| Title: | Towards a FemTech Digital Twin for Improving Women's Health |
| Lead Institution: | Inselspital, Universitatsspital Bern |
| Principal investigator: |
Application 129200
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About
Cardiometabolic diseases are currently the leading cause of death worldwide, and women face sex-specific risk factors that significantly contribute to this burden. Two critical stages where these risks become prominent are pregnancy and menopause. However, despite the heightened vulnerability, women are often underserved in terms of appropriate diagnosis, preventive care, and aggressive treatment for cardiometabolic conditions. Therefore, we aim to conduct this research project, with the final aim of a FemTech DigitalTwin, which will allow for a tailored approach towards prediction and prevention strategies for cardiovascular diseases risk in women. The FemTech DigitalTwin will be a result of the combination of a prediction model and a mechanistic model. The prediction model will be developed using data from a prospective cohort designed for this specific purpose as well as data from the UK Biobank, using an AI/ML approach. The mechanistic model will be developed by deduction of model equations from mass, energy, momentum balances and biochemical reactions. We plan to finish the project within 3 years of accessing the data. This project encompasses a wide range of fertile and infertile populations. Even though the number of pregnancies continue to drop, 300,000 women died from pregnancy-related causes in 2017. On the other hand, World Health Organization projections predict that 1 Billion Women will have reached menopause by 2030. Therefore, As a co-clinical capacity builder, a "FemTech Digital Twin" could address the unmet needs in care for women by i) Addressing under-treatment, ii) Raising awareness and education on short- and long-term risks for both, clinicians and patients, and iii) Providing prediction and preventative care based on early and potentially reversible signs of cardiometabolic disease.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 18129 | Independent External Validation and Head-to-Head Comparison of Guideline-Recommended CVD Risk Prediction Models | Lum Kastrati (+8) | 2026 | American Journal of Preventive Cardiology |
Enabling scientific discoveries that improve human health