AI Analysis of 3D Ultrasound for IVF Outcomes

(AI in ART Trial)

Overseen By Rodriq Stubbs, NP

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Weill Medical College of Cornell University

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 1

Duration: 6-8 weeks

4000 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial aims to use artificial intelligence to enhance the success of IVF (in vitro fertilization) by predicting embryo quality and outcomes. The AI tool analyzes 3D ultrasound images to assess ovarian reserve, aiding in the planning and optimization of fertility treatments. It reduces bias and makes the process more objective. Suitable participants include those currently undergoing ovarian stimulation for fertility treatments, whether for fresh embryo transfers or storing eggs or embryos. As an unphased trial, participants contribute to groundbreaking research that may improve fertility treatment success rates.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What prior data suggests that this AI tool is safe for analyzing 3D ultrasound in IVF outcomes?

Research has shown that using artificial intelligence (AI) with 3D ultrasound in assisted reproductive techniques, such as IVF, appears very promising. Studies have found that AI can enhance tasks like analyzing embryos and sperm, potentially making IVF more effective by predicting outcomes more accurately.

In earlier research, AI combined with 3D ultrasound data achieved about 89% accuracy in predicting ovarian response to treatment. This suggests the technology can handle complex information reliably without causing harm.

No reports of safety issues or negative effects have emerged from using AI in this manner. Since this study does not test a new drug or medical procedure and only uses AI as a tool, the risks are minimal. The AI tool itself is non-invasive and should be easy for participants to tolerate. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about using AI to analyze 3D ultrasounds for IVF outcomes because it offers a new level of precision and efficiency. Unlike traditional methods that rely on human assessment, this AI tool can quickly and accurately count antral follicles, which are crucial for determining a patient's readiness for IVF. This technology could potentially streamline the IVF process, reduce human error, and improve overall success rates, making it a promising advancement in fertility treatments.

What evidence suggests that this AI tool is effective for predicting IVF outcomes?

Research has shown that using artificial intelligence (AI) with 3D ultrasounds can improve results in fertility treatments like IVF. In this trial, participants will receive assessments using an AI tool to analyze 3D ultrasounds, specifically to assess antral follicle count. AI enhances the analysis of embryos and sperm, leading to more personalized care. One study found that AI tools accurately identified important factors, such as follicle sizes, linked to successful IVF outcomes. AI also predicts embryo quality, crucial for successful pregnancies. These findings suggest that AI could make IVF more effective by providing clear assessments and reducing biases in treatment. ^{1 2 6 7 8}

Who Is on the Research Team?

Nikica Zaninovic, PHD

Principal Investigator

Weill Medical College of Cornell University

Are You a Good Fit for This Trial?

This trial is for patients undergoing ovarian stimulation treatments, including those aiming for fresh embryo transfer and cryopreservation of oocytes or embryos. Healthy male partners of female subjects who consent can also participate.

Inclusion Criteria

I am undergoing treatment for embryo transfer and egg or embryo freezing.

All patients undergoing ovarian stimulation (including OI and IVF cycles)

I am a healthy male partner agreeing to participate in the study.

Exclusion Criteria

Not applicable.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Baseline Evaluation

3D transvaginal ultrasound of ovaries at the beginning of an ART cycle to assess baseline ovarian reserve

1 day

1 visit (in-person)

Treatment

Participants undergo ART cycle with AI tool assessing antral follicle count and monitoring of IVF outcomes

6-8 weeks

Follow-up

Participants are monitored for pregnancy outcomes and other ART-related results

up to 9 months

What Are the Treatments Tested in This Trial?

Interventions

• AI to analyze 3 D ultrasound

Trial Overview The study tests an AI tool that analyzes 3D ultrasound images to predict the best IVF/ART outcomes. It aims to create an automated system that reduces physician bias by objectively assessing baseline ovarian reserve at the start of ART cycles.

How Is the Trial Designed?

Group I: 3D Ultrasound with AI Experimental Treatment 1 Intervention

AI tool to assess antral follicle count using 3 D Ultrasound

Who Is Running the Clinical Trial?

Weill Medical College of Cornell University

Lead Sponsor

Trials

1,103

Recruited

1,157,000+

Published Research Related to This Trial

Artificial intelligence (AI) is transforming embryo selection in assisted reproductive technology (ART) by automating the process, which enhances reliability and reproducibility, potentially leading to higher pregnancy rates for couples undergoing IVF.

AI applications in IVF labs are already being developed to assess embryologist performance, ensure quality control, and predict outcomes like embryo viability and live birth rates, suggesting that AI will soon become a standard practice in fertility treatments.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

New frontiers in embryo selection. Glatstein, I., Chavez-Badiola, A., Curchoe, CL.[2023]

A refined endometrial wave classification system was developed from ultrasound images of 24 IVF patients, introducing two new wave types: recoiling CF waves and standing waves.

