Bayesian Network Modeling of IVF Blastocyst Score Prediction

Abstract

Embryo transfer may be performed at cleavage stage (on day 2-3) or at blastocyst stage (on day 5) in In-Vitro Fertilization (IVF) treatment. Elective single embryo transfer at blastocyst stage increases the pregnancy probability and reduces the number of multiple pregnancies. However, the extended culture of embryos in the laboratory may result in transfer cancelation if no high quality blastocyst develops by day 5. Predicting the blastocyst score of individual embryos may help physicians to decide whether or not to further culture the embryos in the laboratory. In this paper, we use Bayesian networks for predicting the blastocyst score by modeling the morphological evolution of IVF embryos. We propose a weighted nearest neighbor approach to adjust the frequency estimates in the conditional probability table. Experimental results show that the proposed method significantly increases the accuracy and reduces false positive rates in IVF data in comparison to the frequency estimate method. Our proposed model can also predict low quality blastocyst development with a 77.3% True Negative rate. Using this model can help preventing developmental failures of embryos during IVF treatment.

Keywords

• In-vitro fertilization
• Predicting blastocyst development
• Bayesian networks
• Parameter learning
• Frequency estimates

Bayes Ağları ile Tüp Bebek Tedavi Sürecinde Blastosist Skoru Tahmini

Abstract

Tüp bebek tedavisinde embriyo transferi bölünme aşamasında (gün 2-3) veya blastosist aşamasında (gün 5) gerçekleştirilebilir. Transfer öncesi tek embriyo seçimi ve transferi gebelik olasılığını arttırırken çoklu gebelik sayısını da düşürür. Diğer taraftan, laboratuvar ortamında uzayan embriyo kültürleme zamanı beşinci güne kadar yüksek kaliteli blastosist gelişmediği takdirde transferin iptal olmasına sebep olabilir. Blastosist skorlarının tahminlenmesi klinisyenlere her bir embriyonun laboratuvar ortamında kültürlenmeye devam edilip edilmeyeceği konusunda destek sağlayabilir. Bu çalışmada Bayes Ağları kullanarak, tüp bebek tedavi sürecinde embriyo morfolojik gelişim değerleri modellenerek blastosist skorları tahminlenmiştir. Çalışmada koşullu olasılık tablosundaki frekans tahminlerini ayarlamak için ağırlıklı en yakın komşu yaklaşımı önerilmiştir. Sonuçlar önerilen modelin tüp bebek tedavisinde doğruluğu önemli ölçüde artırırken yanlış pozitif oranının frekans tahmini yöntemine göre düşük olduğunu göstermektedir. Bunun yanında model düşük kaliteli blastosist gelişimini %77.3 oranıyla doğru negatif tahmin etmektedir. Bu da modelin kullanılmasının tüp bebek tedavisinde embriyo gelişimsel başarısızlığını ciddi ölçüde önlemeye yardımcı olacağını göstermektedir.

Keywords

• Tüp bebek tedavisi
• Blastosist gelişim tahminlemesi
• Bayes ağlar
• parametre öğrenimi
• frekans tahminleri

References

1. Blank, C., DeCroo, I., Weyers, B., van Avermaet, L., Tilleman, K., van Rumste, M., de Sutter, P., Mischi, M., & Schoot, B. C. (2020). Improvement instead of stability in embryo quality between day 3-5: A possible extra predictor for blastocyst selection. European Journal of Obstetrics and Gynecology and Reproductive Biology, 253, 198–205. https://doi.org/10.1016/j.ejogrb.2020.08.027
2. Cheng, J., Greiner, R., Kelly, J., Bell, D., & Liu, W. (2002). Learning Bayesian networks from data: An information-theory based approach. Artificial Intelligence, 137(1–2), 43–90. https://doi.org/10.1016/S0004-3702(02)00191-1
3. Csató, L., & Reiz, B. (2008). Tree-like Bayesian Network classifiers for surgery survival chance prediction. In Article in International Journal of Computers: Vol. III. https://www.researchgate.net/publication/228634935
4. Dessolle, L., Fréour, T., Barrire, P., Daraï, E., Ravel, C., Jean, M., & Coutant, C. (2010). A cycle-based model to predict blastocyst transfer cancellation. Human Reproduction, 25(3), 598–604. https://doi.org/10.1093/humrep/dep439
5. Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian Network Classifiers. Machine Learning, 29(2–3), 131–163. https://doi.org/10.1023/a:1007465528199
6. Gardner, D. K., Lane, M., Stevens, J., Schlenker, T., & Schoolcraft, W. B. (2000). Blastocyst score affects implantation and pregnancy outcome: towards a single blastocyst transfer. Fertility and Sterility, 73(6), 1155–1158. https://doi.org/10.1016/S0015-0282(00)00518-5
7. Gardner, D. K., Surrey, E., Minjarez, D., Leitz, A., Stevens, J., & Schoolcraft, W. B. (2004). Single blastocyst transfer: A prospective randomized trial. Fertility and Sterility, 81(3), 551–555. https://doi.org/10.1016/j.fertnstert.2003.07.023
8. Gerris, J., & De Neubourg, D. (n.d.). Single embryo transfer after IVF/ICSI: present possibilities and limits.

