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Knee Osteoarthritis is the second most common disease in the world. High Tibial Osteotomy (HTO) offers a cost effective solution for thousands of patients who currently have no effective treatment options. The Toka- Surgical Simulator is a digital suite of tools to support surgeons in delivering this treatment. Using virtual data-imaging and 3D printing technologies surgeons will be able to train, plan and perform virtual surgery and personalised treatments. The proposed project aims to deliver: 1) A precise preoperative landscape for surgeons to deliver surgical cost efficiency in the existing NHS context. 2) Reduced waiting times and post-operative stays. 3) Reduced future healthcare costs. 4) Improved patient outcomes.

TOKA – HTO digital planning and personalised surgical devices

Knee Osteoarthritis is the second most common disease in the world. High Tibial Osteotomy (HTO) offers a cost-effective solution for thousands of patients who currently have no treatment options.

The TOKA® Surgical Box combines digital planning and 3D-printed operative devices to remove existing barriers to wider HTO adoption in delivering knee-preserving treatment.

Using virtual data imaging, additive manufacturing technologies and supported by a comprehensive training package, surgeons will be able to train, plan and perform virtual patient specific surgery before delivering personalised treatment.

TOKA is designed to deliver significant patient benefits and reductions in healthcare costs.

Popular opinion suggests that virtual reality (VR) is the future of medical and surgical simulation. However, the limitations of VR become apparent when the accuracy of features such as haptic feedback are brought into question. This is especially true in minimally invasive surgical simulation e.g. laparoscopy. Introducing video based augmented reality to affordable low-fidelity laparoscopic simulators will provide a more realistic and accessible training platform when compared to VR counterparts with much higher price points. With recent financial constraints being placed on the NHS it is imperative that we cater for smaller budgets whilst improving the technology available to clinicians.

Our goal is to make cardiovascular surgeries more efficient and effective. We propose to develop technology that helps clinicians to accurately plan and rehearse stent placements inside blood vessels. Using cutting-edge computational modelling, we make maximum use of available patient data and device mechanics to accurately predict the behaviour of devices inside each patient's vessel configuration. This will allow clinicians to optimise the device selection, thereby reducing complications and associated cost of stenting surgeries for hospitals and society.