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Bayesian Network Modeling of IVF Blastocyst Score Prediction

Yıl 2021, Cilt: 12 Sayı: 45, 12 - 28, 21.05.2021

Asli Uyar Yasemin Atılgan Şengül

https://doi.org/10.5824/ajite.2021.02.001.x

Öz

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.

Anahtar Kelimeler

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

Kaynakça

  • 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
  • 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
  • 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
  • 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
  • Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian Network Classifiers. Machine Learning, 29(2–3), 131–163. https://doi.org/10.1023/a:1007465528199
  • 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
  • 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
  • Gerris, J., & De Neubourg, D. (n.d.). Single embryo transfer after IVF/ICSI: present possibilities and limits.
  • 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
  • Heckerman, D. (2020). A Tutorial on Learning With Bayesian Networks. Studies in Computational Intelligence, 156, 33–82. http://arxiv.org/abs/2002.00269
  • 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
  • Kohavi, R., Langley, P., & Yun, Y. (n.d.). The Utility of Feature Weighting in Nearest-Neighbor Algorithms.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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

Bayes Ağları ile Tüp Bebek Tedavi Sürecinde Blastosist Skoru Tahmini

Yıl 2021, Cilt: 12 Sayı: 45, 12 - 28, 21.05.2021

Asli Uyar Yasemin Atılgan Şengül

https://doi.org/10.5824/ajite.2021.02.001.x

Öz

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.

Anahtar Kelimeler

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

Kaynakça

  • 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
  • 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
  • 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
  • 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
  • Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian Network Classifiers. Machine Learning, 29(2–3), 131–163. https://doi.org/10.1023/a:1007465528199
  • 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
  • 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
  • Gerris, J., & De Neubourg, D. (n.d.). Single embryo transfer after IVF/ICSI: present possibilities and limits.
  • 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
  • Heckerman, D. (2020). A Tutorial on Learning With Bayesian Networks. Studies in Computational Intelligence, 156, 33–82. http://arxiv.org/abs/2002.00269
  • 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
  • Kohavi, R., Langley, P., & Yun, Y. (n.d.). The Utility of Feature Weighting in Nearest-Neighbor Algorithms.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makaleleri
Yazarlar

Asli Uyar 0000-0002-7913-1083

Yasemin Atılgan Şengül 0000-0002-5109-2262

Yayımlanma Tarihi 21 Mayıs 2021
Gönderilme Tarihi 22 Mart 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 12 Sayı: 45

Kaynak Göster

APA Uyar, A., & Atılgan Şengül, Y. (2021). Bayesian Network Modeling of IVF Blastocyst Score Prediction. AJIT-E: Academic Journal of Information Technology, 12(45), 12-28. https://doi.org/10.5824/ajite.2021.02.001.x
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