Enhancing IVF Outcomes with Artificial Intelligence: Current Advances and Future Possibilities
Abstract
The integration of Artificial Intelligence (AI) into In Vitro Fertilization (IVF) practices has marked a revolutionary shift in reproductive medicine, offering enhanced precision, efficiency, and personalized treatment plans. The rapid advancement of Artificial Intelligence (AI) has led to significant innovations in the field of reproductive medicine, particularly in In Vitro Fertilization (IVF). Traditional IVF procedures, while effective, often face challenges such as variable success rates, high costs, and the emotional burden on patients due to multiple treatment cycles. AI offers a promising solution to these issues by enhancing accuracy, personalization, and efficiency throughout the IVF process. AI algorithms have shown remarkable capabilities in diagnosing infertility by analyzing complex datasets from hormone profiles, genetic testing, and medical imaging, enabling early identification of conditions like polycystic ovary syndrome (PCOS) and endometriosis. Moreover, one of the most promising applications of AI in IVF is embryo grading. However, AI systems have been developed to objectively evaluate embryos based on time-lapse imaging, morphology, and other parameters, improving the selection process. Additionally, AI has been instrumental in optimizing ovarian stimulation protocols by analyzing patient data to determine the appropriate medication dosage, minimizing the risk of ovarian hyperstimulation syndrome (OHSS). This review discusses the current state of AI integration in fertility treatments, successful case studies, and ongoing research to develop more sophisticated AI models. Overall, AI holds immense promise in making IVF more accessible, affordable, and successful for patients worldwide, ushering in a new era of precision medicine in reproductive health.
Keywords:
Artificial Intelligence (AI), In Vitro Fertilization (IVF), Embryo Grading, Predictive Analytics, Robotic Systems
Downloads
Download data is not yet available.
References
1. Smith KP, Wang H, Durant TJ., et al. (2020) Applications of artificial intelligence in clinical microbiology diagnostic testing. Clinical Microbiology Newsletter. 42(8): 61-70.
2. La Marca A, Nelson SM, Sighinolfi G, et al. (2011) Anti-Müllerian hormone-based prediction model for a live birth in assisted reproduction. Reproductive biomedicine online. 22(4): 341-349.
3. Yi WJ, Park KS, & Paick JS. (1998) Morphological classification of sperm heads using artificial neural networks. In MEDINFO'98. 1071-1074.
4. Morales DA, Bengoetxea E, Larrañaga P, et al. (2008) Bayesian classification for the selection of in vitro human embryos using morphological and clinical data. Computer methods and programs in biomedicine. 90(2): 104-116.
5. Uyar A, Bener A, Ciray HN. (2015) Predictive modeling of implantation outcome in an in vitro fertilization setting: an application of machine learning methods. Medical Decision Making. 35(6): 714-725.
6. Paternot G, Devroe J, Debrock S, et al. (2009) Intra- and inter-observer analysis in the morphological assessment of early-stage embryos. Reprod Biol Endocrinol. 7: 105.
7. Rubessa M, Feugang JM, Kandel ME, et al. (2020) High-throughput sperm assay using label-free microscopy: Morphometric comparison between different sperm structures of boar and stallion spermatozoa. Anim. Reprod. Sci. 219: 106509.
8. Khosravi, P., Kazemi, E., Zhan, Q., Malmsten, J. E., Toschi, M., Zisimopoulos, P., et al. (2019). Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digit. Med. 2, 1–9. doi: 10.1038/s41746-019-0096-y
9. Kirkegaard, K., Ahlström, A., Ingerslev, H. J., and Hardarson, T. (2015). Choosing the best embryo by time lapse versus standard morphology. Fertil. Steril. 103, 323–332. doi: 10.1016/j.fertnstert.2014.11.003
10. Liao, Q., Zhang, Q., Feng, X., Huang, H., Xu, H., Tian, B., et al. (2021). Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring. Commun. Biol. 4, 1–9. doi: 10.1038/s42003-021-01937-1
11. Machtinger, R., and Racowsky, C. (2013). Morphological systems of human embryo assessment and clinical evidence. Reprod. Biomed. Online 26, 210–221. doi: 10.1016/j.rbmo.2012.10.021
12. Miyagi, Y., Habara, T., Hirata, R., and Hayashi, N. (2019). Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image. Reprod. Med. Biol. 18, 204–211. doi: 10.1002/rmb2.12267
13. Sawada, Y., Sato, T., Nagaya, M., Saito, C., Yoshihara, H., Banno, C., et al. (2021). Evaluation of artificial intelligence using time-lapse images of IVF embryos to predict live birth. Reprod. Biomed. Online 43, 843–852. doi: 10.1016/j.rbmo.2021.05.002
14. Sharma, A., Dorobantiu, A., Ali, S., Iliceto, M., Stensen, M. H., Delbarre, E., et al. (2024). Deep learning methods to forecasting human embryo development in time-lapse videos. bioRxiv. 54, 2024–2003. doi: 10.1101/2024.03.04.583269
15. Borna, M.-R., and Sepehri, M. M. (2024). Predicting IVF pregnancy outcome and analyzing its cost factors: an artificial intelligence approach. Nov. Biomed 12, 23–30. doi: 10.22037/nbm.v12i1.43214
