Similarity detection on female pelvic anatomy imaging data usingMachine Learning methods and develop a first version of a measuringdevice prototype
Pelvic Organ Prolapse (POP) is a condition 1 in every 10 women is diagnosed with. The current non-surgical treatment for POP is an intravaginal device called pessary which has a 40% failure rate as its shape is not fitted to the female anatomy. Poor pessary design and performance arises from the limited data that is studied on the pelvic anatomy. The current research project will study available imaging data using Machine Learning algorithms to facilitate and automate the process for assessing and treating POP. The obtained outcome will be used to design a pessary that can be customized for each patient. This information will be incorporated in the POP assessments that FemTherapeutics perform in a clinical setting for an improved prolapse treatment .