Embryoxite develops an AI-powered platform that analyzes embryo metabolomic data, genetic profiles, and morphokinetic imaging to assess implantation potential in pre-implantation embryos. This technology addresses the high failure rate of embryo implantation in IVF procedures by providing objective, data-driven insights that enhance selection accuracy and improve pregnancy outcomes.
Funding
Funding not disclosed
Founders
Product
Problem
In vitro fertilization (IVF) success rates are limited by the subjective and inaccurate methods currently used to assess embryo viability, leading to a high failure rate of embryo implantation. Existing selection techniques lack comprehensive data analysis, resulting in approximately 70% of embryos failing to implant.
Solution
Embryoxite offers an AI-powered platform that analyzes metabolomic data, genetic profiles, and morphokinetic imaging to provide an objective assessment of pre-implantation embryo potential. The platform integrates clinical data, time-lapse images, and genomic information to optimize decision-making in assisted reproductive technology (ART). By examining metabolites exchanged between embryos and their culture medium, the technology non-invasively identifies molecular profiles related to implantation potential. The AI neural network generates an implantation prediction score, increasing the probability of a successful outcome.
Target Audience
The primary target audience includes IVF clinics and embryologists seeking to improve embryo selection accuracy and increase pregnancy success rates for patients undergoing assisted reproductive treatments.
Features
- AI-driven analysis of embryo metabolomic data, genetic profiles, and morphokinetic imaging
- Non-invasive metabolomic analysis to identify molecular profiles related to embryo implantation
- Integration of clinical data, time-lapse images, and genomic information sets
- AI neural network that provides an implantation prediction score
- Predictive scores for each stage of ART to increase precision levels