P-623 Using machine learning to determine follicle sizes on the day of trigger most likely to yield oocytes
Authors: Hanassab S , Abbara A , Alhamwi T , Comninos A , Salim R , Trew G , Nelson S , Kelsey T , Heinis T , Dhillo W 
Published: June 2023

Abstract

Study question
Which follicle sizes on the day of trigger (DoT) are most likely to yield oocytes after different IVF treatment protocols and trigger types?

Summary answer
Follicles sized 11-19mm on DoT are most likely to yield oocytes in both 'long' and 'short' protocols after using either hCG or GnRH agonist triggers.

What is known already
On the DoT, both follicles that are too small, or too large, are less likely to yield oocytes, but the precise range of follicle sizes that are most contributory to oocyte yield remains uncertain. Knowledge of this optimal follicle size range can aid in selecting the DoT and in quantifying the efficacy of the trigger by benchmarking the expected number of oocytes to be retrieved. Machine learning can aid in the analysis of large complex datasets and thus could be used to determine the follicle sizes on the DoT that are most predictive of the number of oocytes retrieved.

Study design, size, duration
We applied machine learning techniques to data from 8030 patients aged under 35 years who underwent autologous fresh IVF and ICSI cycles between 2011-2021 in a single IVF clinic. The DoT was determined by 2-3 leading follicles reaching ≥ 18mm in size. Follicle sizes from ultrasound scans performed on the DoT (n = 3056), a day prior to DoT (n = 2839), or two days prior to DoT (n = 2135), were evaluated in relation to the number of oocytes retrieved.

Participants/materials, setting, methods
A two-stage random forest pipeline was developed, with the number of follicles of a certain size on DoT as input, and the number of oocytes retrieved as output. First, a variable preselection model to determine the most contributory follicle sizes. Second, a model to identify the optimal range of follicle sizes to yield oocytes. Both models were trained and cross-validated with fixed hyperparameters. The pipeline was run for each protocol and trigger type independently.

Main results and the role of chance
The machine learning pipeline identified follicles sized 11-19mm on the DoT as most contributory in IVF/ICSI cycles when using an hCG trigger. After a GnRH agonist trigger, follicles sized 10-19mm were most predictive of the number of oocytes retrieved. To mitigate the role of chance, the statistical methods were further validated by utilizing scans prior to the DoT to rerun the pipelines, as well as a comparison against the true number of retrieved oocytes with linear regression. In ‘short’ protocol cycles triggered with hCG (n = 1581), follicles sized 11-19mm on the DoT were more closely associated with the number of oocytes retrieved (r2=0.58) than either smaller (r2=0.031), or larger (r2=0.051), follicle size ranges (p &lt; 0.0001). The most predictive follicles sizes on the day prior to DoT were 10-18 mm (n = 1421), and 6-17 mm for two days prior to the DoT (n = 1103), consistent with expected median follicle growth rates of 1-2 mm per day. Using fivefold cross-validation, the mean absolute error was 3.47 oocytes for hCG-triggered 'short' protocol patients. Similarly, significant trends were seen across all protocols and trigger types.

Limitations, reasons for caution
This was a single-center retrospective study and thus the analysis would benefit from further validation by extension to multiple centers using varying clinical practices to ensure model generalizability.

Wider implications of the findings
This data-driven target could enable greater personalization of treatment by guiding selection of the DoT to optimize oocyte yield. Prospective studies to assess whether this proposed target for follicle size range is preferable to standard methods based on lead follicle size are needed to confirm the implication of this data.

Trial registration number
not applicable