Artificial intelligence in in vitro fertilization: a computer decision support system for day-to-day management of ovarian stimulation during in vitro fertilization

A decision support system offers a high degree of agreement with evidence-based clinical decisions and may have a role in decision making to optimize decisions during ovarian stimulation during in vitro fertilization.

VOLUME 114, ISSUE 5, P1026-1031


Gerard Letterie, M.D., Andrew Mac Donald, M.S.



To describe a computer algorithm designed for in vitro fertilization (IVF) management and to assess the algorithm’s accuracy in the day-to-day decision making during ovarian stimulation for IVF when compared to evidence-based decisions by the clinical team.


Descriptive and comparative study of new technology.


Private fertility practice.




Data were derived from monitoring during ovarian stimulation from IVF cycles. The database consisted of 2,603 cycles (1,853 autologous and 750 donor cycles) incorporating 7,376 visits for training. An additional 556 unique cycles were used for challenge and to calculate accuracy. There were 59,706 data points. Input variables included estradiol concentrations in picograms per milliliter; ultrasound measurements of follicle diameters in two dimensions in millimeters; cycle day during stimulation and dose of recombinant follicle-stimulating hormone during ovarian stimulation for IVF.

Main Outcome Measure(s)

Accuracy of the algorithm to predict four critical clinical decisions during ovarian stimulation for IVF: [1] stop stimulation or continue stimulation. If the decision was to stop, then the next automated decision was to [2] trigger or cancel. If the decision was to return, then the next key decisions were [3] number of days to follow-up and [4] whether any dosage adjustment was needed.


Algorithm accuracies for these four decisions are as follows: continue or stop treatment: 0.92; trigger and schedule oocyte retrieval or cancel cycle: 0.96; dose of medication adjustment: 0.82; and number of days to follow-up: 0.87. These accuracies are for first iteration of the algorithm.


We describe a first iteration of a predictive analytic algorithm that is highly accurate and in agreement with evidence-based decisions by expert teams during ovarian stimulation during IVF. These tools offer a potential platform to optimize clinical decision making during IVF.

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Go to the profile of RUIXUE CHEN
over 1 year ago

I read with interest the paper regarding a computer decision support system for day-to-day management of ovarian stimulation. However, I have some questions regarding this paper. 

In the ‘MATERIALS AND METHODS’ section, authors fisrtly mentioned that  the decision support system (DSS) performance was compared against the evidence-based medicine decisions by 12 clinicians. However, when be mentioned later in this section, authors said that the clinical team making the evidence-based medical decisions in this study are 10 board certified reproductive endocrinologists. How many clinicians make the evidence-based medical decisions? 

In this paper, authors summarized the clinical profiles of the included population, including female age, BMI, AFC and AMH. These data points were not included in the decision process. But age, BMI, AFC and AMH are associated with the ovarian response ability. Will the performance of the DSS be improved If these factors are included?