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To evaluate, in a randomized controlled trial, whether AI-guided monitoring and ovulation triggering leads to clinical outcomes comparable to those achieved through physician-led decision-making in patients undergoing ovarian stimulation for IVF.
Assisted Reproductive Technology is undergoing a major transformation with the introduction of artificial intelligence (AI), which is reshaping how medical treatments are carried out. In IVF, one of the persistent challenges has been maximizing the number of oocytes retrieved while efficiently managing clinical workload-particularly by reducing weekend procedures-without compromising outcomes. Although a patient's response may vary between cycles, evidence shows that adjusting the trigger day by one day does not significantly affect clinical results, enabling more flexible scheduling.
AI enables a shift from standardized protocols to personalized treatments, improving clinical outcomes, streamlining processes and enhancing operational efficiency. Recent research shows that AI-based models can optimize ovarian stimulation, improve trigger-day selection, and increase the number of fertilized oocytes compared to decisions made solely by physicians. AI algorithms have also accurately predicted the number of oocytes retrieved, contributing to more effective protocols and higher live birth rates.
Beyond trigger timing, AI has been shown to improve workflow efficiency in IVF clinics by optimizing monitoring schedules and balancing clinical workload without negatively affecting cycle outcomes.
Based on this growing evidence, a randomized controlled trial was designed to compare clinical outcomes of controlled ovarian stimulation when trigger and retrieval decisions are made solely by the physician versus when the physician is assisted by AI guidance.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| AI Algorithm | Experimental | Trigger decisions trigger will be made by the physician assisted by AI guidance. |
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| Routine clinical management | Active Comparator | Following routine clinical management with ovulation trigger decisions made by the physician alone, |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| STIMAI®. | Device | The AI algorithm used in this study is STIMAI®. STIMAI® is an artificial intelligence-based software that assists clinicians by providing data-driven insights to optimize the fertility treatment process and support conception. The software is designed as a clinical decision support tool and does not replace the physician's judgment; final clinical decisions will remain under the responsibility of the treating physician The physician will consult the AI application, which predicts the number of MII oocytes for different trigger days. If the algorithm recommends triggering today or tomorrow, the physician will choose which option to follow. |
| Measure | Description | Time Frame |
|---|---|---|
| MII oocytes | Number of MII oocytes retrieved at oocyte pickup. | Day of pickup approx. 34-36 hours after ovulation trigger. |
| Measure | Description | Time Frame |
|---|---|---|
| Distribution of retrieval procedures during the week. | : The pattern or spread of oocyte retrieval procedures across the days of the week during an IVF cycle. | Assessed at the end of the stimulation cycle, once the retrieval schedule is completed. approx. 34-36 hours after ovulation trigger. |
| Number of COCs |
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Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Dexeus Mujer Sabadell | Not yet recruiting | Sabadell | Barcelona | Spain |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 32819677 | Background | Babayev E. Man versus machine in in vitro fertilization-can artificial intelligence replace physicians? Fertil Steril. 2020 Nov;114(5):963. doi: 10.1016/j.fertnstert.2020.07.042. Epub 2020 Aug 17. No abstract available. | |
| 22296973 | Background | Blockeel C, Engels S, De Vos M, Haentjens P, Polyzos NP, Stoop D, Camus M, Devroey P. Oestradiol valerate pretreatment in GnRH-antagonist cycles: a randomized controlled trial. Reprod Biomed Online. 2012 Mar;24(3):272-80. doi: 10.1016/j.rbmo.2011.11.012. Epub 2011 Nov 30. |
| Label | URL |
|---|---|
| Related Info | View source |
