UBITECH announces the successful funding of two research projects focused on artificial intelligence applications in assisted reproduction. Both projects, conducted in partnership with “Eugonia Assisted Reproduction Unit”, have been awarded grants under the “Research – Innovate” Action of the “Competitiveness” Program NSRF 2021-2027 (Call Code 08KE – MIS Code 9833), co-financed by the European Union and national resources through NSRF 2021-2027. Building on its successful collaboration with Eugonia, one of Europe’s leading assisted reproduction centers internationally recognized for scientific excellence, UBITECH will serve as the technology partner for two complementary AI-powered clinical decision support systems designed to revolutionize in vitro fertilization (IVF) treatment optimization.
AIOREUS (Enhancing IVF Success Through AI-Optimized 3D Ultrasound), with Grant Agreement number EKPAR01-0034469, aims to establish objective, volumetric criteria for determining the optimal day for triggering final oocyte maturation in IVF cycles, replacing traditional subjective 2D ultrasound measurements. The system leverages machine learning, including supervised and reinforcement learning techniques, to analyze 3D ultrasound data and precisely measure follicle dimensions, number, and volume. The project addresses significant inter- and intra-observer variability in conventional ultrasound monitoring while adapting to modern IVF practices, particularly the increasing adoption of “freeze-all” embryo strategies. AIOREUS will be validated through prospective randomized clinical trials assessing mature oocyte counts, embryo quality, and pregnancy rates.
In AIOREUS, UBITECH leads the comprehensive development of the system’s core architecture and artificial intelligence components. The company is responsible for collecting user, medical, and system requirements to create the complete architectural blueprint defining system behavior and seamless integration into Eugonia’s clinical workflow. UBITECH defines all concepts, entities, and relationships required by the recommendation engine to optimally represent information for efficient processing and inference. Additionally, UBITECH trains advanced detection algorithms using clinical study data through Deep Learning techniques and designs and implements the AI-driven recommendation engine that generates actionable medical recommendations based on ultrasound imaging, historical data, and medical information encoded in the semantic model.
OptiGon-AI (Personalized prediction of IVF success and optimal gonadotropin dose using AI based on AMH, 3D ultrasound, age, BMI), with Grant Agreement number EKPAR01-0058526, represents an innovative artificial intelligence system designed to optimize follicle-stimulating hormone (FSH) dosing during controlled ovarian stimulation in IVF cycles. The platform provides fully personalized therapeutic recommendations based on each patient’s clinical and biological profile, addressing the limitations of empirically determined dosing that often leads to suboptimal outcomes.
The system integrates advanced 3D ultrasound-based antral follicle count (AFC), anti-Müllerian hormone (AMH) levels, age, and body mass index (BMI) into sophisticated machine learning models. By personalizing FSH dosing, OptiGon-AI aims to increase the number and quality of retrieved oocytes, improve embryo quality, and enhance IVF success rates while minimizing the risk of ovarian hyperstimulation syndrome (OHSS).
In OptiGon-AI, UBITECH’s expertise drives the development of advanced AI algorithms and system integration across multiple critical areas. The company designs and implements state-of-the-art Deep Learning algorithms to calculate optimal FSH dosage based on multimodal patient data. UBITECH also develops AI mechanisms using reinforcement learning paradigms to discover novel variables and data combinations, revealing new research directions for factors influencing optimal dosing. The company validates recommendation engine results against defined scenarios and criteria, benchmarking predictions against scientific literature and categorizing them by expected clinical outcomes. UBITECH leads the complete integration of the OptiGon-AI system, incorporating services and applications developed across technical work packages in compliance with system architecture and requirements. Finally, UBITECH creates an intuitive graphical user interface with dedicated visualization components for effective interaction with and interpretation of system results.
Both 36-month projects target critical challenges in IVF treatment optimization: AIOREUS addresses the need for objective, reproducible criteria in follicle monitoring and triggering decisions, potentially improving pregnancy rates and supporting clinical decision-making, particularly for less experienced practitioners, while OptiGon-AI tackles the significant problem of non-personalized gonadotropin dosing, which results in suboptimal response in many patients—either insufficient oocyte retrieval or dangerous overstimulation. The system aims to achieve a 4% increase in collected oocytes while ensuring patient safety through OHSS risk reduction.
Upon completion, both projects will deliver significant advances across scientific, clinical, and commercial dimensions. From a scientific perspective, the projects will produce novel AI algorithms, generate new clinical insights through unsupervised learning techniques, and develop validated methodologies to be published in peer-reviewed journals. Clinically, the systems are expected to improve oocyte quality and quantity, achieve higher pregnancy rates in shorter timeframes, enhance patient safety, and provide personalized success predictions to support informed decision-making. Commercially, the projects will yield market-ready, commercially viable AI recommendation engines for the assisted reproduction sector, strengthening competitive positioning and attracting medical tourism to participating clinics.
