INTRODUCING ROSE™
ROSE™ enables egg banks and donor egg programs to optimize distribution & management of donor oocytes, using our best-in-class AI model.
Our clinically validated AI technology is the first in the world to objectively measure oocyte quality.
Developed leveraging the world’s largest oocyte dataset, our model delivers actionable, personalized insights for labs, doctors and patients.
Improve oocyte distribution
based on expected blastocyst outcomes
Create higher assurances in donor lot quality
with objective quality guarantees
Track and manage donor cycles and oocyte bank inventory
Access intuitive lot summary reports to build trust and transparency with recipient clinics
Evidence you can trust
MORE ACCURATE AND CONSISTENT THAN HUMAN ASSESSMENT
In four validation studies,
AI models outperformed all 50 embryologists from 15 different IVF clinics in predicting blastocyst development of mature oocytes with an average relative increased accuracy of 18%. When presented with the same oocyte image later on, our model demonstrated a 100% repeatability rate (versus 81.4% for embryologists).1,2
AI models outperformed all 50 embryologists from 15 different IVF clinics in predicting blastocyst development of mature oocytes with an average relative increased accuracy of 18%. When presented with the same oocyte image later on, our model demonstrated a 100% repeatability rate (versus 81.4% for embryologists).1,2
VALIDATED ON DONOR OOCYTES
Our AI model has been shown to be a valuable tool for assessing blastocyst development of donor oocytes and in optimizing the management of oocyte donation treatments.3,4
CORRELATED WITH
BLASTOCYST DEVELOPMENT, QUALITY AND PLOIDY STATUS
Studies have shown statistically significant differences in blastocyst development, blastocyst quality and euploid status between oocytes from lowest to highest score groups for our oocyte AI predictions.2,5,6
Designed in collaboration with egg banks to support
specialized workflows for donor eggs
1. CONNECT
Microscopes and lasers are connected directly to our platform for easy image-taking.
2. ANALYZE
Capture and upload oocyte images to a secure cloud in real time for analysis by our validated AI tool.
3. SORT
Oocyte predictions can be used to organize oocytes into optimized groups based on expected blastocyst outcomes.
4. INFORM
Download intuitive reports that support donor lab record keeping and tracking, and recipient clinic communications.
INTERESTED IN LEARNING MORE?
Get in touch with us
REFERENCES:
- D. Nayot, J. Meriano, R. Casper, A. Krivoi. An oocyte assessment tool using machine learning: Predicting blastocyst development based on a single image of an oocyte. Oral Presentation – 36th Annual Meeting of ESHRE – Copenhagen 2020.
- N. Mercuri, J. Fjeldstad, A. Krivoi, J. Meriano, D. Nayot. A non-invasive, 2-dimensional (2D) image analysis artificial intelligence (AI) tool scores mature oocytes and correlates with the quality of subsequent blastocyst development. O-191. Oral Presentation – 78th Scientific Congress of the ASRM – Anaheim 2022.
- M.C. Urda Muñoz, V. Palazzo, T. Sanchez, J. Avila, C. Marinè, A. Bellon, M. Hebles, V. Badajoz, L. Mifsud, J. Fjeldstad, N. Mercuri, I. Puerta Vega, D. Nayot, L. Rienzi, D. Cimadomo. An artificial intelligence-powered tool to score fresh donor oocytes and predict blastulation: interim analysis of a prospective investigation conducted at 3 laboratories, Human Reproduction, Volume 39, Issue Supplement_1, July 2024. https://doi.org/10.1093/humrep/deae108.537.
- J. Pons Ballester, M. Alavés, J. Teruel, J. Fjeldstad, N. Mercuri, A. Krivoi. Artificial intelligence (AI)-supported MAGENTA oocyte assessments shown to prospectively correlate with utilizable blastocyst development in patients, and for the first time in oocyte donors, Human Reproduction, Volume 38, Issue Supplement_1, June 2023. https://doi.org/10.1093/humrep/dead093.185.
- D. Nayot, N. Mercuri, A. Krivoi, R.F. Casper, J. Meriano, J. Fjeldstad. A novel non-invasive oocyte scoring system using AI applied to 2-dimensional images. Fertility and Sterility. Sep;21(116), No 3, Supplement, E474, ASRM 2021 Scientific Congress & Expo. https://www.fertstert.org/article/S0015-0282(21)01970-1/fulltext.
- J. Malmsten, N. Zaninovic, N. Mercuri, W. Qi, M. Jaberipour, D. Nayot, Z. Rosenwaks, J. Fjeldstad. Image-based oocyte model predictive of blastocyst development correlates with ploidy status across a broad spectrum of PGT-A indications, accounting for confounding variables. Human Reproduction, Volume 39, Issue Supplement 1, July 2024. https://doi.org/10.1093/humrep/deae108.251.