USING AI TO SPEED UP NAVIGATION IN ROBOTIC SURGERIES

Project overview

As there is a lot of complex motion and granular detail, surgery image data is very complex to analyse. Our consortium, which is comprised of Smith&Nephew Ltd, Deeper Insights and Imperial College London, won Innovate-UK funding to tackle this problem.

We developed custom Computer Vision algorithms using NN's - Deep Learning to identify body parts in medical images. This leads to Markerless Navigation - the ability to detect where to cut bone on a knee for a knee replacement in Robotic surgery.

Knee x-ray

The challenge

In the UK, the total number of Total Knee Replacements (TKA's) per year has increased from 13,546 in 2003 to 98,147 in 2019 costing the NHS an estimated £585m per year. The average cost of a TKA in the UK is £12,000, however, post-surgical complications, e.g surgical site infection, increases this cost by between £1618 and £2398 per patient.

The solution

Our ambitious and innovative project focussed on developing markerless and automated registration to track the patient's limbs. This was tailored for robotic-assisted orthopedic procedures using structured light technology assisted by deep learning to continuously capture the patient's anatomy during surgery.

Orthopedic robot holding a surgical scalpel
Medical team together

The end result

This new platform will be integrated within S&N's commercially available robotic platform 'NAVIO', which was previously supported by I-UK funding, and will obviate the need for percutaneous markers reducing set-up time, cost and complexity during surgery.

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