Description of the company Philips is a global leader in health technology, committed to improving billions of lives worldwide and striving to make the world healthier and more sustainable through innovation. Driven by the vision of a better tomorrow. The goal of the R&D department “Image Chain” in Hamburg is to provide optimal components for X-ray diagnostics and interventional systems (computed tomography, interventional radiology, diagnostic X-ray). We have a long and successful history developing X-ray tubes and high voltage generators and continuously strive for product improvements as well as completely new offerings.
Situation Our computed tomography (CT) systems are used worldwide with a wide variety of usage profiles to save people's lives every day. During the development of the components for the systems, too little consideration is still given to the current usage profile of the hospitals. With the help of data analytics, we want to understand this better, thus developing better products and helping even more people in the world in the future.
Problem Typically, basic input assumptions about customer needs are used to create proposed concepts for X-Segments. These concepts therefore are not optimized to meet the true needs of the market, leading to reduced performance (eg forced wait times between scans) or unneeded costs / commercial uncertainty / overdesign.
Aims of the project This project aims to analyze the data of clinical scans collected in database RADAR, explore and model how potential future market trends could affect usage, and use these insights to directly drive the proposed performance properties of a new X-Segment concept design, e.g. the required Thermal Heat Capacity (and thus size) of the Anode Teller. Size of Anode teller is the key driver for many of the size, weight and cost properties of X-Ray Tube.
Design optimization based on field data using the Value / Lower Performance CT segment as an example.
Accurate analysis of the current usage of our products. Protocol distribution, usage statistics, thermal analyses (Digital Twin / TTS), geographical differences, ...
Target group (students)
Bachelors or Master’s degree in the field of Software Engineering, Data Science, Electrical Engineering, Computer science, Physics or similar technical degree.
Good understanding of common data processing / analytics and visualization techniques.