Artificial Intelligence in IVF: A Need

References

• Angelini, A., Brusco, G.F., Barnocchi, N., El-Danasouri, I., Pacchiarotti, A. and Selman, H.A. (2006) Impact of physician performing embryo transfer on pregnancy rates in an assisted reproductive program. J Assist Reprod Genet 23:329–332.
   PubMedGoogle Scholar
• Bancsi, L.F., Broekmans, F.J., Looman, C.W., Habbema, J.D. and te Velde, E.R. (2004) Impact of repeated antral follicle counts on the prediction of poor ovarian response in women undergoing in vitro fertilization. Fertil Steril 81:35–41.
   PubMed Web of Science ®Google Scholar
• Carney J.G. and Cunningham P. (1999) The NeuralBAG algorithm: Optimizing generalization performance in bagged neural networks. 7th European Symposium on Artificial Neural Networks, Bruges, Belgium.
   Google Scholar
• Cochand-Priollet, B., Koutroumbas, K., Megalopoulou, T.M., Pouliakis, A., Sivolapenk, G. and Karakitsos, P. (2006) Discriminating benign from malignant thyroid lesions using artificial intelligence and statistical selection of morphometric features. Oncology Reports 15:1023–1026.
   PubMedGoogle Scholar
• Creus, M., Penarrubia, J., Fabregues, F., Vidal, E., Carmona, F., Casamitjana, R., (2000) Day 3 serum inhibin B and FSH and age as predictors of assisted reproduction treatment outcome. Hum Reprod 15:2341–2346.
   PubMed Web of Science ®Google Scholar
• Cunningham, P., Carney, J. and Jacob, S. (2000) Stability problems with artificial neural networks and the ensemble solution. Artif Intell Med 20:217–225.
   PubMed Web of Science ®Google Scholar
• Cunningham, P. and Walsh, P. (2002) Principles of Data Mining and Knowledge Discovery. Springer Press, NY, USA.
   Google Scholar
• Dessolle, L., Freour, T., Barriere, P., Darai, E., Ravel, C., Jean, M., (2010) A cycle-based model to predict blastocyst transfer cancellation. Hum Reprod 25:598–604.
   PubMed Web of Science ®Google Scholar
• Eastham, J.A., Kattan, M.W. and Scardino, P.T. (2002) Nomograms as predictive models. Semin Urol Oncol 20:108–115.
   PubMedGoogle Scholar
• Ecke, T.H., Bartel, P., Hallmann, S., Koch, S., Ruttloff, J., Cammann, H., et al. (2010) Outcome prediction for prostate cancer detection rate with artificial neural network (ANN) in daily routine. Urol Oncol Epub ahead of print.
   Google Scholar
• Frattarelli, J.L. and Gerber, M.D. (2006) Basal and cycle androgen levels correlate with in vitro fertilization stimulation parameters but do not predict pregnancy outcome. Fertil Steril 86:51–57.
   PubMed Web of Science ®Google Scholar
• Gnoth, C., Schuring, A.N., Friol, K., Tigges, J., Mallmann, P. and Godehardt, E. (2008) Relevance of anti-Mullerian hormone measurement in a routine IVF program. Hum Reprod 23:1359–1365.
   PubMed Web of Science ®Google Scholar
• Haykin, S.S. (1994) Neural networks: a comprehensive foundation. xix, Macmillan College Publishing Company: NY, USA.
   Google Scholar
• Hopfield, J.J. (1982) Neural networks and physical systems with emergent collective computational abilities, Proc. of the Nat. Acad. of Sciences of the USA, 79:2554–2558.
   Google Scholar
• Hunault, C.C., Eijkemans, M.J., Pieters, M.H., te Velde, E.R., Habbema, J.D., Fauser, B.C., (2002) A prediction model for selecting patients undergoing in vitro fertilization for elective single embryo transfer. Fertil Steril 77:725–732.
   PubMed Web of Science ®Google Scholar
• Jiang, Y., Zhou, Z.-H. and Chen, Z.-Q. (2002) Rule Learning based on Neural Network Ensemble. Proceedings of the International Joint Conference on Neural Networks, Honolulu, HI, USA, 1416–1420.
   Google Scholar
• Jurisica, I., Mylopoulos, J., Glasgow, J., Shapiro, H. and Casper, R.F. (1998) Case-based reasoning in IVF: prediction and knowledge mining. Artif Intell Med 12:1–24.