16. Zhang, X. (2020). AI-driven Ovarian Stimulation. Artificial Intelligence in Medicine, 12(3), 412-418.
17. Chavez-Badiola, A., Flores-Saiffe Farias, A., Mendizabal-Ruiz, G., Garcia-Sanchez, R., Drakeley, A. J., and Garcia-Sandoval, J. P. (2020). Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning. Sci. Rep. 10, 1–6. doi: 10.1038/s41598-020-61357-9
18. Johnson, R., & Lee, M. (2021). Predictive Models for IVF Success. Fertility & Sterility, 115(3), 225-233.
19. Vogiatzi P, Pouliakis A, Siristatidis C. (2019) An artificial neural network for the prediction of assisted reproduction outcome. Journal of assisted reproduction and genetics. 36(7): 1441-1448.
20. Arora, N., et al. (2022). AI Diagnosis of PCOS. Endocrinology Today, 27(5), 445-452.
21. Gupta, P., & Singh, V. (2021). Deep Learning for Endometrial Receptivity. Reproductive Biology Insights, 14(4), 123-130.
22. Chen, T.-J., Zheng, W.-L., Liu, C.-H., Huang, I., Lai, H.-H., and Liu, M. (2019). Using deep learning with large dataset of microscope images to develop an automated embryo grading system. Fertil. Reprod. 1, 51–56. doi: 10.1142/S2661318219500051
23. Lee, K., et al. (2021). Follicular Development Prediction. IVF Technologies Journal, 18(7), 290-298.
24. Miller, D., & Johnson, R. (2021). Embryo Selection Using AI Imaging. Human Reproduction Update, 27(2), 110-117.
25. Cooper, J. & Lee, M. (2019). Machine Learning in Embryo Grading. Journal of Biomedical Engineering, 25(6), 377-384.
26. Fernandez, L., Bener A., et al. (2020). AI in Fertility Treatment Personalization. Computational Biology Journal, 35(4), 231-238.
27. Rossi, A., & Patel, R. (2021). Predictive Analytics in IVF. IVF Reports, 29(3), 170-179.
28. Arora, N., & Gupta, M. (2022). Role of AI in PCOS and Endometriosis Diagnosis. Journal of Women’s Health, 29(2), 102-110.
29. Kumar, S., et al. (2022). AI-based Sperm Selection. International Journal of Fertility, 42(1), 50-57.
30. Thompson, M. (2022). AI in Egg Quality Assessment. Journal of Reproductive Endocrinology, 31(1), 67-75.
31. Li, Y., & Hsu, C. (2023). AI for Early Diagnosis of Infertility. Journal of Reproductive Health, 40(2), 94-102.
32. Mehta, R., & Choudhary, S. (2020). Time-lapse Imaging Combined with AI. Journal of Embryology, 17(5), 305-312.
33. Wang, Y., et al. (2022). AI-based Oocyte Selection. Fertility Science & Technology, 19(7), 390-398.
34. Smith, J., Doe, A., & Kim, L. (2020). AI in Gamete Selection. Journal of Reproductive Medicine, 47(4), 312-320.
35. Kratz A, Bengtsson HI, Casey JE. (2005) Performance evaluation of the CellaVision DM96 system: WBC differentials by automated digital image analysis supported by an artificial neural network. American journal of clinical pathology. 124(5): 770-781.
36. Wang, T., et al. (2023). Machine Learning in IVF. Journal of Assisted Reproduction and Genetics, 30(2), 134-141.
37. Peterson, N. (2021). AI-enhanced Monitoring in IVF. Reproductive Technology Review, 16(4), 123-131.
38. Hirose, A. (2022). Integrating Robotics with AI in IVF. Robotics in Medicine, 14(2), 88-96.
39. Smith, L., Brown, A., & Wang, H. (2023). AI and Robotics in Assisted Reproduction. Journal of Clinical Reproduction, 37(8), 582-589.
