Artificial intelligence in the service of intrauterine insemination and timed intercourse in spontaneous cycles

Objective To develop a machine learning model designed to predict the time of ovulation and optimal fertilization window for performing intrauterine insemination or timed intercourse (TI) in natural cycles. Design A retrospective cohort study. Setting A large in vitro fertilization unit. Patient(s) Patients who underwent 2,467 natural cycle–frozen embryo transfer cycles between 2018 and 2022. Intervention(s) None. Main Outcome Measure(s) Prediction accuracy of the optimal day for performing insemination or TI. Result(s) The data set was split into a training set including 1,864 cycles and 2 test sets. In the test sets, ovulation was determined according to either expert opinion, with 2 independent fertility experts determining ovulation day (“expert”) (496 cycles), or according to the disappearance of the leading follicle between 2 consecutive days' ultrasound examinations (“certain ovulation”) (107 cycles). Two algorithms were trained: an NGBoost machine learning model estimating the probability of ovulation occurring on each cycle day and a treatment management algorithm using the learning model to determine an optimal insemination day or whether another blood test should be performed. The estradiol progesterone and luteinizing hormone levels on the last test performed were the most influential features used by the model. The mean numbers of tests were 2.78 and 2.85 for the “certain ovulation” and “expert” test sets, respectively. In the “expert” set, the algorithm correctly predicted ovulation and suggested day 1 or 2 for performing insemination in 92.9% of the cases. In 2.9%, the algorithm predicted a “miss,” meaning that the last test day was already ovulation day or beyond, suggesting avoiding performing insemination. In 4.2%, the algorithm predicted an “error,” suggesting performing insemination when in fact it would have been performed on a nonoptimal day (0 or −3). The “certain ovulation” set had similar results. Conclusion(s) To our knowledge, this is the first study to implement a machine learning model, on the basis of the blood tests only, for scheduling insemination or TI with high accuracy, attributed to the capability of the algorithm to integrate multiple factors and not rely solely on the luteinizing hormone surge. Introducing the capabilities of the model may improve the accuracy and efficiency of ovulation prediction and increase the chance of conception. Clinical Trial Registration Number HMC-0008-21. To develop a machine learning model designed to predict the time of ovulation and optimal fertilization window for performing intrauterine insemination or timed intercourse (TI) in natural cycles. A retrospective cohort study. A large in vitro fertilization unit. Patients who underwent 2,467 natural cycle–frozen embryo transfer cycles between 2018 and 2022. None. Prediction accuracy of the optimal day for performing insemination or TI. The data set was split into a training set including 1,864 cycles and 2 test sets. In the test sets, ovulation was determined according to either expert opinion, with 2 independent fertility experts determining ovulation day (“expert”) (496 cycles), or according to the disappearance of the leading follicle between 2 consecutive days' ultrasound examinations (“certain ovulation”) (107 cycles). Two algorithms were trained: an NGBoost machine learning model estimating the probability of ovulation occurring on each cycle day and a treatment management algorithm using the learning model to determine an optimal insemination day or whether another blood test should be performed. The estradiol progesterone and luteinizing hormone levels on the last test performed were the most influential features used by the model. The mean numbers of tests were 2.78 and 2.85 for the “certain ovulation” and “expert” test sets, respectively. In the “expert” set, the algorithm correctly predicted ovulation and suggested day 1 or 2 for performing insemination in 92.9% of the cases. In 2.9%, the algorithm predicted a “miss,” meaning that the last test day was already ovulation day or beyond, suggesting avoiding performing insemination. In 4.2%, the algorithm predicted an “error,” suggesting performing insemination when in fact it would have been performed on a nonoptimal day (0 or −3). The “certain ovulation” set had similar results. To our knowledge, this is the first study to implement a machine learning model, on the basis of the blood tests only, for scheduling insemination or TI with high accuracy, attributed to the capability of the algorithm to integrate multiple factors and not rely solely on the luteinizing hormone surge. Introducing the capabilities of the model may improve the accuracy and efficiency of ovulation prediction and increase the chance of conception.