Project title
跨領域整合促成人工生殖技術之再進化
Internal ID
108-2314-B-002-146-MY3
Principal Investigator
Start Date
August 1, 2021
End Date
July 31, 2022
Investigators
Organizations
Partner Organizations
Interdisciplinary Integration in Catalyzing the Re-Evolution of Artificial Reproductive Technologies = 跨領域整合促成人工生殖技術之再進化
Description
Certain limitations in in vitro fertilization (IVF) have remained insurmountable. The yield rate from IVF cycles remains low and live birth rates are reported to range from 20 to 30%. Poor embryo selection methodologies often necessitated the transfer of multiple embryos, which in turn increased the chances of multifetal gestations. Premature birth, underweight fetus, maternal eclampsia/pre-eclampsia, and gestational diabetes invariably result. Ovarian stimulation are also plagued by an inability to optimally predict treatment response, and inadequately stimulation cycles have resulted in failed oocyte retrieval or hyperstimulation cycles, which result in patient disappointment or significant patient morbidity. Therefore creating novel methods to improve the outcomes of artificial reproductive technologies should be important and in urgency. The integration of artificial intelligence (AI) and clinical medicine is definitely a promising trend. It can utilize bioinformatics and computerized technology to incorporate and analyze huge amount of complicated clinical data, including personal history, laboratory examination, imaging profiles, and even big data such as genomics, metabolomics, microbiota…etc. With the application of deep learning and machine learning technologies, a prediction model for the design and prognosis of IVF treatment can be established to achieve personalized precision medicine. Besides, so far there was no reliable non-invasive biomarker to predict the developmental potential of human embryos and therefore the success rate of embryo transfer is limited. Thus innovating biomarkers for embryo viability are necessary for clinical care and definitely have a huge commercialized potential. Therefore the aim of our proposal is to develop an interdisciplinary approach to overcome the current limitation of reproductive technologies. It will focus on four core objectives: (1) Improvement in oocyte fertilization rates by optimizing the selection of gametes; (2) Creation of an automatized embryo morphology assessment system to enhance the selection of the optimal embryo; (3) Identification of biomarkers that predict embryo implantation potential, and integrating this data with morphological analyses in order to improve clinical outcomes; (4) Creation of a predictive model for treatment responses in IVF, for patient-specific treatment. The combination of artificial intelligence and big data analysis with reproductive medicine is not only crucial and urgent, but an inevitable step in the progress of clinical and medical research.