40. Bizzaro N, Antico A, Platzgummer S. (2014) Automated antinuclear immunofluorescence antibody screening: a comparative study of six computer-aided diagnostic systems. Autoimmunity Reviews. 13(3): 292-298.
41. La Marca A, Nelson SM, Sighinolfi G. (2011) Anti-Müllerian hormone-based prediction model for a live birth in assisted reproduction. Reproductive biomedicine online. 22(4): 341-349.
42. Agarwal A, Said TM. (2003) Role of sperm chromatin abnormalities and DNA damage in male infertility. Human reproduction update. 9(4): 331-345.
43. Broekhuijse MLWJ, Šoštarić E, Feitsma H. (2011) Additional value of computer assisted semen analysis (CASA) compared to conventional motility assessments in pig artificial insemination. Theriogenology. 76(8): 1473-1486.
44. Verstegen J, Iguer-Ouada M, Onclin K. (2002) Computer assisted semen analyzers in andrology research and veterinary practice. Theriogenology. 57(1): 149-179.
45. Kime DE, Van Look KW, McAllister BG. (2001) Computer-assisted sperm analysis (CASA) as a tool for monitoring sperm quality in fish. Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacology. 130(4): 425-433.
46. Tırpan MB, Olğaç KT, Gürler H. (2017) Effects of Different Equilibration Conditions on Cryopreserved Bovine Sperm Quality. Kocatepe Veterinary Journal. 10(2): 57-62.
47. Kavak A, Johannisson A, Lundeheim N. (2003) Evaluation of cryopreserved stallion semen from Tori and Estonian breeds using CASA and flow cytometry. Animal Reproduction Science, 76(3-4): 205-216.
48. Cheuquemán C, Sánchez R, Risopatrón J. (2017) Effect of Sperm Selection Techniques in Frozen/Thawed Cat Spermatozoa on Sperm Motility Analyzed by CASA System. International Journal of Morphology. 35(4).
49. Neagu VR, García BM, Sandoval CS. (2010) Freezing dog semen in presence of the antioxidant butylated hydroxytoluene improves postthaw sperm membrane integrity. Theriogenology. 73(5): 645-650.
50. Özer Kaya Ş, Güngör İH, Dayan Cı̇nkara S. (2021) Effect of different doses of hydrated C60 fullerene nanoparticles on ram semen during cool storage. Turk J Vet Anim Sci. 45: 139-147.
51. Wilson-Leedy JG, Ingermann RL. (2007) Development of a novel CASA system based on open-source software for characterization of zebrafish sperm motility parameters. Theriogenology. 67(3): 661-672.
52. Moore HD, Akhondı MA. (1996) Fertilizing capacity of rat spermatozoa is correlated with decline in straight‐line velocity measured by Continuous Computer‐Aided Sperm Analysis: epididymal rat spermatozoa from the proximal cauda have a greater fertilizing capacity in vitro than those from the distal cauda or vas deferens. Journal of Andrology. 17(1): 50-60.
53. Martínez‐Pastor F, Mata‐Campuzano M, Álvarez‐Rodríguez M, et al. (2010) Probes and techniques for sperm evaluation by flow cytometry. Reproduction in domestic animals. 45: 67-78.
54. Coticchio G, Fiorentino G, Nicora G, Sciajno R, Cavalera F, Bellazzi R, Garagna S, Borini A, Zuccotti M.(2021) Cytoplasmic movements of the early human embryo: imaging and artificial intelligence to predict blastocyst development. Reproductive Biomedicine .42:521–528.
55. Fernandez EI, Ferreira AS, Cecilio MHM, Cheles DS, de Souza RCM, Nogueira MFG, Rocha JC. (2020) Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data. Journal of Assisted Reproduction and Genetics. 37:2359–2376.
56. Kandel ME, Rubessa M, He YR, Schreiber S, Meyers S, Matter Naves L, Sermersheim MK, Sell GS, Szewczyk MJ, Sobh N et al. (2020) Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure. PNAS 117: 18302–18309.
57. VerMilyea M, Hall JMM, Diakiw SM, Johnston A, Nguyen T, Perugini D, Miller A, Picou A, Murphy AP, Perugini M. (2020) Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Human Reproduction. 35: 770–784.
58. Vogiatzi P, Pouliakis A, Siristatidis C. (2019) An artificial neural network for the prediction of assisted reproduction outcome. Journal of Assisted Reproduction and Genetics. 36: 1441–1448.
