Yıl 2021, Cilt 12 , Sayı 45, Sayfalar 12 - 28 2021-05-21

Bayes Ağları ile Tüp Bebek Tedavi Sürecinde Blastosist Skoru Tahmini
Bayesian Network Modeling of IVF Blastocyst Score Prediction

Asli UYAR [1] , Yasemin ATILGAN ŞENGÜL [2]


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.
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.
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Birincil Dil en
Konular Fen
Bölüm Araştırma Makaleleri
Yazarlar

Orcid: 0000-0002-7913-1083
Yazar: Asli UYAR
Kurum: OKAN UNIVERSITY
Ülke: Turkey


Orcid: 0000-0002-5109-2262
Yazar: Yasemin ATILGAN ŞENGÜL (Sorumlu Yazar)
Kurum: DOĞUŞ ÜNİVERSİTESİ
Ülke: Turkey


Tarihler

Başvuru Tarihi : 22 Mart 2021
Kabul Tarihi : 1 Mayıs 2021
Yayımlanma Tarihi : 21 Mayıs 2021

APA Uyar, A , Atılgan Şengül, Y . (2021). Bayesian Network Modeling of IVF Blastocyst Score Prediction . AJIT-e: Bilişim Teknolojileri Online Dergisi , 12 (45) , 12-28 . DOI: 10.5824/ajite.2021.02.001.x