The study demonstrated strong agreement between different observers when classifying endometrial wave types, indicating that the new system can reliably describe complex wave patterns during IVF cycles.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Complex endometrial wave-patterns in IVF. van Gestel, I., IJland, MM., Evers, JL., et al.[2016]

An artificial intelligence algorithm was developed to optimize the workflow during IVF by predicting the best day for monitoring ovarian stimulation, achieving a mean error of just 1.355 days in its predictions.

The algorithm also accurately estimated the total number of oocytes retrieved, with an accuracy of 76% using baseline data and 80% when including data from the monitoring day, which can help streamline IVF processes and reduce patient visits.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

An artificial intelligence platform to optimize workflow during ovarian stimulation and IVF: process improvement and outcome-based predictions. Letterie, G., MacDonald, A., Shi, Z.[2022]

A deep learning-based embryo selection model, trained on a dataset of 115,832 embryos from 18 IVF centers, demonstrated strong performance in predicting successful implantation, achieving an area under the curve (AUC) of 0.67 for known implantation data (KID) embryos and 0.95 for all embryos.

The model showed good generalizability across different clinics and patient subgroups, with AUC values ranging from 0.60 to 0.75 for KID embryos, indicating its potential to improve embryo selection in diverse clinical settings while reducing biases associated with manual evaluations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences. Berntsen, J., Rimestad, J., Lassen, JT., et al.[2022]

A multi-modal fusion model that combines ultrasound-based deep learning radiomics with clinical parameters significantly improves the prediction of clinical pregnancy outcomes after frozen embryo transfer (FET) in a study of 240 women, outperforming traditional methods that use either imaging or clinical data alone.

The model achieved a high area under the curve (0.825) and demonstrated strong sensitivity (96.2%) and specificity (58.3%), indicating its potential as a reliable and non-invasive tool for personalized evaluation of endometrial receptivity.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

An ultrasound-based deep learning radiomic model combined with clinical data to predict clinical pregnancy after frozen embryo transfer: a pilot cohort study. Liang, X., He, J., He, L., et al.[2023]

Recent advancements in IVF have incorporated artificial intelligence (AI) to analyze large datasets, aiming to personalize treatments and improve outcomes, particularly in embryo selection and stimulation protocols.

Despite these technological advancements, the embryo transfer process remains reliant on human skill, highlighting the importance of manual techniques and decision-making in achieving successful IVF results.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

In Contemporary Reproductive Medicine Human Beings are Not Yet Dispensable. Allahbadia, GN., Allahbadia, SG., Gupta, A.[2023]

Artificial intelligence classifiers analyzing images of blastocysts can predict the likelihood of achieving a live birth more accurately than conventional embryo evaluation, with overall accuracy rates of 0.72 compared to 0.74 for all age groups combined.

The AI showed improved performance across different maternal age categories, particularly excelling in predicting outcomes for women aged 40-41 and ≥42 years, indicating its potential as a valuable tool in reproductive medicine.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Feasibility of deep learning for predicting live birth from a blastocyst image in patients classified by age. Miyagi, Y., Habara, T., Hirata, R., et al.[2023]

Citations

1. trialx.com trialx.com/clinical-trials/listings/295690/use-of-artificial-intelligence-for-clinical-assessment-of-assisted-reproductive-techniques-and-ivf-outcomes/

Use of Artificial Intelligence for Clinical Assessment ... The use of machine learning techniques using an artificial intelligence tool is proposed to analyze clinical data to predict best possible IVF/ ...

2. sciencedirect.com sciencedirect.com/science/article/pii/S1028455924002742

Artificial intelligence and assisted reproductive technology The findings indicate that AI technologies significantly enhance ART processes by refining tasks such as embryo and sperm analysis and facilitating personalized ...

3. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC11966177/

Artificial Intelligence in Assisted Reproductive Technology Artificial intelligence (AI) is increasingly applied in assisted reproduction, enhancing success rates and enabling personalized fertility care.

4. nature.com nature.com/articles/s41467-024-55301-y

Explainable artificial intelligence to identify follicles that ... We harnessed explainable artificial intelligence to identify follicle sizes that contribute most to relevant downstream clinical outcomes.

5. cell.com cell.com/patterns/fulltext/S2666-3899(25)00195-3

Current progress and open challenges for applying ... In the context of IVF, these AI techniques can be employed to analyze the complex and diverse data from the IVF process, such as clinical ...

6. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC12108729/

Bioinformatic Analysis of Complex In Vitro Fertilization Data ... Using machine learning methods, predictive models were developed to estimate the likelihood of a live birth based on the corresponding case's characteristics.

7. sciencedirect.com sciencedirect.com/science/article/pii/S2666389925001953

Current progress and open challenges for applying ... 3D ultrasound data from 515 IVF cases, CNN (3D U-Net), the best model achieved an accuracy of 0.89 in predicting ovarian hyper-response. Barnes ...

8. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC9292970/

Artificial Intelligence in the Assessment of Female ... In this review, we mainly discussed the applicability, feasibility, and value of clinical application of AI in ultrasound to monitor follicles, ...