NCT07515118

Brief 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.

Trial Health

77
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
644

participants targeted

Target at P75+ for not_applicable

Timeline
16mo left

Started Apr 2026

Geographic Reach
1 country

5 active sites

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress6%
Apr 2026Sep 2027

First Submitted

Initial submission to the registry

March 24, 2026

Completed
14 days until next milestone

First Posted

Study publicly available on registry

April 7, 2026

Completed
1 day until next milestone

Study Start

First participant enrolled

April 8, 2026

Completed
12 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

April 1, 2027

Expected
5 months until next milestone

Study Completion

Last participant's last visit for all outcomes

September 1, 2027

Last Updated

April 22, 2026

Status Verified

April 1, 2026

Enrollment Period

12 months

First QC Date

March 24, 2026

Last Update Submit

April 21, 2026

Conditions

InfertilityOvarian StimulationArtificial Intelligence

Outcome Measures

Primary Outcomes (1)

  • MII oocytes

    Number of MII oocytes retrieved at oocyte pickup.

    Day of pickup approx. 34-36 hours after ovulation trigger.

Secondary Outcomes (7)

  • Distribution of retrieval procedures during the week.

    Assessed at the end of the stimulation cycle, once the retrieval schedule is completed. approx. 34-36 hours after ovulation trigger.

  • Number of COCs

    Measured on the day of oocyte retrieval. approx. 34-36 hours after ovulation trigger.

  • Length of stimulation (days)

    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)

    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)

    AFC measured at baseline; COCs counted on the day of retrieval approx. 34-36 hours after ovulation trigger.

  • +2 more secondary outcomes

Study Arms (2)

AI Algorithm

EXPERIMENTAL

Trigger decisions trigger will be made by the physician assisted by AI guidance.

Device: STIMAI®.

Routine clinical management

ACTIVE COMPARATOR

Following routine clinical management with ovulation trigger decisions made by the physician alone,

Other: Routine clinical management

Interventions

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.

AI Algorithm
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.

Routine clinical management

Eligibility Criteria

Age18 Years - 42 Years
Sexfemale
Healthy VolunteersNo
Age GroupsAdult (18-64)

You may qualify if:

  • Undergoing COS for IVF with autologous oocytes, oocyte donation and elective fertility preservation with all monitoring USS (ultrasound scan) conducted at our centers.

You may not qualify if:

  • Medically indicated fertility preservation
  • Inability to attend clinic visits for monitoring.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (5)

Dexeus Mujer Sabadell

Sabadell, Barcelona, Spain

NOT YET RECRUITING

Dexeus Mujer Sant Cugat

Sant Cugat del Vallès, Barcelona, 08195, Spain

NOT YET RECRUITING

Dexeus Mujer Reus

Reus, Tarragona, 43202, Spain

NOT YET RECRUITING

Hospital Universitario Quiron Dexeus

Barcelona, 08028, Spain

RECRUITING

Dexeus Mujer Tarragona

Tarragona, 43206, Spain

NOT YET RECRUITING

Related Publications (8)

  • 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.

    PMID: 32819677BACKGROUND

  • 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.

    PMID: 22296973BACKGROUND

  • 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.

    PMID: 39164339BACKGROUND

  • 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.

    PMID: 35118395BACKGROUND

  • 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.

    PMID: 35027326BACKGROUND

  • 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.

    PMID: 37581894BACKGROUND

  • 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.

    PMID: 34256948BACKGROUND

  • 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.

    PMID: 34865998BACKGROUND

Related Links

MeSH Terms

Conditions

Infertility

Condition Hierarchy (Ancestors)

Genital DiseasesUrogenital Diseases

Study Design

Study Type
interventional
Phase
not applicable
Allocation
RANDOMIZED
Masking
NONE
Purpose
TREATMENT
Intervention Model
PARALLEL
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

March 24, 2026

First Posted

April 7, 2026

Study Start

April 8, 2026

Primary Completion (Estimated)

April 1, 2027

Study Completion (Estimated)

September 1, 2027

Last Updated

April 22, 2026

Record last verified: 2026-04

Locations

ES(5)