Charalampos Siristatidis1, Paraskevi Vogiatzi2, Abraham Pouliakis3, Marialenna Trivella4, Nikolaos Papantoniou5, Stefano Bettocchi6. 1. Assisted Reproduction Unit, 3rd Department of Obstetrics and Gynecology, University of Athens, Attikon Hospital, Athens, Greece harrysiri@yahoo.gr. 2. Assisted Reproduction Unit, 3rd Department of Obstetrics and Gynecology, University of Athens, Attikon Hospital, Athens, Greece. 3. Department of Cytopathology, University of Athens, Attikon University Hospital, Athens, Greece. 4. Centre for Statistics in Medicine, University of Oxford, Botnar Research Centre, Oxford, U.K. 5. Third Department of Obstetrics and Gynecology, University of Athens, Attikon Hospital, Athens, Greece. 6. First Unit of Obstetrics and Gynecology, Department of Biomedical Sciences and Human Oncology, University "Aldo Moro", Bari, Italy.
Abstract
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.
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.
Authors: M Simopoulou; K Sfakianoudis; P Giannelou; A Rapani; E Maziotis; P Tsioulou; S Grigoriadis; E Simopoulos; D Mantas; M Lambropoulou; M Koutsilieris; K Pantos; J C Harper Journal: J Assist Reprod Genet Date: 2019-12-01 Impact factor: 3.412