We have created an algorithm for improving medical imaging tests that
enables more accurate and reliable diagnoses in less time. This,
combined with our AI to generate 3D models of both each patient’s organs
and surgical guides, allows specialists to operate
with greater precision in less time, as well as enabling them to
distinguish cancer cases that were previously inoperable. When connected to each patient’s data, this 3D model generates a digital twin that
we can subject to different treatments virtually before they
take place. We recreate this model physically with advanced materials
to enable a detailed, personalised simulation. The result of
our ecosystem is a reduction in surgery times of up to
70%, costs per patient by up to 40%, earlier recovery
and, in many cases, avoiding excessive imaging tests,
thereby reducing waiting lists. These physical and digital models serve to
keep universities connected with hospitals to receive
current cases and with the possibility of practising on hyper-realistic
simulators and with virtual reality.