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| Name | Class |
|---|---|
| Univfy Inc. | INDUSTRY |
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Infertility is a globally significant medical condition, profoundly impacting individuals and couples both emotionally and physically. The multifaceted nature of in vitro fertilization (IVF) treatment demands active patient participation, with engagement playing a pivotal role in treatment success and satisfaction. However, suboptimal engagement can lead to challenges such as not initiating treatment, missed appointments, medication errors, dropping out and heightened stress levels, all of which may adversely affect clinical outcomes.
Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized healthcare, offering innovative solutions for personalized patient care. In IVF, AI-ML models hold the potential to enhance patient engagement by delivering tailored communication, reminders, and educational support, but also improved prognostication by providing personalized and accurate predictions of treatment outcomes. These capabilities enable patients to make more informed decisions and enhance their adherence to treatment protocols.This protocol outlines a prospective evaluation of an AI-ML model, specifically the Univfy PreIVF report, developed to improve patient engagement in IVF care. Recently, a retrospective, multicenter study reported improved IVF utilization rates among patients counselled using the Univfy PreIVF Report. The current study will prospectively assess the model's effectiveness in addressing individual patient needs and creating a supportive treatment environment. Specifically, this study will measure adherence to providers' recommendation of treatment protocols. By analyzing the impact of these interventions, this research aims to provide robust evidence for the integration of AI-ML technologies in reproductive medicine, paving the way for broader implementation and improved patient outcomes.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Conventional counselling group | A retrospective cohort of patients who underwent their new patient visit with one of the doctors participating in the study between December 2024 and June 2025 will be analyzed. | ||
| AI-based counselling group | A prospective cohort of patients undergoing their NPV with one of the doctors participating in the study will receive an artificial intelligence-machine learning report with their accurate personalized probabilities of having a live birth rate together with a medical explanation by their physician |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Artificial intelligence-Machine learning report with accurate personalized probabilities of having a live birth rate | Other | Patients included in the prospective arm will receive the Univfy® PreIVF Report with their accurate personalized probabilities of having a live birth rate (Univfy®) together with a medical explanation by their physician |
| Measure | Description | Time Frame |
|---|---|---|
| 9-month conversion rate | 9-month conversion, with conversion being defined as the first usage of Medically Assisted Reproduction (MAR) following a new patient visit | From enrollment until 9 months after |
| Measure | Description | Time Frame |
|---|---|---|
| 3-month MAR conversion | 3-month conversion rate | From enrollment until 3 months after |
| 6-month MAR conversion | 6-month conversion | From enrollment until 6 month after |
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Inclusion Criteria:
Exclusion Criteria:
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The study period will run from December 1, 2024, to December 31, 2025. During the first phase, from December 1, 2024, to May 31, 2025, a retrospective cohort of patients attending their first appointment at IVI Lisboa with a research team member will have received conventional counseling regarding IVF treatment prognosis. In the second phase, from June 1, 2025, to November 30, 2025, a prospective cohort of patients attending their first appointment with the same team will receive AI-based counseling using the Univfy® PreIVF Report.®.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Ana R Neves, MD, PhD | Contact | +351 800 180 614 | ana.neves@ivirma.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| IVI-RMA Lisboa | Recruiting | Lisbon | Portugal |
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|
| ID | Term |
|---|---|
| D007246 | Infertility |
| ID | Term |
|---|---|
| D000091662 | Genital Diseases |
| D000091642 | Urogenital Diseases |
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