59. Roberts, T., et al. (2021). Ethical Issues in AI-based Reproductive Care. Bioethics Today, 22(3), 222-230.
60. Brown, J., et al. (2023). Future Directions in AI and Reproductive Health. Advanced Medical Technologies, 41(3), 150-160.
61. Smith KP, Wang H, Durant TJ., et al. (2020) Applications of artificial intelligence in clinical microbiology diagnostic testing. Clinical Microbiology Newsletter. 42(8): 61-70.
62. Manna C, Nanni L, Lumini A, Pappalardo S. (2013) Artificial intelligence techniques for embryo and oocyte classification. Reproductive Biomedicine Online. 26:42–49.
2. La Marca A, Nelson SM, Sighinolfi G, et al. (2011) Anti-Müllerian hormone-based prediction model for a live birth in assisted reproduction. Reproductive biomedicine online. 22(4): 341-349.
3. Yi WJ, Park KS, & Paick JS. (1998) Morphological classification of sperm heads using artificial neural networks. In MEDINFO'98. 1071-1074.
4. Morales DA, Bengoetxea E, Larrañaga P, et al. (2008) Bayesian classification for the selection of in vitro human embryos using morphological and clinical data. Computer methods and programs in biomedicine. 90(2): 104-116.
5. Uyar A, Bener A, Ciray HN. (2015) Predictive modeling of implantation outcome in an in vitro fertilization setting: an application of machine learning methods. Medical Decision Making. 35(6): 714-725.
6. Paternot G, Devroe J, Debrock S, et al. (2009) Intra- and inter-observer analysis in the morphological assessment of early-stage embryos. Reprod Biol Endocrinol. 7: 105.
7. Rubessa M, Feugang JM, Kandel ME, et al. (2020) High-throughput sperm assay using label-free microscopy: Morphometric comparison between different sperm structures of boar and stallion spermatozoa. Anim. Reprod. Sci. 219: 106509.
8. Khosravi, P., Kazemi, E., Zhan, Q., Malmsten, J. E., Toschi, M., Zisimopoulos, P., et al. (2019). Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digit. Med. 2, 1–9. doi: 10.1038/s41746-019-0096-y
9. Kirkegaard, K., Ahlström, A., Ingerslev, H. J., and Hardarson, T. (2015). Choosing the best embryo by time lapse versus standard morphology. Fertil. Steril. 103, 323–332. doi: 10.1016/j.fertnstert.2014.11.003
10. Liao, Q., Zhang, Q., Feng, X., Huang, H., Xu, H., Tian, B., et al. (2021). Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring. Commun. Biol. 4, 1–9. doi: 10.1038/s42003-021-01937-1
11. Machtinger, R., and Racowsky, C. (2013). Morphological systems of human embryo assessment and clinical evidence. Reprod. Biomed. Online 26, 210–221. doi: 10.1016/j.rbmo.2012.10.021
12. Miyagi, Y., Habara, T., Hirata, R., and Hayashi, N. (2019). Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image. Reprod. Med. Biol. 18, 204–211. doi: 10.1002/rmb2.12267
13. Sawada, Y., Sato, T., Nagaya, M., Saito, C., Yoshihara, H., Banno, C., et al. (2021). Evaluation of artificial intelligence using time-lapse images of IVF embryos to predict live birth. Reprod. Biomed. Online 43, 843–852. doi: 10.1016/j.rbmo.2021.05.002
14. Sharma, A., Dorobantiu, A., Ali, S., Iliceto, M., Stensen, M. H., Delbarre, E., et al. (2024). Deep learning methods to forecasting human embryo development in time-lapse videos. bioRxiv. 54, 2024–2003. doi: 10.1101/2024.03.04.583269
15. Borna, M.-R., and Sepehri, M. M. (2024). Predicting IVF pregnancy outcome and analyzing its cost factors: an artificial intelligence approach. Nov. Biomed 12, 23–30. doi: 10.22037/nbm.v12i1.43214
16. Zhang, X. (2020). AI-driven Ovarian Stimulation. Artificial Intelligence in Medicine, 12(3), 412-418.
17. Chavez-Badiola, A., Flores-Saiffe Farias, A., Mendizabal-Ruiz, G., Garcia-Sanchez, R., Drakeley, A. J., and Garcia-Sandoval, J. P. (2020). Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning. Sci. Rep. 10, 1–6. doi: 10.1038/s41598-020-61357-9
18. Johnson, R., & Lee, M. (2021). Predictive Models for IVF Success. Fertility & Sterility, 115(3), 225-233.
19. Vogiatzi P, Pouliakis A, Siristatidis C. (2019) An artificial neural network for the prediction of assisted reproduction outcome. Journal of assisted reproduction and genetics. 36(7): 1441-1448.
