Use of Artificial Intelligence for Clinical Assessment of Assisted Reproductive Techniques and IVF Outcomes
AI in ART
The Use of Artificial Intelligence for Clinical Assessment of Assisted Reproductive Techniques and IVF Outcomes
1 other identifier
interventional
4,000
1 country
1
Brief Summary
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for not_applicable
Started Feb 2020
Longer than P75 for not_applicable
1 active site
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
First Submitted
Initial submission to the registry
February 3, 2020
CompletedFirst Posted
Study publicly available on registry
February 5, 2020
CompletedStudy Start
First participant enrolled
February 12, 2020
CompletedPrimary Completion
Last participant's last visit for primary outcome
January 31, 2029
ExpectedStudy Completion
Last participant's last visit for all outcomes
September 30, 2029
December 24, 2025
December 1, 2025
9 years
February 3, 2020
December 17, 2025
Conditions
Keywords
Outcome Measures
Primary Outcomes (2)
Number of baseline antral follicle count
To assess the accuracy and feasibility of using our artificial intelligence tool to assess antral follicle count, an indicator of baseline ovarian reserve, at time of baseline ultrasound for ART compared to qAVCantral and manual follicle counts to number of total oocytes retrieved.
Baseline
Number of retrieved oocytes
To assess the accuracy and feasibility of using our artificial intelligence tool
2 weeks
Secondary Outcomes (4)
Number of mature oocytes
2 weeks
Number of multiple gestation
approximately 6- 8 weeks
Number of clinical intrauterine pregnancies IVF
approximately 6- 8 weeks
Number of clinical intrauterine pregnancies OI
Approximately 6- 8 weeks
Study Arms (1)
3D Ultrasound with AI
OTHERAI tool to assess antral follicle count using 3 D Ultrasound
Interventions
AI to assess 3 D ultrasound to assess antral follicle count
Eligibility Criteria
You may qualify if:
- 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.
You may not qualify if:
- None
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Weill Cornell Medicine
New York, New York, 10021, United States
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Nikica Zaninovic, PHD
Weill Medical College of Cornell University
Central Study Contacts
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- NA
- Masking
- NONE
- Purpose
- OTHER
- Intervention Model
- SINGLE GROUP
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
February 3, 2020
First Posted
February 5, 2020
Study Start
February 12, 2020
Primary Completion (Estimated)
January 31, 2029
Study Completion (Estimated)
September 30, 2029
Last Updated
December 24, 2025
Record last verified: 2025-12
Data Sharing
- IPD Sharing
- Will not share