Cervical cancer is a leading cause of death for women in low- and middle-income countries (LMICs) like Ethiopia, where barriers such as cost, distance, and a shortage of specialists prevent effective screening. Existing visual inspection methods in local clinics often lack the reliability needed for early detection, leading to preventable deaths. To address this, we have developed an integrated, low-cost (<€200) AI-assisted cervical imaging system. The device uses a novel cross-polarisation filter on an off-the-shelf endoscope to capture glare-free, high-quality images. A removable hardware guide ensures safe, repeatable positioning, while an AI model running on a portable Raspberry Pi provides real-time sharpness feedback and diagnostic classification support to non-specialist users. This system is designed to function as a "digital endoscope," empowering local healthcare workers to perform accurate screenings without expensive equipment. By enabling a "screen-and-treat" workflow, our solution aims to decentralise care, reduce the burden on overwhelmed referral hospitals, and provide women in remote communities with immediate access to life-saving diagnostics, transforming a multi-week ordeal into a single, confident clinical encounter.

This graduation project focuses on the redesign of the Chloe Gynaecological Training Model (GTM). The six-month project is being conducted on behalf of TU Delft and Chloe Company. The goal is to improve the first version of the GTM by enhancing usability, stability, and increasing the number of procedures that can be practiced on the model.

The development of the model is divided into three sprints. The first sprint focuses on improving the original model based on feedback from users in the Netherlands. The second sprint took place in Kenya, where, over a period of four weeks, the local context was explored and the model was tested with twenty medical professionals. In the third sprint, the feedback from Kenya was incorporated into the final design.