Artificial Intelligence (AI), Infertility (IVF Patients)
Conditions
Keywords
IVF, Artificial intelligence, counselling, Conversion rate
Brief summary
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.
Interventions
OTHERArtificial intelligence-Machine learning report with accurate personalized probabilities of having a live birth rate
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
Sponsors
Instituto Valenciano de Infertilidade de Lisboa
Univfy Inc.
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to 45 Years
Healthy volunteers
No
Inclusion criteria
* Infertile patients aged 18-45 years * Patients willing to undergo Medically Assisted Reproduction (heterosexual couples, same-sex female couples and single females undergoing artificial insemination, IVF/ICSI or oocyte donation treatments)
Exclusion criteria
* Age \>45 years * Patients who are not candidates for IVF/ICSI * Patients who are menopausal or peri-menopausal * Patients undergoing Fertility Preservation * Same-sex couples who will undergo reception of oocytes from partner. * Patients who decline to be counselled about their probability of having a live birth from IVF/ICSI treatment
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 9-month conversion rate | From enrollment until 9 months after | 9-month conversion, with conversion being defined as the first usage of Medically Assisted Reproduction (MAR) following a new patient visit |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 3-month MAR conversion | From enrollment until 3 months after | 3-month conversion rate |
| 6-month MAR conversion | From enrollment until 6 month after | 6-month conversion |
Countries
Portugal
Contacts
CONTACTAna R Neves, MD, PhD
Outcome results
None listed