InfertilityAI-TOPNCT07515118
AI-TOP Study Artificial Intelligence for Trigger Optimization.
A study testing STIMAI®. and Routine clinical management in people with Infertility and Ovarian Stimulation, run by Fundacion Dexeus at 5 sites in Spain.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 to 42 years, women only
- Conditions the study is for
- Infertility, Ovarian Stimulation, Artificial Intelligence
- What is being tested?
- STIMAI®. and Routine clinical management
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 644 participants
- Where
- 5 sitesSpain
- Study dates
- 8 Apr 2026to Sep 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 42 years
- Women only
- No healthy volunteers
- Adult
Who may be able to join
- Undergoing COS for IVF with autologous oocytes, oocyte donation and elective fertility preservation with all monitoring USS (ultrasound scan) conducted at our centers.
Who cannot join
- Medically indicated fertility preservation
- Inability to attend clinic visits for monitoring.
Trial locations and contacts
5 sites in 1 country. Contact the study team or a site near you to ask about taking part.
5 sites in 1 country
Spain5 sites, 5 recruiting
- Recruiting
Hospital Universitario Quiron Dexeus
Barcelona, 08028
Nikolaos P Polyzos, MD PhD, 0034932274700, nikpol@dexeus.com
Ignacio Rodriguez, MSc, 0034932274700, nacrod@dexeus.com
- Not yet recruiting
Dexeus Mujer Reus
Reus, Tarragona, 43202
Josep Gonzalo, MD, JOSGON@dexeus.com
Josep Gonzalo, MD
- Not yet recruiting
Dexeus Mujer Sabadell
Sabadell, Barcelona
Ainhoa Coco, MD, 0034932274700, aincoc@dexeus.com
Ainhoa Coco, MD
- Not yet recruiting
Dexeus Mujer Sant Cugat
Sant Cugat del Vallès, Barcelona, 08195
Ainhoa Coco, MD, 0034932274700, aincoc@dexeus.com
Ainhoa Coco, MD
- Not yet recruiting
Dexeus Mujer Tarragona
Tarragona, 43206
Josep Gonzalo, MD, josgon@dexeus.com
Josep Gonzalo, MD
About this study
Official title: An Artificial Intelligence Based Approach for Selecting the Optimal Day for Triggering.
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.
Read the detailed description
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.
- Conditions
- Infertility, Ovarian Stimulation, Artificial Intelligence
- Being tested
- STIMAI®., Routine clinical management
- Medical subject headings
- Infertility
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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.
Routine clinical managementOther
As soon as 2-3 follicles of 17 mm are detected, the physician will determine the timing of ovulation triggering based on clinical judgment.
| Study group | Role | What they receive |
|---|---|---|
| AI Algorithm | Experimental | Trigger decisions trigger will be made by the physician assisted by AI guidance. Receives: Device: STIMAI®. |
| Routine clinical management | Active comparator | Following routine clinical management with ovulation trigger decisions made by the physician alone, Receives: Other: Routine clinical management |
What is being measured
The primary outcome is the main question the study is designed to answer.
MII oocytes
Number of MII oocytes retrieved at oocyte pickup.
Measured: Day of pickup approx. 34-36 hours after ovulation trigger.
Secondary outcomes (7)
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.
Measured: Assessed at the end of the stimulation cycle, once the retrieval schedule is completed. approx. 34-36 hours after ovulation trigger.
Number of COCs
The total number of oocytes surrounded by cumulus cells retrieved during the oocyte pick-up procedure, reflecting ovarian response to stimulation.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Detected between the trigger administration and the planned oocyte retrieval. approx. 34-36 hours after ovulation trigger.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Apr 2026
- Study start8 Apr 2026
- Primary completionApr 2027 (estimated)
- Study completionSep 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for InfertilityStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 644 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Fundacion Dexeus
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07515118
- Sponsor's protocol ID
- FSD-AIG-2025-20
- Acronym
- AI-TOP
- Status last verified
- Apr 2026
- First submitted
- 24 Mar 2026
- Last updated
- 22 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 32819677Blockeel 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.
PubMed 22296973Canon 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.
PubMed 39164339Chow 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.
PubMed 35118395Dimitriadis 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.
PubMed 35027326Ferrand 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.
PubMed 37581894Hariton 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.
PubMed 34256948Letterie 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.
PubMed 34865998
Study at a glance
From the registry record, last updated by the sponsor on 22 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- An Artificial Intelligence Based Approach for Selecting the Optimal Day for Triggering.
- ClinicalTrials.gov ID
- NCT07515118
- Acronym / sponsor ID
- AI-TOP / FSD-AIG-2025-20
- Conditions
- Infertility, Ovarian Stimulation, Artificial Intelligence
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 644 participants (estimated)
- Lead sponsor
- Fundacion DexeusUniversity, hospital or other
- Study start
- 8 Apr 2026 (actual)
- Primary completion
- Apr 2027 (estimated)
- Study completion
- Sep 2027 (estimated)
- First posted
- 7 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI Algorithm | Experimental | Device: STIMAI®.Trigger decisions trigger will be made by the physician assisted by AI guidance. |
| Routine clinical management | Active comparator | Other: Routine clinical managementFollowing routine clinical management with ovulation trigger decisions made by the physician alone, |
Who may qualify
- Ages
- 18 to 42 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult
Key inclusion criteria (1 listed)
- Undergoing COS for IVF with autologous oocytes, oocyte donation and elective fertility preservation with all monitoring USS (ultrasound scan) conducted at our centers.
Key exclusion criteria (2 listed)
- Medically indicated fertility preservation
- Inability to attend clinic visits for monitoring.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| STIMAI®.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. | Device | AI Algorithm |
| Routine clinical managementAs soon as 2-3 follicles of 17 mm are detected, the physician will determine the timing of ovulation triggering based on clinical judgment. | Other | Routine clinical management |
Primary outcome
MII oocytes
Time frame: Day of pickup approx. 34-36 hours after ovulation trigger.
Number of MII oocytes retrieved at oocyte pickup.
7 secondary outcomes
Distribution of retrieval procedures during the week.
Time frame: Assessed at the end of the stimulation cycle, once the retrieval schedule is completed. approx. 34-36 hours after ovulation trigger.
Number of COCs
Time frame: Measured on the day of oocyte retrieval. approx. 34-36 hours after ovulation trigger.
Length of stimulation (days)
Time frame: 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)
Time frame: 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)
Time frame: AFC measured at baseline; COCs counted on the day of retrieval approx. 34-36 hours after ovulation trigger.
Number of visits
Time frame: Counted from the start of stimulation until the trigger day. up to 8-12 days
Spontaneous ovulation
Time frame: Detected between the trigger administration and the planned oocyte retrieval. approx. 34-36 hours after ovulation trigger.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Apr 2026.
- Background: PubMed 32819677Babayev 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.
- Background: PubMed 22296973Blockeel 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.
- Background: PubMed 39164339Canon 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.
- Background: PubMed 35118395Chow 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.
- Background: PubMed 35027326Dimitriadis 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.
- Background: PubMed 37581894Ferrand 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.
- Background: PubMed 34256948Hariton 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.
- Background: PubMed 34865998Letterie 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.
- Related InfoLink provided by the sponsor.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
5 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing STIMAI®. and Routine clinical management in people with Infertility and Ovarian Stimulation, run by Fundacion Dexeus at 5 sites in Spain.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 22 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in Spain. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
MII oocytes (time frame: Day of pickup approx. 34-36 hours after ovulation trigger.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07515118, which also lists 8 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Apr 2026.