Intrauterine insemination timing models-LH can only take you so far.

RESEARCH QUESTION: Can an optimal LH threshold algorithm accurately predict timing of ovulation for natural cycle-intrauterine insemination (NC-IUI)? DESIGN: A retrospective cohort study (2018-2022) including 2467 natural cycles. Ovulation timing for these cycles was determined using a previously developed AI model. Two LH thresholds, low and high, were determined in the LH algorithm. Being below the low threshold meant that ovulation is likely to occur in ≥ 4 days, suggesting another daily blood test. Between the two thresholds meant that ovulation was likely in 2-3 days, suggesting IUI the next day. Above the high threshold meant that ovulation will likely occur tomorrow, suggesting performing IUI on the same day. RESULTS: The optimal LH model with a high threshold of 40 mIU/ml and a low threshold of 11 mIU/ml succeeded in correctly predicting timing for IUI (day - 1, - 2 relative to ovulation) in 75.4% (95%CI 75.3-75.4). In 23.1% (95%CI 23.0-23.2), the algorithm predicted "error," suggesting performing insemination when in fact it would have been performed on a non-optimal day (0 or - 3). A previously described 3-hormone-based (LH, estradiol, progesterone) AI model performed significantly better in all parameters (93.6% success rate, 4.3 "error" rate). CONCLUSIONS: An LH threshold model, representing common practice, evaluating all possible high and low LH threshold combinations, was successful in accurately scheduling timing for IUI in only 75% of cases. Integrating all three hormones as performed in the AI model may have an advantage in accurately predicting the optimal time for IUI, over the use of LH only.

An artificial intelligence-based approach for selecting the optimal day for triggering in antagonist protocol cycles.

RESEARCH QUESTION: Can a machine-learning model suggest an optimal trigger day (or days), analysing three consecutive days, to maximize the number of total and mature (metaphase II [MII]) oocytes retrieved during an antagonist protocol cycle? DESIGN: This retrospective cohort study included 9622 antagonist cycles between 2018 and 2022. The dataset was divided into training, validation and test sets. An XGBoost machine-learning algorithm, based on the cycles' data, suggested optimal trigger days for maximizing the number of MII oocytes retrieved by considering the MII predictions, prediction errors and outlier detection results. Evaluation of the algorithm was conducted using a test dataset including three quality groups: 'Freeze-all oocytes', 'Fertilize-all' and 'ICSI-only' cycles. The model suggested 1, 2 or 3 days as trigger options, depending on the difference in potential outcomes. The suggested days were compared with the actual trigger day chosen by the physician and were labelled 'concordant' or 'discordant' in terms of agreement. RESULTS: In the 'freeze-all' test-set, the concordant group showed an average increase of 4.8 oocytes and 3.4 MII oocytes. In the 'ICSI-only' test set there was an average increase of 3.8 MII oocytes and 1.1 embryos, and in the 'fertilize-all' test set an average increase of 3.6 oocytes and 0.9 embryos was observed (P < 0.001 for all parameters in all groups). CONCLUSIONS: Utilizing a machine-learning model for determining the optimal trigger days may improve antagonist protocol cycle outcomes across all age groups in freeze-all or fresh transfer cycles. Implementation of these models may more accurately predict the number of oocytes retrieved, thus optimizing physicians' decisions, balancing workloads and creating more standardized, yet patient-specific, protocols.

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.