Underuse of Clinical Data Contributes to Missed Oocyte Potential in Stim Cycles
3.8 More MII Oocytes When Physicians Follow AI Guidance
A 2024 study published in Reproductive BioMedicine Online (RBMO) highlighted that IVF cycles aligned with AI-recommended trigger timing yielded, on average, 3.8 more mature (MII) oocytes and 1.1 more usable embryos compared to those where physicians diverged from the model's recommendation (p < 0.001 in all groups). According to the study, “Utilizing a machine-learning model for determining the optimal trigger days may improve antagonist protocol cycle outcomes across all age groups.”
In over 70% of discordant cases, physicians opted to trigger ovulation earlier than the algorithm advised. “In almost all cases, the physicians decided to trigger earlier than the model,” the study noted, citing differences in judgment, interpretation of hormone and ultrasound data, and the lack of standardized guidelines.
Doctors May Trigger Too Early—And It Could be Costing Embryos
The timing of ovulation triggers affects not only the number of mature eggs retrieved but also the smooth functioning of the clinic. Triggering too early can compromise follicle development, lowering embryo yield and freezing opportunities. As the RBMO study noted, “Across all test sets, the majority of discordant cycles involved the physician instigating an earlier trigger than the one proposed by the algorithm.”
This unpredictability also complicates scheduling. Without standardized timing, retrieval calendars become less efficient, creating staffing peaks and underutilized lab windows. “This may further improve the clinical outcome and workload balance, enabling better personnel and resource management,” the authors stated.
ESHRE Guidelines Reinforce Complexity of Trigger Decisions
The European Society of Human Reproduction and Embryology (ESHRE) offers no universal threshold for trigger decisions. Instead, clinicians are expected to juggle follicle size, estradiol levels, duration of stimulation, prior outcomes, OHSS risk, and operational constraints. This multifactorial decision-making process, often conducted under pressure and with incomplete data, increases the likelihood of inconsistency. The mental burden is substantial—and compounded in networks striving for standardization across multiple locations.
53,000-Cycle Algorithm Offers Predictive Insight Without Rigidity
To address this challenge, FertilAI developed a predictive algorithm trained on over 53,000 ovarian stimulation cycles from 11 IVF centers across North America, Europe, and Asia. Rather than enforcing rigid decisions, the tool presents clinicians with predicted oocyte yield for three potential trigger days—today, tomorrow, and two days later.
The model’s performance metrics include an R² of 0.81 for total oocytes and 0.72 for MII oocytes using same-day inputs. This level of predictive accuracy significantly outperforms clinician-only estimates and heuristic approaches, offering an objective basis to support—but not replace—clinical judgment.
Improves Oocyte Yield Without Replacing Physician Judgment
The implications for fertility networks are significant. Even small improvements in average outcomes—such as a modest increase in mature oocyte yield—can lead to measurable gains in cumulative pregnancy rates, embryo storage, and frozen transfer volume. These performance improvements can become a source of competitive differentiation, especially for groups aiming to optimize care at scale.
One solution designed to operationalize these insights leverages lifetimes of anonymized stimulation data to help secure success—both clinically and economically—without disrupting physician autonomy. It does so not by replacing the clinician’s role, but by streamlining complex decisions that were previously reliant on intuition and fragmented data.