“RECENT TRENDS AND NOVEL MODALITIES IN THE FIELD OF ART"
Main Article Content
Keywords
Assisted Reproductive Technology, Artificial Intelligence, Cryopreservation, Preimplantation Genetic Testing, Embryo Quality, Clinical Pregnancy
Abstract
Background: Assisted reproductive technology (ART) has come a long way by considering the incorporation of new modalities like artificial intelligence (AI), time-lapse imaging, and cryopreservation.
Objective: The proposed study aimed to assess the latest developments and new forms of ART in the The Homerton Fertility Center, London, UK, in terms of their clinical practice and outcomes.
Materials and Methods: A prospective observational study was performed between from 1st June, 2018 to 1st September, 2018. Two hundred couples who have undergone ART techniques, such as in vitro fertilization (IVF), intracytoplasmic sperm injection (ICSI), cryopreservation, and preimplantation genetic testing (PGT), were included. Descriptive statistics were used to analyze data on fertilization, embryo quality, and clinical outcomes.
Results: The average age of females was 32.5 years, and the average infertility period was 5.2 years. 68% primary infertility and 32% secondary infertility. The most common procedure was ICSI (45%), then IVF (40%), cryopreservation (10%), and PGT (5%). The fertilization rate was 72%, the high-quality embryos rate was 65%, the clinical pregnancy rate was 48%, and the implantation rate was 35%. The use of AI in embryo grading and vitrification has significantly enhanced results in comparison with traditional methods.
Conclusion: The ART outcome has been enhanced by the introduction of AI and better cryopreservation using time-lapse imaging, as the local practice has not been left behind in adapting to the global trends. Accessibility and investment should also be increased to make ART benefits more inclusive of a greater number of people.
References
2. Sadeghi, A. H., El Mathari, S., Abjigitova, D., Maat, A. P., Taverne, Y. J., Bogers, A. J., & Mahtab, E. A. (2022). Current and future applications of virtual, augmented, and mixed reality in cardiothoracic surgery. The Annals of Thoracic Surgery, 113(2), 681-691.
3. Papaioannou, G., Volakaki, M. G., Kokolakis, S., & Vouyioukas, D. (2023). Learning spaces in higher education: a state-of-the-art review. Trends in Higher Education, 2(3), 526-545.
4. Alum, E. U., Uti, D. E., Ugwu, O. P. C., & Alum, B. N. (2024). Toward a cure – Advancing HIV/AIDs treatment modalities beyond antiretroviral therapy: A Review. Medicine, 103(27), e38768.
5. Miao, D., Zhao, J., Han, Y., Zhou, J., Li, X., Zhang, T., … & Xia, Y. (2024). Management of locally advanced non-small cell lung cancer: state of the art and future directions. Cancer Communications, 44(1), 23-46.
6. Chun, K. R. J., Miklavčič, D., Vlachos, K., Bordignon, S., Scherr, D., Jais, P., & Schmidt, B. (2024). State-of-the-art pulsed field ablation for cardiac arrhythmias: ongoing evolution and future perspective. Europace, 26(6), euae134.
7. Hou, Y., Kenderdine, S., Picca, D., Egloff, M., & Adamou, A. (2022). Digitizing intangible cultural heritage embodied: State of the art. Journal on Computing and Cultural Heritage (JOCCH), 15(3), 1-20.
8. Khera, R., Oikonomou, E. K., Nadkarni, G. N., Morley, J. R., Wiens, J., Butte, A. J., & Topol, E. J. (2024). Transforming cardiovascular care with artificial intelligence: from discovery to practice: JACC state-of-the-art review. Journal of the American College of Cardiology, 84(1), 97-114.
9. Bian, S., Liu, M., Zhou, B., & Lukowicz, P. (2022). The state-of-the-art sensing techniques in human activity recognition: A survey. Sensors, 22(12), 4596.
10. Momtazmanesh, S., Nowroozi, A., & Rezaei, N. (2022). Artificial intelligence in rheumatoid arthritis: current status and future perspectives: a state-of-the-art review. Rheumatology and Therapy, 9(5), 1249-1304.
11. Chen, S., Zhu, P., Mao, L., Wu, W., Lin, H., Xu, D., … & Shi, J. (2023). Piezocatalytic medicine: an emerging frontier using piezoelectric materials for biomedical applications. Advanced Materials, 35(25), 2208256.
12. Chiarion, G., Sparacino, L., Antonacci, Y., Faes, L., & Mesin, L. (2023). Connectivity analysis in EEG data: a tutorial review of the state of the art and emerging trends. Bioengineering, 10(3), 372.
13. Yin, L., Li, W., Du, Y., Wang, K., Liu, Z., Hui, H., & Tian, J. (2022). Recent developments of the reconstruction in magnetic particle imaging. Visual Computing for Industry, Biomedicine, and Art, 5(1), 24.
14. Punn, N. S., & Agarwal, S. (2022). Modality specific U-Net variants for biomedical image segmentation: a survey. Artificial Intelligence Review, 55(7), 5845-5889.
15. Nam, D., Chapiro, J., Paradis, V., Seraphin, T. P., & Kather, J. N. (2022). Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Reports, 4(4), 100443.
16. Quer, G., Arnaout, R., Henne, M., & Arnaout, R. (2021). Machine learning and the future of cardiovascular care: JACC state-of-the-art review. Journal of the American College of Cardiology, 77(3), 300-313.
17. Chen, Z., Pawar, K., Ekanayake, M., Pain, C., Zhong, S., & Egan, G. F. (2023). Deep learning for image enhancement and correction in magnetic resonance imaging — state-of-the-art and challenges. Journal of Digital Imaging, 36(1), 204-230.
18. Carriero, A., Groenhoff, L., Vologina, E., Basile, P., & Albera, M. (2024). Deep learning in breast cancer imaging: State of the art and recent advancements in early 2024. Diagnostics, 14(8), 848.