20. Arora, N., et al. (2022). AI Diagnosis of PCOS. Endocrinology Today, 27(5), 445-452.
21. Gupta, P., & Singh, V. (2021). Deep Learning for Endometrial Receptivity. Reproductive Biology Insights, 14(4), 123-130.
22. Chen, T.-J., Zheng, W.-L., Liu, C.-H., Huang, I., Lai, H.-H., and Liu, M. (2019). Using deep learning with large dataset of microscope images to develop an automated embryo grading system. Fertil. Reprod. 1, 51–56. doi: 10.1142/S2661318219500051
23. Lee, K., et al. (2021). Follicular Development Prediction. IVF Technologies Journal, 18(7), 290-298.
24. Miller, D., & Johnson, R. (2021). Embryo Selection Using AI Imaging. Human Reproduction Update, 27(2), 110-117.
25. Cooper, J. & Lee, M. (2019). Machine Learning in Embryo Grading. Journal of Biomedical Engineering, 25(6), 377-384.
26. Fernandez, L., Bener A., et al. (2020). AI in Fertility Treatment Personalization. Computational Biology Journal, 35(4), 231-238.
27. Rossi, A., & Patel, R. (2021). Predictive Analytics in IVF. IVF Reports, 29(3), 170-179.
28. Arora, N., & Gupta, M. (2022). Role of AI in PCOS and Endometriosis Diagnosis. Journal of Women’s Health, 29(2), 102-110.
29. Kumar, S., et al. (2022). AI-based Sperm Selection. International Journal of Fertility, 42(1), 50-57.
30. Thompson, M. (2022). AI in Egg Quality Assessment. Journal of Reproductive Endocrinology, 31(1), 67-75.
31. Li, Y., & Hsu, C. (2023). AI for Early Diagnosis of Infertility. Journal of Reproductive Health, 40(2), 94-102.
32. Mehta, R., & Choudhary, S. (2020). Time-lapse Imaging Combined with AI. Journal of Embryology, 17(5), 305-312.
33. Wang, Y., et al. (2022). AI-based Oocyte Selection. Fertility Science & Technology, 19(7), 390-398.
34. Smith, J., Doe, A., & Kim, L. (2020). AI in Gamete Selection. Journal of Reproductive Medicine, 47(4), 312-320.
35. Kratz A, Bengtsson HI, Casey JE. (2005) Performance evaluation of the CellaVision DM96 system: WBC differentials by automated digital image analysis supported by an artificial neural network. American journal of clinical pathology. 124(5): 770-781.
36. Wang, T., et al. (2023). Machine Learning in IVF. Journal of Assisted Reproduction and Genetics, 30(2), 134-141.
37. Peterson, N. (2021). AI-enhanced Monitoring in IVF. Reproductive Technology Review, 16(4), 123-131.
38. Hirose, A. (2022). Integrating Robotics with AI in IVF. Robotics in Medicine, 14(2), 88-96.
39. Smith, L., Brown, A., & Wang, H. (2023). AI and Robotics in Assisted Reproduction. Journal of Clinical Reproduction, 37(8), 582-589.
40. Bizzaro N, Antico A, Platzgummer S. (2014) Automated antinuclear immunofluorescence antibody screening: a comparative study of six computer-aided diagnostic systems. Autoimmunity Reviews. 13(3): 292-298.
41. La Marca A, Nelson SM, Sighinolfi G. (2011) Anti-Müllerian hormone-based prediction model for a live birth in assisted reproduction. Reproductive biomedicine online. 22(4): 341-349.
42. Agarwal A, Said TM. (2003) Role of sperm chromatin abnormalities and DNA damage in male infertility. Human reproduction update. 9(4): 331-345.
43. Broekhuijse MLWJ, Šoštarić E, Feitsma H. (2011) Additional value of computer assisted semen analysis (CASA) compared to conventional motility assessments in pig artificial insemination. Theriogenology. 76(8): 1473-1486.
44. Verstegen J, Iguer-Ouada M, Onclin K. (2002) Computer assisted semen analyzers in andrology research and veterinary practice. Theriogenology. 57(1): 149-179.
