AI for IVF Success Prediction
Using machine learning to improve in vitro fertilization outcomes and help families on their fertility journey.
We got featured on CBS for our project!
Research Overview
In vitro fertilization (IVF) is a complex and often emotionally challenging process for many families. Despite advances in reproductive medicine, success rates vary significantly, and the process involves multiple decisions with uncertain outcomes.
This research explores how artificial intelligence can be leveraged to better predict IVF outcomes, personalize treatment protocols, and ultimately improve success rates while reducing emotional and financial burden on patients.
Technical Architecture
Our AI-driven prediction system uses a multi-layered architecture to process and analyze data from various sources:
The architecture integrates data processing, machine learning models, and clinical interfaces to deliver actionable predictions.
Methodology
Data Collection
We've compiled anonymized data from various fertility clinics, including patient demographics, medical history, hormone levels, embryo quality assessments, and treatment outcomes.
AI Models
We're developing machine learning models that identify patterns in the data that human clinicians might miss. Our approach combines:
- Computer vision algorithms for embryo quality assessment
- Natural language processing to extract insights from clinical notes
- Predictive models for personalized treatment recommendations
Validation Process
Our models are being validated through retrospective analysis and prospective clinical trials in partnership with leading fertility clinics.
Potential Impact
The implications of this research extend beyond technical achievements:
- Reduced emotional stress for patients through more accurate success prediction
- Lower financial burden by avoiding unnecessary treatment cycles
- More equitable access to fertility treatment through optimization
- Better informed decision-making for both patients and clinicians