A Situation-Aware DSS to Support Assisted Reproductive Technology Outcome Prediction

Assisted reproductive technology (ART) has been using increasingly over the years as infertility treatment globally among women of different ages since the first baby was born as a result of in vitro fertilization in the UK in 1978. Despite this diffusion, the success rate of assisted reproductive technology is relatively low. The main reasons are poor quality of embryos and eggs, age of the woman, small uterine cavity, immune system disorders and genetic factors such as chromosomal alterations. ART procedures are associated with risk for mothers and infants (e.g., miscarriage, premature delivery, low birth weight infants, and long-term disability among infants). Furthermore, since these treatments are highly expensive and time-consuming, their repetition is not affordable for most infertile couples. An adequate design of a decision support system and its capability to support ART outcome prediction can help infertile couples. In this work, we propose an approach to predict the outcome of ART procedures, based on the identification of the situation of the infertile couple (mainly in terms of physiological parameters), and to provide the doctor with a set of feedback aimed at supporting the doctor's decision to start or continue ART process. A prototypical system is proposed to preliminary verify the feasibility and utility of the approach.