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| ID | Term |
|---|---|
| D007246 | Infertility |
| ID | Term |
|---|---|
| D000091662 | Genital Diseases |
| D000091642 | Urogenital Diseases |
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|
| Routine clinical management | Other | As soon as 2-3 follicles of 17 mm are detected, the physician will determine the timing of ovulation triggering based on clinical judgment. |
|
The total number of oocytes surrounded by cumulus cells retrieved during the oocyte pick-up procedure, reflecting ovarian response to stimulation. |
| Measured on the day of oocyte retrieval. approx. 34-36 hours after ovulation trigger. |
| Length of stimulation (days) | The total number of days a patient receives gonadotropins for controlled ovarian stimulation before triggering ovulation. | From the first day of stimulation until the day the ovulation trigger is administered. Up to 8-15 days |
| FORT (pre-ovulatory follicles on trigger day/AFC) | A measure of follicular responsiveness calculated as the number of pre-ovulatory follicles on trigger day divided by the baseline antral follicle count (AFC). It reflects the efficiency of stimulation in growing recruitable follicles. | AFC is measured at baseline (cycle day 2-3); pre-ovulatory follicles are counted on trigger day. Up to 8-15 days |
| FOI (N COCs/AFC) | The number of cumulus-oocyte complexes retrieved divided by the baseline antral follicle count (AFC), indicating how effectively baseline follicles resulted in retrieved oocytes. | AFC measured at baseline; COCs counted on the day of retrieval approx. 34-36 hours after ovulation trigger. |
| Number of visits | The total number of in-clinic monitoring visits required during controlled ovarian stimulation, including ultrasound assessments and blood tests. | Counted from the start of stimulation until the trigger day. up to 8-12 days |
| Spontaneous ovulation | Occurrence of unintended follicular rupture before oocyte retrieval, indicating that ovulation happened prior to the scheduled procedure despite monitoring. | Detected between the trigger administration and the planned oocyte retrieval. approx. 34-36 hours after ovulation trigger. |
| Dexeus Mujer Sant Cugat | Not yet recruiting | Sant Cugat del Vallès | Barcelona | 08195 | Spain |
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| Dexeus Mujer Reus | Not yet recruiting | Reus | Tarragona | 43202 | Spain |
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| Hospital Universitario Quiron Dexeus | Recruiting | Barcelona | 08028 | Spain |
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| Dexeus Mujer Tarragona | Not yet recruiting | Tarragona | 43206 | Spain |
|
| 39164339 | Background | Canon C, Leibner L, Fanton M, Chang Z, Suraj V, Lee JA, Loewke K, Hoffman D. Optimizing oocyte yield utilizing a machine learning model for dose and trigger decisions, a multi-center, prospective study. Sci Rep. 2024 Aug 20;14(1):18721. doi: 10.1038/s41598-024-69165-1. |
| 35118395 | Background | Chow DJX, Wijesinghe P, Dholakia K, Dunning KR. Does artificial intelligence have a role in the IVF clinic? Reprod Fertil. 2021 Aug 23;2(3):C29-C34. doi: 10.1530/RAF-21-0043. eCollection 2021 Jul. |
| 35027326 | Background | Dimitriadis I, Zaninovic N, Badiola AC, Bormann CL. Artificial intelligence in the embryology laboratory: a review. Reprod Biomed Online. 2022 Mar;44(3):435-448. doi: 10.1016/j.rbmo.2021.11.003. Epub 2021 Nov 12. |
| 37581894 | Background | Ferrand T, Boulant J, He C, Chambost J, Jacques C, Pena CA, Hickman C, Reignier A, Freour T. Predicting the number of oocytes retrieved from controlled ovarian hyperstimulation with machine learning. Hum Reprod. 2023 Oct 3;38(10):1918-1926. doi: 10.1093/humrep/dead163. |
| 34256948 | Background | Hariton E, Chi EA, Chi G, Morris JR, Braatz J, Rajpurkar P, Rosen M. A machine learning algorithm can optimize the day of trigger to improve in vitro fertilization outcomes. Fertil Steril. 2021 Nov;116(5):1227-1235. doi: 10.1016/j.fertnstert.2021.06.018. Epub 2021 Jul 10. |
| 34865998 | Background | Letterie G, MacDonald A, Shi Z. An artificial intelligence platform to optimize workflow during ovarian stimulation and IVF: process improvement and outcome-based predictions. Reprod Biomed Online. 2022 Feb;44(2):254-260. doi: 10.1016/j.rbmo.2021.10.006. Epub 2021 Oct 20. |