Evaluating the integration of two independently-developed artificial intelligence tools for predicting outcomes in ovarian stimulation

Objective: To evaluate the integration of two independently-developed artificial intelligence (AI) tools: (1) automated follicle measurements from MyCycleClarity, and (2) predictions of number of eggs retrieved using Alife Health’s Stim AssistTM.

Materials and Methods: MyCycleClarity uses AI to automatically count and measure follicles from 3D ultrasound images, and was previously trained on 91,782 follicles in 19,776 ovaries. The Alife Stim AssistTM Trigger Tool uses a linear regression model to predict the number of eggs retrieved based on a patient’s individual follicle sizes and estradiol level on the day of trigger, and was previously trained on 26,179 cycles. In the current study, electronic medical record data from 553 patients from one US clinic was collected. Data from this clinic contained both manually counted human follicle measurements as well as AI follicle measurements from MyCycleClarity. On the day of trigger, there were 82 cycles with human follicle measurements and 186 cycles with AI follicle measurements, and 25 cycles with both human and AI follicle measurements. To assess the integration of these two machine learning tools, we evaluated the accuracy of Stim AssistTM using human follicle measurements compared to using AI follicle measurements.

Results: The linear regression model coefficients from the Stim AssistTM Trigger Tool showed that follicles size 7-25mm were significantly associated with the number of eggs retrieved (p < 0.05). Follicles size 14-17mm measured at trigger had the strongest association with egg outcomes. Across all patients at the test clinic on the day of trigger, MyCycleClarity counted more small follicles (<10 mm) compared to human measurements (9.5 ± 9.4 vs 0.8 ± 0.9); however, it counted a similar number of large follicles (>10 mm) (14.1 ± 10.5 vs 11.7 ± 7.7). On the set of 25 cycles with both AI and human follicle measurements, the Stim AssistTM Trigger Tool had a mean absolute prediction error of 3.30 eggs using the AI follicle measurements and 3.84 eggs using the human follicle measurements.

Conclusions: In an analysis of patients with both AI-counted follicles from MyCycleClarity and human-counted follicles, the Alife’s Stim AssistTM tool had slightly more accurate predictions using AI counted follicles compared to human measurements. This is likely because MyCycleClarity more thoroughly counted follicles, especially the small ones <10mm, on the day of trigger.

Impact Statement: This preliminary study shows the synergy of two distinct machine learning tools and how their combined use in practice may increase the accuracy for predicting the number of eggs retrieved during ovarian stimulation.