Automated Orthopaedic Implant Identification through Artificial Intelligence
Innovate UK
Unicorn Medics Limited
The project aims to automate the process of identifying orthopaedic implants on plain radiographs through artificial intelligence techniques. A software will be developed, wherein, a plain radiograph will be fed and the make and model of the implant will be identified within seconds.    The objective of the project lies in bringing a change in clinical practise by better utilisation of resources. Automatic identification of implants can lead to better inventory stock management, and better use of surgical and technical staff's time in contrast to spending hours correctly identifying implants' makes and models. We expect this to be invaluable in every joint replacement hospital, particularly those situated in areas with large migrant populations who may have been previously operated for a Total Knee Arthroplasty/Total Hip Arthroplasty with foreign unfamiliar implants. Alongside, the project looks towards tapping into the unmet need in the NHS to rapidly and accurately diagnose unfamiliar implants in the preoperative phase of revision arthroplasty of knee and hip. This will also allow a significant reduction in overall treatment cost and morbidity. In areas where resources are limited, or the technical staff inexperienced, automatic identification will go a long way in patient care.    The software will make orthopaedic surgeons more confident of the type of implant prior to surgery, translating to greater precision intra-op, knowing what type of implant is present. Performance of these automated methods is typically on par with trained surgeons and radiologists, and superior to general practitioners. The most experienced surgeons demonstrated an accuracy of 85.6% in recognising the type of implant. Artificial Intelligence has shown to provide nearly 99% accuracy on an identification task on 12 implant models (from the knee and hip), trained and validated on approximately 2,000 radiographs.    However, many of these systems use limited training data and/or only attempt to recognise small numbers of implant types, or confine themselves to validate on one joint. Our aim is to ensure a systematic validation of an implant identification system, with implants prioritised by cost-effectiveness, rarity and joint, on a minimum of 100 examples of each type, based on a quality assured, curated training dataset. The project will be encompassing both upper limb and lower limb X-rays.
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