title: Predicting IVF Outcome: A Proposed Web-based System Using Artificial Intelligence.
abstract: OBJECTIVE
  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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Assisted Reproduction Unit, 3rd Department of Obstetrics and Gynecology, University of Athens, Attikon Hospital, Athens, Greece harrysiri@yahoo.gr.
authors.forename: Charalampos
authors.initials: C
authors.lastname: Siristatidis
authors.affiliation: Assisted Reproduction Unit, 3rd Department of Obstetrics and Gynecology, University of Athens, Attikon Hospital, Athens, Greece.
authors.forename: Paraskevi
authors.initials: P
authors.lastname: Vogiatzi
authors.affiliation: Department of Cytopathology, University of Athens, Attikon University Hospital, Athens, Greece.
authors.forename: Abraham
authors.initials: A
authors.lastname: Pouliakis
authors.affiliation: Centre for Statistics in Medicine, University of Oxford, Botnar Research Centre, Oxford, U.K.
authors.forename: Marialenna
authors.initials: M
authors.lastname: Trivella
authors.affiliation: Third Department of Obstetrics and Gynecology, University of Athens, Attikon Hospital, Athens, Greece.
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Papantoniou
authors.affiliation: First Unit of Obstetrics and Gynecology, Department of Biomedical Sciences and Human Oncology, University "Aldo Moro", Bari, Italy.
authors.forename: Stefano
authors.initials: S
authors.lastname: Bettocchi
mesh_terms: D001185:Artificial Intelligence / Q000706:statistics & numerical data*; D005260:Female; D005307:Fertilization in Vitro / Q000379:methods*; D006801:Humans; D020407:Internet / Q000706:statistics & numerical data*; D008954:Models, Biological; D010062:Ovulation Induction; D011247:Pregnancy; D011256:Pregnancy Outcome; D011379:Prognosis
keywords: Artificial neural network; assisted reproduction; in vitro fertilization; personalized treatment; prediction model; subfertility