   PubMedGoogle Scholar
• Karakitsos, P., Kyroudes, A., Pouliakis, A., Stergiou, E.B., Voulgaris, Z. and Kittas, C. (2002) Potential of the learning vector quantizer in the cell classification of endometrial lesions in postmenopausal women. Anal Quant Cytol Histol 24:30–38.
   PubMedGoogle Scholar
• Karakitsos, P., Pouliakis, A., Kordalis, G., Georgoulakis, J., Kittas, C. and Kyroudes, A. (2005) Potential of radial basis function neural networks in discriminating benign from malignant lesions of the lower urinary tract. Anal Quant Cytol Histol 27:35–42.
   PubMedGoogle Scholar
• Kaufmann, S.J., Eastaugh, J.L., Snowden, S., Smye, S.W. and Sharma, V. (1997) The application of neural networks in predicting the outcome of in-vitro fertilization. Hum Reprod 12:1454–1457.
   PubMed Web of Science ®Google Scholar
• Kohonen, T. (1988) Self-organization and associative memory. Springer series in information sciences Springer-Verlag, NY, USA.
   Google Scholar
• Kohonen, T., Hynninen, J., Kangas, J., Laaksonen, J. and Torkkola, K. (1995) LVQ_PAK The Learning Vector Quantization Program Package: a Software program manual from Helsinki University available from: http://www.cis.hut.fi/research/papers/lvq_tr96.ps.Z.
   Google Scholar
• Kohonen, T., Kangas, J., Laaksonen, J. and Torkkola, K. (1992) LVQPAK: A software package for the correct application of Learning Vector Quantization algorithms. Neural Networks, 1992. International Joint Conference on Neural Networks, Baltimore, MD, USA 1:725–730 vol.721.
   Google Scholar
• Kwee, J., Elting, M.E., Schats, R., McDonnell, J. and Lambalk, C.B. (2007) Ovarian volume and antral follicle count for the prediction of low and hyper responders with in vitro fertilization. Reprod Biol Endocrinol 5:9.
   PubMed Web of Science ®Google Scholar
• Lin, H.C., Su, C.T. and Wang, P.C. (2009) An Application of Artificial Immune Recognition System for Prediction of Diabetes Following Gestational Diabetes. J Med Syst Epub ahead of print.
   Google Scholar
• Lovell, D.R., Rosario, B., Niranjan, M., Prager, R.W., Dalton, K.J., Derom, R., (1997) Design, construction and evaluation of systems to predict risk in obstetrics. Int J Med Inform 46:159–173.
   PubMedGoogle Scholar
• Markopoulos, C., Karakitsos, P., Botsoli-Stergiou, E., Pouliakis, A., Ioakim-Liossi, A., Kyrkou, K., (1997) Application of the learning vector quantizer to the classification of breast lesions. Anal Quant Cytol Histol 19:453–460.
   PubMedGoogle Scholar
• McCulloch, W. and Pitts, W. (1943) A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 7:115–133.
   Google Scholar
• Milewski, R., Jamiolkowski, J., Milewska Anna, J., Domitrz, J., Szamatowicz, J. and Wolczynski, S. (2009) Prognosis of the IVF ICSI/ET procedure efficiency with the use of artificial neural networks among patients of the Department of Reproduction and Gynecological Endocrinology. Ginekol Pol 80:900–906.
   PubMedGoogle Scholar
• Minsky M.L. and Papert S. (1969) Perceptrons; an introduction to computational geometry, MIT Press.
   Google Scholar
• Morales, D.A., Bengoetxea, E., Larranaga, P., Garcia, M., Franco, Y., Fresnada, M., (2008) Bayesian classification for the selection of in vitro human embryos using morphological and clinical data. Comput Methods Programs Biomed 90:104–116.
   PubMedGoogle Scholar
• Muasher, S.J., Oehninger, S., Simonetti, S., Matta, J., Ellis, L.M., Liu, H.C., (1988) The value of basal and/or stimulated serum gonadotropin levels in prediction of stimulation response and in vitro fertilization outcome. Fertil Steril 50:298–307.
   PubMed Web of Science ®Google Scholar
• Naik, P.K., Mittal, V.K. and Gupta, S. (2008) RetroPred: A tool for prediction, classification and extraction of non-LTR retrotransposons (LINEs & SINEs) from the genome by integrating PALS, PILER, MEME and ANN. Bioinformation 2:263–270.
