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Use of Artificial Intelligence for Clinical Assessment of Assisted Reproductive Techniques and IVF Outcomes

Weill Medical College of Cornell University · NCT04255615

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Opens the official ClinicalTrials.gov record, with the study team’s contacts. Bored Studies doesn’t enroll anyone.

Key facts

Compensation
Not posted. Ask the study team before screening.
Who can join
Ages 18–89 · All sexes
Healthy volunteers
Accepted
Study type
Interventional · Not applicable
Target enrollment
4000
US sites recruiting
1
Study start
February 12, 2020
Record last updated
December 24, 2025

Status re-checked against ClinicalTrials.gov within the last 12 hours.

What the study is about

The use of machine learning techniques using an artificial intelligence tool is proposed to analyze clinical data to predict best possible IVF/ART outcomes. This tool has been utilized to accurately predict embryo quality here at Cornell. Utilizing this tool to assess objective clinical findings and predict outcomes of assisted reproductive techniques is sought, with the ultimate goal of an automated tool to reduce implicit physician bias. Within this goal, using this tool to objectively and accurately assess baseline ovarian reserve at the start of an ART cycle is proposed, using 3D sonography to image the ovary and artificial intelligence tool to objectively identify baseline antral follicle counts.

Being studied: AI to analyze 3 D ultrasound

Conditions: Infertility; in Vitro Fertilization (IVF); ART

Eligibility criteria

Copied from the official record. The study team makes the final decision at screening.

Show full criteria

Inclusion Criteria

  • All patients undergoing ovarian stimulation (including OI and IVF cycles)
  • Treatment for fresh embryo transfer and cryopreservation of oocytes or embryos upfront
  • Healthy male partners of the female subjects who agree to be part of the study.

Exclusion Criteria

  • None

Recruiting US locations

  • Weill Cornell Medicine

    New York, NY 10021

    Site contact: Nikica Zaninovic, PhD · 646-962-2764 · nizanin@med.cornell.edu

Study contacts (from the official record)

  • Nikica Zaninovic, PhD · 646-962-2764 · nizanin@med.cornell.edu
  • Rodriq Stubbs, NP · 646-962-3276 · res2011@med.cornell.edu
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Use of Artificial Intelligence for Clinical Assessment of Assisted… (NCT04255615) | Bored Studies