Selection of human embryos for transfer by Bayesian classifiers

In this work we approach by Bayesian classifiers the selection of human embryos from images. This problem consists of choosing the embryos to be transferred in human-assisted reproduction treatments, which Bayesian classifiers address as a supervised classification problem. Different Bayesian classifiers capable of taking into account diverse dependencies between variables of this problem are tested in order to analyse their performance and validity for building a potential decision support system. The analysis by receiver operating characteristic (ROC) proves that the Bayesian classifiers presented in this paper are an appropriated and robust approach for this aim. From the Bayesian classifiers tested, the tree augmented naive Bayes, k-dependence Bayesian and naive Bayes classifiers showed to perform almost as well as the semi naive Bayes and selective naive Bayes classifiers.

[1]  D. De Neubourg,et al.  Characterization of a top quality embryo, a step towards single-embryo transfer. , 1999, Human reproduction.

[2]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[3]  D. Bamber.  The area above the ordinal dominance graph and the area below the receiver operating characteristic graph , 1975 .

[4]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[5]  Fritz Albregtsen,et al.  Texture Classification of Mouse Liver Cell Nuclei Using Invariant Moments of Consistent Regions , 1995, CAIP.

[6]  P Barlow,et al.  Embryo scoring as a prognostic tool in IVF treatment. , 1987, Human reproduction.

[7]  Marvin Minsky,et al.  Steps toward Artificial Intelligence , 1995, Proceedings of the IRE.

[8]  John Mylopoulos,et al.  Case-based reasoning in IVF: prediction and knowledge mining , 1998, Artif. Intell. Medicine.

[9]  James P. Egan,et al.  Signal detection theory and ROC analysis , 1975 .

[10]  Pedro Larrañaga,et al.  Bayesian classification for the selection of in vitro human embryos using morphological and clinical data , 2008, Comput. Methods Programs Biomed..

[11]  Alfredo Ruggeri,et al.  Automatic recognition of cell layers in corneal confocal microscopy images , 2002, Comput. Methods Programs Biomed..

[12]  G. Patrizi,et al.  Experimental results on the recognition of embryos in human assisted reproduction. , 2004, Reproductive biomedicine online.

[13]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[14]  Pedro Larrañaga,et al.  Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS , 2005, J. Biomed. Informatics.

[15]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[16]  G A Hill,et al.  The influence of oocyte maturity and embryo quality on pregnancy rate in a program for in vitro fertilization-embryo transfer. , 1989, Fertility and sterility.

[17]  D. Louis Collins,et al.  Brain morphometry using 3D moment invariants , 2004, Medical Image Anal..

[18]  M. Maloof.  Learning When Data Sets are Imbalanced and When Costs are Unequal and Unknown , 2003 .

[19]  Pedro Larrañaga,et al.  Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes , 2006, Int. J. Approx. Reason..

[20]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[21]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[22]  Lutz Hamel,et al.  Comparing data mining and logistic regression for predicting IVF outcome , 2003 .

[23]  Doug Fisher,et al.  Learning from Data: Artificial Intelligence and Statistics V , 1996 .

[24]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[25]  C. Giorgetti,et al.  Implantation: Embryo score to predict implantation after in-vitro fertilization: based on 957 single embryo transfers , 1995 .

[26]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[27]  V. Gomel,et al.  The effect of embryo quality on subsequent pregnancy rates after in vitro fertilization. , 1991, Fertility and sterility.

[28]  Tom Fawcett,et al.  Robust Classification for Imprecise Environments , 2000, Machine Learning.

[29]  R. Saith,et al.  Relationships between the developmental potential of human in-vitro fertilization embryos and features describing the embryo, oocyte and follicle. , 1998, Human reproduction update.

[30]  R. Grol,et al.  Determinants of the choice for single or double embryo transfer in twin prone couples , 2008, Acta obstetricia et gynecologica Scandinavica.

[31]  Mehran Sahami,et al.  Learning Limited Dependence Bayesian Classifiers , 1996, KDD.

[32]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[33]  Michael J. Pazzani,et al.  Searching for Dependencies in Bayesian Classifiers , 1995, AISTATS.

[34]  Greta Verheyen,et al.  Developmental stage on day-5 and fragmentation rate on day-3 can influence the implantation potential of top-quality blastocysts in IVF cycles with single embryo transfer , 2007, Reproductive biology and endocrinology : RB&E.

[35]  Ron Kohavi,et al.  The Case against Accuracy Estimation for Comparing Induction Algorithms , 1998, ICML.

[36]  Y Beyth,et al.  Relationship between embryo morphology and implantation rate after in vitro fertilization treatment in conception cycles. , 1993, Fertility and sterility.

[37]  Marinus J C Eijkemans,et al.  Predictors of ongoing pregnancy after single-embryo transfer following mild ovarian stimulation for IVF. , 2008, Fertility and sterility.

[38]  Andrew McCallum,et al.  Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data , 2004, J. Mach. Learn. Res..

[39]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[40]  Pat Langley,et al.  Induction of Selective Bayesian Classifiers , 1994, UAI.

[41]  Kent A. Spackman,et al.  Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning , 1989, ML.

[42]  David G. Stork,et al.  Pattern Classification , 1973 .

[43]  Igor Kononenko,et al.  Semi-Naive Bayesian Classifier , 1991, EWSL.

[44]  Lucila Ohno-Machado,et al.  The use of receiver operating characteristic curves in biomedical informatics , 2005, J. Biomed. Informatics.

[45]  L. Nieddu,et al.  Pattern recognition methods in human‐assisted reproduction , 2004 .

[46]  Lynette Scott,et al.  Pronuclear scoring as a predictor of embryo development. , 2003, Reproductive biomedicine online.