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

Study Overview
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Brief Summary
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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.

Detailed Description
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This study will collect prospective data, specifically 3D transvaginal ultrasound of ovaries at time of baseline evaluation at beginning of an ART cycle. All participants will be asked to give written consent to be included in the study. At the time of initial ultrasound that is routinely done on the first day of the ART cycle, the physician performing the ultrasound will use a 3D ultrasound transvaginal probe to perform the ultrasound and capture both 2D and 3D images. 3D ultrasound is performed routinely for patients undergoing ART and is not an investigative procedure, however is not uniformly performed at the time of the baseline ultrasound. As per standard practice, the baseline antral follicle count will be documented by the performing physician, as well as a 3D image saved to be analyzed later using AI. Information about the medical history, treatment and outcomes will be collected as part of the study. Data maintained in the medical record as a result of standard of care monitoring for IVF and IUI will also be used for this study. This will include semen analysis (male partners if applicable) and pregnancy outcomes. For male partners, the semen analysis record will be part of the fertility history and semen analysis will be performed as standard of care with semen processing for fertilization. Additional data related to the treatment and outcomes will be collected from the medical record from the time of consent through the end of the treatment (including pregnancy outcomes). The time commitment for subjects may take up to 1 month (time from consent signing to 3D ultrasound) and to the time of delivery if pregnant (up to 9 months). No further procedures will be performed in the study group.

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

Conditions
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Infertility, in Vitro Fertilization (IVF), ART

Intervention / Treatment
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* Other: AI to analyze 3 D ultrasound


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 89 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Other
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Other: 3D Ultrasound with AI<br>AI tool to assess antral follicle count using 3 D Ultrasound | Other: AI to analyze 3 D ultrasound<br>* AI to assess 3 D ultrasound to assess antral follicle count<br>* Other names: z;|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Number of mature oocytes | Number of mature oocytes (ART cycles) | 2 weeks | 
| Number of multiple gestation | Number of multiple gestation for OI cycles. | approximately 6- 8 weeks | 
| Number of clinical intrauterine pregnancies IVF | Clinical intrauterine pregnancies | approximately 6- 8 weeks | 
| Number of clinical intrauterine pregnancies OI | Clinical intrauterine pregnancies | Approximately 6- 8 weeks | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Artificial Intelligence