45. Kime DE, Van Look KW, McAllister BG. (2001) Computer-assisted sperm analysis (CASA) as a tool for monitoring sperm quality in fish. Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacology. 130(4): 425-433.
46. Tırpan MB, Olğaç KT, Gürler H. (2017) Effects of Different Equilibration Conditions on Cryopreserved Bovine Sperm Quality. Kocatepe Veterinary Journal. 10(2): 57-62.
47. Kavak A, Johannisson A, Lundeheim N. (2003) Evaluation of cryopreserved stallion semen from Tori and Estonian breeds using CASA and flow cytometry. Animal Reproduction Science, 76(3-4): 205-216.
48. Cheuquemán C, Sánchez R, Risopatrón J. (2017) Effect of Sperm Selection Techniques in Frozen/Thawed Cat Spermatozoa on Sperm Motility Analyzed by CASA System. International Journal of Morphology. 35(4).
49. Neagu VR, García BM, Sandoval CS. (2010) Freezing dog semen in presence of the antioxidant butylated hydroxytoluene improves postthaw sperm membrane integrity. Theriogenology. 73(5): 645-650.
50. Özer Kaya Ş, Güngör İH, Dayan Cı̇nkara S. (2021) Effect of different doses of hydrated C60 fullerene nanoparticles on ram semen during cool storage. Turk J Vet Anim Sci. 45: 139-147.
51. Wilson-Leedy JG, Ingermann RL. (2007) Development of a novel CASA system based on open-source software for characterization of zebrafish sperm motility parameters. Theriogenology. 67(3): 661-672.
52. Moore HD, Akhondı MA. (1996) Fertilizing capacity of rat spermatozoa is correlated with decline in straight‐line velocity measured by Continuous Computer‐Aided Sperm Analysis: epididymal rat spermatozoa from the proximal cauda have a greater fertilizing capacity in vitro than those from the distal cauda or vas deferens. Journal of Andrology. 17(1): 50-60.
53. Martínez‐Pastor F, Mata‐Campuzano M, Álvarez‐Rodríguez M, et al. (2010) Probes and techniques for sperm evaluation by flow cytometry. Reproduction in domestic animals. 45: 67-78.
54. Coticchio G, Fiorentino G, Nicora G, Sciajno R, Cavalera F, Bellazzi R, Garagna S, Borini A, Zuccotti M.(2021) Cytoplasmic movements of the early human embryo: imaging and artificial intelligence to predict blastocyst development. Reproductive Biomedicine .42:521–528.
55. Fernandez EI, Ferreira AS, Cecilio MHM, Cheles DS, de Souza RCM, Nogueira MFG, Rocha JC. (2020) Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data. Journal of Assisted Reproduction and Genetics. 37:2359–2376.
56. Kandel ME, Rubessa M, He YR, Schreiber S, Meyers S, Matter Naves L, Sermersheim MK, Sell GS, Szewczyk MJ, Sobh N et al. (2020) Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure. PNAS 117: 18302–18309.
57. VerMilyea M, Hall JMM, Diakiw SM, Johnston A, Nguyen T, Perugini D, Miller A, Picou A, Murphy AP, Perugini M. (2020) Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Human Reproduction. 35: 770–784.
58. Vogiatzi P, Pouliakis A, Siristatidis C. (2019) An artificial neural network for the prediction of assisted reproduction outcome. Journal of Assisted Reproduction and Genetics. 36: 1441–1448.
59. Roberts, T., et al. (2021). Ethical Issues in AI-based Reproductive Care. Bioethics Today, 22(3), 222-230.
60. Brown, J., et al. (2023). Future Directions in AI and Reproductive Health. Advanced Medical Technologies, 41(3), 150-160.
61. Smith KP, Wang H, Durant TJ., et al. (2020) Applications of artificial intelligence in clinical microbiology diagnostic testing. Clinical Microbiology Newsletter. 42(8): 61-70.
62. Manna C, Nanni L, Lumini A, Pappalardo S. (2013) Artificial intelligence techniques for embryo and oocyte classification. Reproductive Biomedicine Online. 26:42–49.
Published
31-10-2024
Statistics
54 Views | 33 Downloads
Citatons
How to Cite
1.
N Y, R P, S RA. Enhancing IVF Outcomes with Artificial Intelligence: Current Advances and Future Possibilities. World Journal of Current Med and Pharm Research [Internet]. 2024Oct.31 [cited 2024Dec.6];6(3):1-. Available from: https://wjcmpr.com/index.php/journal/article/view/340
Issue
Section
Review Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright © Author(s) retain the copyright of this article.
