VitalHelix offers AI‑driven predictive intelligence that forecasts IVF success rates before treatment begins, giving clinicians and patients data‑backed insight into likely outcomes. By integrating science‑backed decision support into fertility care, the platform helps reduce failed cycles and lowers the overall cost per live birth for women undergoing IVF.
Funding
Funding not disclosed
Founders
Product
Problem
Couples undergoing in‑vitro fertilization (IVF) face high costs—often exceeding $100 k per live birth—without reliable methods to predict which cycles will succeed. Clinics typically optimize individual cycle metrics rather than a patient’s overall chance of a live birth, leading to multiple failed attempts and increased financial and emotional burden.
Solution
VitalHelix offers a data‑driven decision support platform that predicts a patient’s likelihood of achieving a live birth before any IVF cycle begins. By integrating clinical data with models developed by fertility specialists and data scientists, the system generates a personalized “FICO‑style” score reflecting a woman’s reproductive biology. Clinicians can use this score to tailor treatment plans, prioritize interventions with higher success probabilities, and reduce the number of unnecessary cycles. The platform provides transparent, science‑backed guidance throughout the fertility care journey, aiming to lower the cost per live birth and improve overall outcomes.
Target Audience
Primary users are fertility clinics, reproductive endocrinologists, and IVF program managers seeking to improve patient counseling and treatment planning.
Features
- Predictive scoring algorithm that estimates live‑birth probability prior to treatment initiation
- Integration of patient medical history, hormonal profiles, and clinic-specific data into the model
- Clinician dashboard presenting risk scores, confidence intervals, and recommended next steps
- Evidence‑based decision pathways built in collaboration with fertility specialists
- Continuous model refinement using aggregated outcomes to enhance prediction accuracy