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AIM: To propose a functional in vitro fertilization (IVF) prediction model to assist clinicians in tailoring personalized treatment of subfertile couples and improve assisted reproduction outcome. MATERIALS AND METHODS: Construction and evaluation of an enhanced web-based system with a novel Artificial Neural Network (ANN) architecture and conformed input and output parameters according to the clinical and bibliographical standards, driven by a complete data set and "trained" by a network expert in an IVF setting. RESULTS: The system is capable to act as a routine information technology platform for the IVF unit and is capable of recalling and evaluating a vast amount of information in a rapid and automated manner to provide an objective indication on the outcome of an artificial reproductive cycle. CONCLUSION: ANNs are an exceptional candidate in providing the fertility specialist with numerical estimates to promote personalization of healthcare and adaptation of the course of treatment according to the indications.

Type

Journal article

Journal

In Vivo

Publication Date

07/2016

Volume

30

Pages

507 - 512

Keywords

Artificial neural network, assisted reproduction, in vitro fertilization, personalized treatment, prediction model, subfertility, Artificial Intelligence, Female, Fertilization in Vitro, Humans, Internet, Models, Biological, Ovulation Induction, Pregnancy, Pregnancy Outcome, Prognosis