PelviTwin: Digital Twins and Ultrasound Technology to Prevent Childbirth Injuries
Pelvic floor injuries after childbirth are common but difficult to diagnose. The project develops a digital twin of the pelvic floor to help researchers and clinicians understand how injuries occur, predict and prevent risks, and design individualised rehabilitation strategies, thereby improving women’s health and quality of life.
Background
Many women experience pelvic floor injuries after childbirth, especially to the large levator ani muscle (LAM). These injuries can cause incontinence, prolapse, and other long-term problems that affect quality of life. Despite up to half of middle-aged women suffering from pelvic floor dysfunction, these muscle injuries remain poorly understood and are often difficult to detect. Current diagnostic methods mainly show anatomy and miss important features such as muscle strength, structure, and vulnerability. Today, there are no reliable tools to understand injury mechanisms, assess risks before childbirth, or develop effective preventive and rehabilitative strategies.
Project goals / research focus
The project builds on a pilot study from spring 2025, which developed ultrasound technology to study the pelvic floor muscles and identify biomechanical signs of childbirth injuries. The current phase expands this work with PelviTwin—a personalized digital twin of the pelvic floor. By combining advanced imaging techniques such as ultrasound, MRI, and elastography with computational models, the project bridges the gap between image-based diagnostics and biomechanical understanding of LAM injuries. This enables better insight into how injuries occur, prediction of individual risks before childbirth, and development of tailored preventive and rehabilitative strategies.
Advancing gender equality
PelviTwin promotes gender equality by addressing a historically underexplored aspect of women’s health. The project improves understanding of pelvic floor injuries after childbirth and enables accurate, cost-effective, image-based assessments that can be applied broadly to women in diverse clinical and demographic settings. This can reduce suffering, enhance quality of life and social inclusion, and contribute to sustainable healthcare and lower treatment costs.
Researcher
The project is led by Matilda Larsson, Professor of Medical Imaging at KTH. It is interdisciplinary and involves gynecologists, obstetricians, and engineers.
