La Trobe University and reproductive health innovator GonGlobal have entered into a strategic collaboration to develop and validate advanced artificial intelligence (AI) and digital twin technologies for Assisted Reproductive Technology (ART) and In Vitro Fertilization (IVF).
Led by Professor Nilmini Wickramasinghe as part of La Trobe's Optus Digital Health team, the collaboration brings together GonGlobal's expertise in reproductive health innovation, including non-invasive drug delivery, therapeutic drug-device combination development and commercialisation, with the University's internationally recognised capabilities in AI-enabled health and digital twins.
The partnership aims to deliver a new generation of intelligent clinical decision-support tools that can enable more personalised, evidence-based and predictive IVF care.
The project will analyse large and complex ART/IVF clinical datasets, aiming to:
- identify clinically interpretable predictors of successful ART/IVF outcomes;
- predict the probability of pregnancy and live birth;
- predict embryo developmental outcomes and trajectories; and
- generate actionable evidence to support and enhance clinician decision making.
Key technologies to be developed and evaluated include:
- Cohort Matching Digital Twin - enabling patient-level comparison with clinically relevant cohorts and identification of personalised outcome patterns;
- FT-Transformer Model - applying advanced deep-learning techniques to complex clinical and tabular data; and
- Modified Temporal Fusion Transformer Model - modelling longitudinal patient and treatment data to improve prediction of IVF outcomes over time.
GonGlobal's mission is to radically improve the ART/IVF patient experience and treatment success.
GonGlobal aims to replace the pain and stress of the injection burden (often hundreds of injections to achieve a live birth) with inhalation of its proprietary formulations of the existing and approved drugs using a cloud-connected digital device, OVULAIR™, that will facilitate real-time communication between IVF patients and clinicians.
Combined with AI and machine learning applied to IVF datasets, this technology has the potential to support care that is increasingly tailored to each patient's individual biology and treatment trajectory.
Professor Wickramasinghe sees significant potential in the collaboration:
"This is an exciting opportunity to translate our work in AI-enabled digital twins into a highly personalised clinical setting. By combining sophisticated predictive analytics with patient-specific digital twins, we have the potential to generate genuinely actionable intelligence for clinicians and ultimately improve outcomes and the experience of patients undergoing IVF."
Professor Chris Pakes, Deputy Vice-Chancellor (Research and Innovation) emphasised La Trobe's continued excellence in moving research from discovery into action.
"La Trobe University has exceptional researchers, who continually develop new knowledge, technology and treatments. This partnership demonstrates the life-changing practices that arise when collaboration between industry and research occur. We are proud to support research with purpose and that makes a difference."
Ellen Gonda, Co-Founder and CEO of GonGlobal, said: "We are delighted to collaborate with Professor Wickramasinghe and La Trobe University to advance the future of reproductive healthcare. Our shared ambition is to move towards a more predictive, personalised and patient-centred model of reproductive care - one that can improve clinical decision making, treatment outcomes and the patient experience."
Dr Igor Gonda, Co-Founder, Chief Scientist and Executive Chairman of GonGlobal, said: "Our focus is to make each patient's journey more joyful, with an improved chance of realising their dream of giving birth. The use of digital twins is very promising for personalised medicine, especially in such diverse populations of patients as those seeking ART/IVF."
Together, GonGlobal and La Trobe University aim to establish a strong foundation for AI-enabled, predictive and personalised fertility care, with the potential to translate advanced digital technologies into meaningful socioeconomic benefits for patients and healthcare professionals.
