ISSN: 2474-9230
Authors: Kochar Kaur K*, Allahbadia G and Singh M
Artificial Intelligence (AI) is an expanding field to optimize results in medicine with special emphasis in radiology with optimization of images. Similarly in reproductive medicine it has taken over in the last few years. Ideally embryo assessment as well as selection decides the ultimate fate of the in vitro fertilization (IVF) event. The objective is to pick the best out of the large cohort of the oocytes that managed to get fertilized. Maximum of which would work out to be nonviable secondary to aberrant generation or chromosomal aberrations. It has usually been recognized that despite embryo selection depending on the compounded results of morphology, morph kinetics characteristics, time lapse microscopic (TLM) photography, or embryo biopsy with Preimplantation genetic testing for aneuploidy (PGT-A), implantation rates in the human has not been easy to anticipate. Hence in our efforts to escalate embryo assessment as well as selection along with escalation of live birth rate (LBR) would need innovative methods. Currently AI-dependent techniques have come out having standardized, objective along with great effectiveness in embryo assessment. Thus a systematic review was carried out utilizing the search engine PubMed, Google scholar; web of science; embase; Cochrane review library from 2000 to 2020 till date. The MeSH terms utilized were AI; ART; IVF; ICSI; PGT; TLM; BL(blastocyst) morph kinetics; morphology; annotation studies; sperm parameters; ploidy evaluation by AI; utilization of images static vs video for generation of standards; embryo grading; infertility evaluation. We found a total of 316 articles out of which we selected 56 articles for this review. No meta-analysis was carried out. Further AI can be utilized for other aspects of IVF like gonadotropin stimulation individualization protocols as well as assessment of Reproductive potential. Moreover AI possesses capacity to evaluate Big Data. Moreover our main objective would be the application of AI tools for evaluation of embryological, genetic as well as clinical data to give patients specific treatment.
Keywords: AI; Embryo Assessment; Embryo Selection; Ploidy Evaluation; Machine Learning
