CTO Blog – Building the future of 3D printing in healthcare
axial3D has gone through a period of rapid growth both in terms of our team and ambition. My role is driving the firm’s technology strategy: improving the user experience, building out our machine learning capabilities and creating integrations with imaging and 3D printing partners. I’m proud to be working with a team that is truly committed to building the next generation of 3D anatomical printing technology helping doctors make better decisions.
My aim upon joining was to improve the process of making 3D models for surgeons. I wanted to make the overall process slicker – both faster and more intuitive. To do this I’ve focused on addressing the key technical challenges of quickly and reliably processing patient data – converting 2D patient scan data into 3D models. Our team of software developers and biomedical engineers have delivered improvements to the workflow that allow faster upload of image files and streamlined the process of defining 3D printed model requirements. We’ve shifted to a continuous delivery model, pushing out new versions of our software regularly. This means we are able to adapt to feedback from surgeons and radiologists, improving functionality and user experience quickly. It’s this pace of change that I find really exciting. It allows us to reliably move from prototype to design and into implementation through to delivery.
Here at axial3D, we are building the future of 3D printing in healthcare. Our platform axial3D insight makes it easy for surgeons to generate 3D anatomical models of their patients. Our models allow surgeons to make better decisions in the pre-planning phase of surgery to improve patient outcomes.
The future of radiology is in bringing doctors and machines together. We are actively developing new and better ways to visualize medical imaging and make patient data real. For us, that future involves the automation of image processing and 3D model generation. We are developing novel algorithms for image processing and applying machine learning to the process of creating 3D models. Our recent success in winning a Knowledge Transfer Partnership grant in collaboration with Ulster University to develop our 3D web-based visualization system. This will enable even more realistic and accurate representations of anatomy for ease of ordering by surgeons. I will discuss the technology and the development lifecycle in greater detail in upcoming blog posts.