D, et al. Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Hum Reprod 2020; 35: 770-784. [DOI:10.1093/humrep/deaa013] [PMID] [PMCID]
33. Ivan JN, Mamun SA, Ravishanker N, Persaud B, Lyon C, Srinivasan R, et al. Improved Prediction Models for Crash Types and Crash Severities. Washington, DC: The National Academies Press; 2021. [DOI:10.17226/26164]
34. Khosravi P, Kazemi E, Zhan Q, Malmsten JE, Toschi M, Zisimopoulos P, et al. Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digit Med 2019; 2: 21. [DOI:10.1038/s41746-019-0096-y] [PMID] [PMCID]
35. Kakkar P, Gupta S, Paschopoulou KI, Paschopoulos I, Paschopoulos I, Siafaka V, et al. The integration of artificial intelligence in assisted reproduction: A comprehensive review. Front Reprod Health 2025; 7: 1520919. [DOI:10.3389/frph.2025.1520919] [PMID] [PMCID]
36. Wang R, Pan W, Jin L, Li Y, Geng Y, Gao C, et al. Artificial intelligence in reproductive medicine. Reproduction 2019; 158: R139-R154. [DOI:10.1530/REP-18-0523] [PMID] [PMCID]
37. Humaidan P, Alviggi C, Fischer R, Esteves SC. The novel POSEIDON stratification of 'low prognosis patients in assisted reproductive technology' and its proposed marker of successful outcome. F1000Res 2016; 5: 2911. [DOI:10.12688/f1000research.10382.1] [PMID] [PMCID]
38. Reig A, Garcia-Velasco JA, Seli E. Bologna vs. POSEIDON criteria as predictors of the likelihood of obtaining at least one euploid embryo in poor ovarian response: An analysis of 6,889 cycles. Fertil Steril 2023; 120: 605-614. [DOI:10.1016/j.fertnstert.2023.05.007] [PMID]
39. Ueno S, Takehara Y, Inoue K, Yoshida A, Ishii T, Kitayama S, et al. Prediction of ovarian response using machine learning in patients undergoing controlled ovarian stimulation for IVF. Reprod Med Biol 2020; 19: 192-199.
40. Blank C, Wilde K, Dressler L, Ahrens LM, Schafer SM, Vogl TS, et al. Individualized prediction of gonadotropin dose for controlled ovarian stimulation using machine learning. Hum Reprod 2019; 34: 1825-1834.
41. Morin SJ, Patounakis G, Juneau CR, Neal SA, Scott RT, Seli E. Machine learning models for the prediction of live birth after IVF. Fertil Steril 2019; 112: e306.
42. Shore C, Helman A, Cavagnini K, Wizemann T. Toward a framework to improve diversity and inclusion in clinical trials: Proceedings of a workshop. Washington, DC: The National Academies Press; 2025.
43. McMahon C, Hammarberg K, Lensen S, Wang R, Mol BW, Vollenhoven BJN. What do women undergoing in vitro fertilization (IVF) understand about their chance of IVF success? Hum Reprod 2024; 39: 130-138. [DOI:10.1093/humrep/dead239] [PMID] [PMCID]
44. Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One 2017; 12: e0174944. [DOI:10.1371/journal.pone.0174944] [PMID] [PMCID]
45. Bulletti C, Franasiak JM, Busnelli A, Sciorio R, Berrettini M, Aghajanova L, et al. Artificial intelligence, clinical decision support algorithms, mathematical models, calculators applications in infertility: Systematic review and hands-on digital applications. Mayo Clin Proc Digit Health 2024; 2: 518-532. [DOI:10.1016/j.mcpdig.2024.08.007] [PMID] [PMCID]
46. Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med 2019; 25: 44-56. [DOI:10.1038/s41591-018-0300-7] [PMID]
47. Rashed HA, O'Brien L, Di Carlo C. Algorithmic bias in fertility AI: A call for global data harmonization. Hum Reprod Update 2021; 27: 987-992.
48. Rolfes V, Bittner U, Gerhards H, Krüssel JS, Fehm T, Ranisch R, et al. Artificial intelligence in reproductive medicine-an ethical perspective. Geburtshilfe Frauenheilkd 2023; 83: 106-115. [DOI:10.1055/a-1866-2792] [PMID] [PMCID]
49. Rosenwaks Z. Artificial intelligence in reproductive medicine: A fleeting concept or the wave of the future? Fertil Steril 2020; 114: 905-907. [DOI:10.1016/j.fertnstert.2020.10.002] [PMID]
| بازنشر اطلاعات | |
 |
این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است. |
