Table 3.

Trigger day assessment studies using artificial intelligence

Study	Aims of study	Outcomes of interest	Dataset	AI methods	Results
Abbara et al. (2018) 25	Follicle sizes on the day of trigger most likely to yield mature oocytes.	Optimal follicle sizes on TD.	Retrospective dataset with 499 patients. GnRH-ant protocol with hCG (n = 161); GnRH-a (n = 165); KP-54 (n = 173) triggers. Input variables: individual follicle diameters (in mm) from ultrasound scan on TD.	Random forest with 5-fold CV.	Follicles of diameter 12–19 mm were most contributory to the models following all three trigger types.
Abbara et al. (2020)19	Examine the relationship between endocrine changes following the use of different oocyte maturation triggers. Assess the relative importance of endocrine predictors when predicting mature oocyte yield.	(1) Accuracy in predicting the number of mature oocytes retrieved. (2) Relative importance of LH/hCG as an input variable.	Retrospective dataset with 499 patients. GnRH-ant protocol with hCG (n = 161); GnRH-a (n = 165); KP-54 (n = 173) triggers. Input variables: baseline endocrine characteristics, number of follicles sized 12–19 mm.	Performance comparison between a random forest with 5-fold CV and an ANN model.	Random forest had 88% accuracy within a tolerance level of 3 mature oocytes. The performance dropped to 83% when data on baseline LH/hCG levels were excluded. -The ANN had 57% accuracy.
Robertson et al. (2021)32	Finding the optimal tracking strategy for OS to minimize face-to-face interactions.	Earliest day during OS which can predict both the optimal TD and risk of OHSS accurately.	Retrospective dataset with 2128 cycles of 1731 women in a single center. 88.8% were GnRH-ant (fixed) cycles. An hCG trigger was used. Input variables: age, AFC, follicle count by size on each scan.	Random forest regressor for TD. Binary random forest classifier for OHSS prediction.	Day-5 was the earliest cycle day for predicting both outcomes accurately. The day-5 model had a MSE of 2.16 ± 0.12 for TD and AUC of 0.91 ± 0.01 for OHSS classification.
Hariton et al. (2021)26	Optimize TD timing to maximize 2PN and usable blastocyst yield.	Average improvement of 2PNs (primary outcome), and usable blastocysts vs. a clinician’s decision.	Retrospective dataset with 7,866 ICSI cycles. 1,967 cycles (25%) held out for independent testing. GnRH-ant, LD21, Lupron stop, flare, or mini-IVF (natural cycle) protocols were used. Input variables: age, BMI, number of follicles of {6-10, 11-15, 16-20, 21-25} mm, E2 level, protocol type, TD.	Light Gradient Boosting Machine with bagging.	Average improvement: 3.015 more 2PNs (95% CI 2.626, 3.371) and 1.515 more usable blastocysts (95% CI 1.134,1.871). Given physician agreement with the model (52.57% for 2PNs, 61.89% for blastocysts): 1.430 more 2PNs, and 0.577 more usable blastocysts. Follicle sizes 16-20mm were most contributory to the model performance.
Letterie et al. (2022)31	Workflow optimization of OS: (1) single ‘best day’ for monitoring during OS; (2) predict optimal TD; (3) predict total number of retrieved oocytes.	Acc., TPR, and PPV of total number of retrieved oocytes and mature oocytes stratified into: 0–10, and >10. MAE of determining the aims of (1) and (3).	Retrospective data-set with 1591 IVF cycles from a single center. 318 cycles (20%) held out for independent testing. An hCG or Lupron trigger was used. Pre-cycle selected input variables: age, AMH. ‘Best day’ selected input variables: E2 levels, follicle counts and sizes, day of cycle during OS, dose of FSH during OS.	Stacking ensemble model comprising: linear regression, random forest, extra trees regression, k-nearest neighbors, XGBoost.	(1) ‘Best day’ prediction: MAE 1.355. (2) Variance of 0-3 days for trigger choice showed “little impact” to oocytes retrieved. (3) Total number of oocytes: MAE 3.517. Total retrieved oocytes: Acc. 0.77; 0-10 oocytes (TPR 0.80; PPV 0.79); >10 oocytes (TPR 0.74; PPV 0.74). Total retrieved mature oocytes: Acc. 0.89; 0-10 oocytes (0.91; 0.89); >10 oocytes (0.86; 0.88). Total number of oocytes: MAE 3.517.
Fanton et al. (2022)27	Optimize TD timing to maximize MIIs, 2PNs, and blastocyst yield.	Average number of MIIs (primary outcome), 2PNs, and usable blastocysts.	Retrospective dataset with 30,278 cycles from 3 centers (2555, 3051, 14,672 cycles). 20% held out for independent testing. No available protocols were excluded. Pre-cycle input variables: age, BMI, AFC, previous IVF cycles, AMH, E2 level, cycle length (in days). Mid-cycle input variables: number of follicle scans during OS, E2 levels during OS, number of follicles of size {<11, 11–13, 14–15, 16–17, 18–19, >19} mm on TD.	Multivariable linear regression	Patients with early triggers had 2.3 fewer MIIs, 1.8 fewer 2PNs, and 1.0 fewer usable blastocysts when compared to propensity-matched on-time triggers. Patients with late triggers had 2.7 fewer MIIs, 2.0 fewer 2PNs, and 0.7 fewer usable blastocysts when compared to propensity-matched on-time triggers. Only follicle sizes and E2 were used in the final model.

Studies that use machine learning (ML) techniques to optimize trigger day timing during OS. IVF in vitro fertilization, CDSS clinical decision support system, OS ovarian stimulation, TD trigger day, Acc. accuracy, TPR true positive rate (sensitivity), PPV positive predicted value, FSH follicle-stimulating hormone, GnRH-a gonadotropin-releasing hormone agonist, GnRH-ant gonadotropin-releasing hormone antagonist, hCG human chorionic gonadotropin, KP-54 kisspeptin-54, E2 estradiol, P4 progesterone, AFC antral follicle count, AMH anti-Müllerian hormone, LH luteinizing hormone, LD21 long day 21, ANN artificial neural network, MAE mean absolute error, MSE mean squared error, AUC area under curve, CV cross-validation, BMI body-mass index, IU international units, MIIs metaphase-II oocytes, 2PN two-pronuclear embryo, k-NN k-nearest neighbor, cLBR cumulative live birth rate.