We are excited to announce that our groundbreaking research papers were presented at the European Society of Human Reproduction and Embryology (ESHRE) 2024 conference in Amsterdam. This prestigious event allowed us to share our innovative studies and demonstrate the transformative potential of artificial intelligence (AI) in reproductive medicine.

Our research presentations included:

1. “Prospective Validation of the EMBRYOAID Software for AI-Based Evaluation of Embryos on the 5th Day After Fertilization During the In Vitro Procedure – Multi-Centre Study”
Description: This poster presents a study validating the effectiveness of the EMBRYOAID software, which uses AI for the assessment of blastocysts on the 5th day post-fertilization. Conducted across multiple international centers since November 2023, the study compares AI-based evaluations with traditional assessments by experienced embryologists. Preliminary results indicate that EMBRYOAID can achieve comparable pregnancy rates and high implantation rates, suggesting that AI-based embryo evaluation can be as effective as standard clinical practices. The study also observed a faster time to pregnancy when using AI-supported assessment, highlighting its potential benefits in IVF procedures.

2. “A Machine Learning Model for Optimal Trigger Day Recommendation”
Description: This poster presents a study on using AI to optimize the timing of trigger injections in IVF cycles. By analyzing data from 1085 IVF cycles, the model predicts the number of MII oocytes retrieved based on the day the trigger is applied. The study shows that using the AI model’s recommendations can improve the number of MII oocytes retrieved, potentially enhancing IVF success rates.

3. “Fully Automated Follicle Counting Matches Human Accuracy Levels in Predicting Stimulation Outcomes”
Description: This poster discusses the use of AI to automate the measurement of ovarian follicles, comparing the accuracy of AI-generated annotations with manual physician reports in predicting the number of retrieved and mature oocytes. The study involved 589 IVF cycles across five centers in Poland. The AI system, FOLLISCAN, demonstrated similar predictive accuracy to manual assessments, suggesting that AI can provide a reliable and efficient alternative for follicle monitoring during IVF stimulation, potentially reducing inter-observer variability and saving time for clinicians.

We are incredibly proud of our team’s accomplishments and grateful for the opportunity to share our work with the global reproductive medicine community at ESHRE 2024. We remain committed to advancing the use of AI in fertility treatments and are excited about the potential impact our work could have on improving patient outcomes. We look forward to continuing our research and sharing our findings at future events.