Dipo Olaosun’s scientific contributions

This research evaluates a qualitative data-driven approach where generative design is used to manufacture personalized wearable cooling caps capable of preventing chemotherapy-induced Alopecia. A plethora of CAD software is explored to transform human head data collected by healthcare professionals into usable CAD data through means of a generative design, where a simple algorithm is developed to modify a CAD geometry for individual patients. 3D Printed bespoke cap artifacts are produced and presented in this research as a proof of concept for the developed framework.

Following an extensive research and development phase, in a funded project conducted over the past few years, personalized scalp cooling caps are developed with generative design tools using cranial data collected from healthcare professionals to provide an optimally fitting wearable cryotherapy device utilizing CAD packages and design tools.Recent research demonstrated personalized cooling caps are essential to improve Scalp Cooling success rates/efficacy to over 80% through a perfect fit. Perfect fit requires extensive iterative research with multidisciplinary global healthcare professionals, scientists, and Designers. Following a study where cranial parameters were studied that could provide the optimal fit of head wearable designs,several pilot studies were able to prove a 93.8% accuracy rate against control for human head data collection. Following this, collected data would be used to generate CAD models to be 3D printed,providing accurately fitting cooling caps that represented the measured patient's head with high precision. This approach utilizes a qualitative approach to mass customization whereby individuals’ cranial data drives the generative design of CAD models for mass personalization.