Modeling and Prediction of Outcome for the Superovulation Stage in In-Vitro Fertilization(IVF)
|Yenkie KM1,2, Diwekar UM1,2* and Bhalerao V3|
|1Department of Bioengineering, University of Illinois, Chicago, IL 60607, USA|
|2Center for Uncertain Systems: Tools for Optimization & Management (CUSTOM), Vishwamitra Research Institute, Clarendon Hills, IL 60514, USA|
|3Jijamata Hospital, Nanded, Maharashtra, India|
|*Corresponding Author :||Urmila M Diwekar
Center for Uncertain Systems: Tools for Optimization & Management (CUSTOM)
Vishwamitra Research Institute, Clarendon Hills, IL 60514, USA
E-mail: [email protected]
|Received January 29, 2014; Accepted April 27, 2014; Published April 27, 2014|
|Citation: Yenkie KM, Diwekar UM, Bhalerao V (2014) Modeling and Prediction of Outcome for the Superovulation Stage in In-Vitro Fertilization (IVF). J Fertil In Vitro IVF Worldw Reprod Med Genet Stem Cell Biol 2:122. doi: 10.4172/jfiv.1000122|
|Copyright: © 2014 Yenkie KM, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.|
Background: IVF is divided into four stages: superovulation, egg-retrieval, fertilization and embryo transfer. The superovulation stage has specific protocols which include daily injections of hormones, decided by regular monitoring and testing involving cost intensive methods like ultrasound, to enable multiple ovulations per menstrual cycle. However, there is a lack of systematic planning and hormonal dosage prediction for successful superovulation using apriori information.
Methods: This work aims at developing a systematic outcome projection method for superovulation based on initial observations in an IVF cycle. The information about the size range and number of follicles was transformed to moment based information using the mathematical approach from crystallization literature. The follicle growth was modeled as a function of injected hormones and the properties of the follicles were represented in terms of moments. The model parameters were estimated using the data obtained from initial two days of observation. This information was used to project the follicle size distribution for the remaining cycle days.
Results and Conclusion: The model assumptions and its correlation to batch crystallization prove promising on comparison of the simulated follicle size and number to the observed data for the patients. The model prediction accuracy is determined by the statistical analysis of clinical data available for 50 superovulation cycles. Thus, it can act as an indicator for the success or failure of the ongoing superovulation stage in the IVF cycle and the decision whether to continue with the procedure or abandon it and start from donor eggs can be made, thus saving treatment cost and time on unsuccessful attempts.