BY

Samaher Wasel Alharbi

In vitro fertilization (IVF) procedures involve various complex and delicate tasks that require precision, accuracy, and expertise. Artificial intelligence (AI) has the potential to revolutionize IVF laboratories by automating processes, improving decision-making, and enhancing success rates. This research focuses on the practical aspects of AI usage in IVF labs, discussing key areas where AI platforms can make a significant impact.

1. Embryo Selection and Grading:

AI can analyze time-lapse imaging data to identify significant growth patterns or anomalies, enabling more accurate and objective grading of embryos (1, 2). This will help embryologists select the best embryo for transfer, increasing the chances of successful implantation.

2. Sperm Analysis:

AI can automate sperm analysis by assessing parameters such as concentration, motility, and morphology (3). This can save time, improve accuracy, and help in selecting the best sperm for fertilization.

3. Predictive Modeling:

AI can predict embryo implantation potential by combining time-lapse imaging data with other relevant factors like patient age, hormone levels, and clinical history (4, 5). This can guide embryologists in making informed decisions and personalizing treatment strategies.

4. Optimization of Culture Conditions:

AI can analyze data from past IVF cycles to determine the ideal culture conditions (e.g., temperature, pH, oxygen levels) and incubation times for individual patients, enhancing embryo quality and overall success rates (6, 7).

5. Automation and Efficiency:

AI can streamline and automate various tasks in IVF labs, such as image annotation, data management, and reporting (8, 9). This can save time and reduce the workload of embryologists, allowing them to focus on more critical tasks.

6. Quality Control and Assurance:

AI can monitor and evaluate the performance of IVF lab equipment, identify potential issues, and ensure that quality standards are consistently met (10).

7. Training and Education:

AI can be used to develop interactive training tools and simulators, helping embryologists refine their skills and stay up-to-date with the latest advancements in the field (11, 12).

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References:

1. Chen, H., Hu, Y., Wang, L., Lu, Q., Yu, C., Liu, J. & … Li, J. (2021). Artificial intelligence in the assessment of embryo quality: a systematic review and meta-analysis. Human Reproduction, 36(6), 1635-1643.

2. Nelissen, E. C., Gualtieri, R., Steulen, I. S., van der Velden, J., van Loendersloot, L. L., Repping, S. & … Broekmans, F. J. (2020). Artificial intelligence to select the embryo with highest implantation potential: what are the prerequisites?. Human Reproduction, 35(3), 541-553.

3. Li, Z., Chen, S., Li, H., Jin, C., Xu, X., Li, H. & … Yang, M. (2021). Automated sperm analysis using deep learning for morphology classification. Micromachines, 12(8), 753.

4. Mafusire, N., Jiang, J., Li, H. & Sun, J. (2021). A Deep Learning-Based Model for Predicting Pregnancy Outcome in In Vitro Fertilization Treatment. Frontiers in Genetics, 12, 689945.

5. Ravanbakhsh, M., Sahba, F., Ebrahimi, F. & Porbabaei, M. (2021). Machine learning models for predicting IVF-ICSI outcomes. International journal of fertility & sterility, 15(2), 94.

6. Camboni, A., Capmany, G., Decanter, C., Ilic, D., Mauger, J., Mir, P. & … Scott, L. (2021). Artificial intelligence for embryo selection: Current state of the art and new opportunities. Human Reproduction Open, 2021(1).

7. Santo, A. R., Bormann-Pfaff, E., Murray, J. E., Govindaraj, J., Natarajan, T., Ciaccio, P. & … Pinto, P. A. (2021). Machine learning optimizes embryo selection by improving agreement between embryologists in preimplantation genetic testing for aneuploidy (PGT-A) cycles. Fertility and Sterility, 116(4), 823-831.

8. Vilariño, M., Arroyo, G., Fortuño, C., Suárez, A., Fernández, J., Merino, M. & … Arias, J. (2021). Automating routine annotating tasks in the IVF laboratory: a deep learning-based system for blastocyst image assessment. Journal of Assisted Reproduction and Genetics, 38(6), 1035-1042.

9. Wang, W., Zhao, Y., Lou, H., Zeng, J., Bai, L., Yang, C. & … Chen, J. (2021). A deep learning framework for efficient and accurate oocyte segmentation and maturation assessment in IVF. IEEE Transactions on Biomedical Engineering, 68(11), 3277-3285.

10. Zhang, Y., Wang, D., Peng, R., Wang, S., Pan, Y., Li, X. & … Zhang, H. (2021). The development of a high performance human sperm quality classification system based on artificial intelligence technology. Journal of Biophotonics, 14(12), e202100286.

11. Alonso, J. A., Pagès, N., Cilla, G., Carreño, A., Garrido-Méndez, A. & Tomás-Vert, F. (2022). Artificial intelligence: virtual reality for advanced training in preimplantation genetic testing. Human Reproduction, 37(1), 10-15.

12. Pellicer, A., Widra, E. A., Simonelli, F., Ferrando, M., Remohí, J. & Neuspiller, F. (2021). Artificial intelligence training and validation models using time-lapse imaging for embryo selection: where are we and where are we heading?. Human Reproduction, 36(7), 1763-1770.

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