An artificial intelligence model to predict upcoming patient oocyte collections to help optimize efficiency and safety in the embryology laboratory.

Study question

Can artificial intelligence (AI) be used to forecast the number of oocyte collections that will occur at a clinic multiple days in advance?

Summary answer

An AI model can accurately predict how many oocyte collections will occur across a clinic up to seven days in advance.

What is known already

There exists significant variability in the duration of ovarian stimulation for patients going through IVF, which can often lead to peaks and valleys of workload for the embryology laboratory. On a given day, a laboratory being under or over resourced can lead to poor utilization of resources or overtime hours. This is especially problematic as the demand for IVF steadily increases, particularly for large clinics doing thousands of oocyte collections (OC) per year. A tool to help predict clinic OC workload many days in advance could assist with laboratory staffing and operational efficiency through load leveling.

Study design, size, duration

Historical, de-identified electronic medical record data from 6140 patients undergoing ovarian stimulation at a large IVF clinic in the U.S. between January 2021 to January 2023 was collected. Patients who started treatment between January 2021 to December 2021 were used for training the AI model (N = 3126). The remaining patients were used for evaluating the model (N = 3014).

Participants/materials, setting, methods

A gradient boosting classifier (Catboost) was trained to predict a patient’s remaining cycle duration using E2, follicle sizes, and day of medication. This model outputs a probability that a patient’s OC will land on any specific future day. To predict the number of OC across a clinic on a future day, the model predicts the probability that each patient will have their retrieval on that day, and calculates the convolution to estimate total retrievals.

Main results and the role of chance

The clinic had an average of 7.9 OC per day. The OC projection accuracy was assessed by iterating through each date in the test dataset and predicting the number of OC each day over the next 7 days, for all patients undergoing stimulation who had not yet been triggered. In this analysis, the model predicted the total number of OC on each day with a mean absolute error of 1.56 retrievals and an R2 of 0.72. The model was able to identify days where workload exceeded 11 OC (80th percentile) with 86% accuracy and 58% precision. The model was able to identify days where workload was below 5 OC (20th percentile) with 95% accuracy and 91% precision.

Limitations, reasons for caution

This model was trained at a single clinic and evaluated retrospectively. Future work should assess model performance at clinics with varying patient throughput, and evaluate prospectively how this tool could assist with load leveling of OC volume.

Wider implications of the findings

An AI model can predict when patients will be triggered based on their medication and response to treatment. This tool can be used to forecast the total number of OC at a clinic up to 7 days in advance, which could help improve efficiency for embryology and clinical staff.