9. Greiner, R., Su, X., Shen, B., & Zhou, W. (2005). Structural extension to logistic regression: Discriminative parameter learning of belief net classifiers. Machine Learning, 59(3), 297–322. https://doi.org/10.1007/s10994-005-0469-0
10. Heckerman, D. (2020). A Tutorial on Learning With Bayesian Networks. Studies in Computational Intelligence, 156, 33–82. http://arxiv.org/abs/2002.00269
11. Irmawati, Basari, & Gunawan, D. (2019). Automated Detection of Human Blastocyst Quality Using Convolutional Neural Network and Edge Detector. 2019 1st International Conference on Cybernetics and Intelligent System, ICORIS 2019, 181–184. https://doi.org/10.1109/ICORIS.2019.8874925
12. Kohavi, R., Langley, P., & Yun, Y. (n.d.). The Utility of Feature Weighting in Nearest-Neighbor Algorithms.
13. Lucas, P. J. F. (2004). Restricted Bayesian Network Structure Learning (pp. 217–234). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39879-0_12
14. Martikainen, H., Orava, M., Lakkakorpi, J., & Tuomivaara, L. (2004). Day 2 elective single embryo transfer in clinical practice: Better outcome in ICSI cycles. Human Reproduction, 19(6), 1364–1366. https://doi.org/10.1093/humrep/deh197
15. Meloni, A., Ripoli, A., Positano, V., & Landini, L. (2009). Mutual information preconditioning improves structure learning of bayesian networks from medical databases. IEEE Transactions on Information Technology in Biomedicine, 13(6), 984–989. https://doi.org/10.1109/TITB.2009.2026273
16. Mladenić, D., & Grobelnik, M. (2003). Feature selection on hierarchy of web documents. Decision Support Systems, 35(1), 45–87. https://doi.org/10.1016/S0167-9236(02)00097-0
17. Papanikolaou, E. G., Kolibianakis, E. M., Tournaye, H., Venetis, C. A., Fatemi, H., Tarlatzis, B., & Devroey, P. (2008). Live birth rates after transfer of equal number of blastocysts or cleavage-stage embryos in IVF. A systematic review and meta-analysis. Human Reproduction, 23(1), 91–99. https://doi.org/10.1093/humrep/dem339
18. Steptoe, P. C., & Edwards, R. G. (1978). Birth after the reimplantation of a human embryo. In Lancet (Vol. 2, Issue 8085, p. 366). Lancet. https://doi.org/10.1016/s0140-6736(78)92957-4
19. Su, J., Zhang, H., Ling, C. X., & Matwin, S. (2008). Discriminative parameter learning for Bayesian networks. Proceedings of the 25th International Conference on Machine Learning, 1016–1023. https://doi.org/10.1145/1390156.1390284
20. Thurin, A., Hausken, J., Hillensjö, T., Jablonowska, B., Pinborg, A., Strandell, A., & Bergh, C. (2004). Elective Single-Embryo Transfer versus Double-Embryo Transfer in in Vitro Fertilization. New England Journal of Medicine, 351(23), 2392–2402. https://doi.org/10.1056/nejmoa041032
21. Uyar, A., Bener, A., Ciray, H. N., & Bahceci, M. (2010). Bayesian networks for predicting IVF blastocyst development. Proceedings - International Conference on Pattern Recognition, 2772–2775. https://doi.org/10.1109/ICPR.2010.679
22. Veleva, Z., Vilska, S., Hydén-Granskog, C., Tiitinen, A., Tapanainen, J. S., & Martikainen, H. (2006). Elective single embryo transfer in women aged 36-39 years. Human Reproduction, 21(8), 2098–2102. https://doi.org/10.1093/humrep/del137
23. Vivencio, D. P., Hruschka, E. R., Do Carmo Nicoletti, M., Dos Santos, E. B., & Galvão, S. D. C. O. (2007). Feature-weighted k-nearest neighbor classifier. Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007, 481–485. https://doi.org/10.1109/FOCI.2007.371516
24. Zhan, Q., Sierra, E. T., Malmsten, J., Ye, Z., Rosenwaks, Z., & Zaninovic, N. (2020). Blastocyst score, a blastocyst quality ranking tool, is a predictor of blastocyst ploidy and implantation potential. F&S Reports, 1(2), 133–141. https://doi.org/10.1016/j.xfre.2020.05.004