   PubMedGoogle Scholar
• Opitz, D. and Shavlik J. (1996) Advances in Neural Information Processing Systems. Generating accurate and diverse members of a neural network ensemble 8:535–541.
   Google Scholar
• Ottosen, L.D., Kesmodel, U., Hindkjaer, J. and Ingerslev, H.J. (2007) Pregnancy prediction models and eSET criteria for IVF patients–do we need more information? J Assist Reprod Genet 24:29–36.
   PubMed Web of Science ®Google Scholar
• Pantazopoulos, D., Karakitsos, P., Iokim-Liossi, A., Pouliakis, A., Botsoli-Stergiou, E. and Dimopoulos, C. (1998) Back propagation neural network in the discrimination of benign from malignant lower urinary tract lesions. J Urol 159:1619–1623.
   PubMedGoogle Scholar
• Papik, K., Molnar, B., Schaefer, R., Dombovari, Z., Tulassay, Z. and Feher, J. (1998) Application of neural networks in medicine - a review. Med Sci Monit 4:MT538–546.
   Google Scholar
• Repping, S., van Weert, J.M., Mol, B.W., de Vries, J.W. and van der Veen, F. (2002) Use of the total motile sperm count to predict total fertilization failure in in vitro fertilization. Fertil Steril 78:22–28.
   PubMed Web of Science ®Google Scholar
• Saith, R.R., Srinivasan, A., Michie, D. and Sargent, I.L. (1998) Relationships between the developmental potential of human in-vitro fertilization embryos and features describing the embryo, oocyte and follicle. Hum Reprod Update 4:121–134.
   PubMed Web of Science ®Google Scholar
• Sharkey, A.J.C. (1999) Combining artificial neural nets: ensemble and modular multi-net systems. Perspectives in neural computing, Springer-Verlag, NY, USA.
   Google Scholar
• Shu, C. and Burn, D.H. (2004) Artificial neural network ensembles and their application in pooled flood frequency analysis. Water Resources Research 40:1–10.
   Google Scholar
• Sima, J. and Orponen, P.A. (2001) Computational Taxonomy and Survey of Neural Network Models In of Numbers and Symbols 12:2965–2989.
   Google Scholar
• Srouji, S.S., Mark, A., Levine, Z., Betensky, R.A., Hornstein, M.D. and Ginsburg, E.S. (2005) Predicting in vitro fertilization live birth using stimulation day 6 estradiol, age, and follicle-stimulating hormone. Fertil Steril 84:795–797.
   PubMedGoogle Scholar
• Uyar, A., Bener, A., Ciray, H. and Bahceci, M. (2009) A frequency based encoding technique for transformation of categorical variables in mixed IVF dataset. Conf Proc IEEE Eng Med Biol Soc 2009:6214–6217.
   Google Scholar
• Uyar, A., Bener, A., Ciray, H.N. and Bahceci, M. (2009) Adjusting decision threshold in Naive Bayes based IVF embryo selection. Biomedical Engineering Meeting, BIYOMUT, Izmir, Turkey, 2009. 14th National:1–4. DOI: 10.1109/BIYOMUT.2009.5130302.
   Google Scholar
• van Weert, J.M., Repping, S., van der Steeg, J.W., Steures, P., van der Veen, F. and Mol, B.W. (2008) A prediction model for ongoing pregnancy after in vitro fertilization in couples with male subfertility. J Reprod Med 53:250–256.
   PubMedGoogle Scholar
• Verhagen, T.E., Hendriks, D.J., Bancsi, L.F., Mol, B.W. and Broekmans, F.J. (2008) The accuracy of multivariate models predicting ovarian reserve and pregnancy after in vitro fertilization: a meta-analysis. Hum Reprod Update 14:95–100.
   PubMed Web of Science ®Google Scholar
• Wald, M., Sparks, A.E., Sandlow, J., Van-Voorhis, B., Syrop, C.H. and Niederberger, C.S. (2005) Computational models for prediction of IVF/ICSI outcomes with surgically retrieved spermatozoa. Reprod Biomed Online 11:325–331.
   PubMedGoogle Scholar
• Werbos, P.J. (1990) Backpropagation through time: what it does and how to do it. Proceedings of the IEEE 78:1550–1560.
   Google Scholar
• Yi, W.J., Park, K.S. and Paick, J.S. (1998) Morphological classification of sperm heads using artificial neural networks. Stud Health Technol Inform 52 Pt 2:1071–1074.
   PubMedGoogle Scholar