Background of Study. Identification of the exact make and model of an orthopaedic implant prior to a revision surgery can be challenging depending upon the surgeon's experience and available knowledge base about the available implants. The current identification procedure is manual and time consuming as the surgeon may have to do a comprehensive search within an online database of radiographs of an implant to make a visual match. There is further time lapse in contacting that particular implant manufacturer to confirm the make and model of the implant and then order the whole inventory for the revision surgery. This leads to delay in treatment thus requiring extra hospital bed occupancy. Materials and Methods. We have analysed image recognition techniques currently in use for image recognition to understand the underlying technologies based on an interface commonly known as Application Programming interface (API). These API's specifies how the software components of the proposed application interact with each other. The objective of this study is to leverage one or a combination of API's to design a fully functional application in the initial phase and that can help recognize the implant accurately from a large database of radiographs and then develop a specialized and advanced API/Technology in the implant identification application. Results. Our study takes into account the existent technologies such as Facebook, Pictoria, Imagga, Google images. We found that there is an API currently available that can be directly applied to build an implant recognition system. However, commonly known Facebook's image tagging algorithms to store unique information with each image is the starting point to help build an intelligent system that in combination with image processing and development of a custom implant recognition API. Conclusion. There is an urgent need to have a robust and accurate system for identification of orthopaedic implants. Revision surgeries may need to be carried out by hospitals without access to index surgery operating notes. Patients may approach the most convenient not necessarily the same surgeon for a revision surgery. The dependency upon surgeon's experience, hospitals facilities and archiving of records can be avoided with the use of a single application that allows multiple manufacturers to contribute to a database of catalogue of their products

Aims

The principles of evidence-based medicine (EBM) are the foundation of modern medical practice. Surgeons are familiar with the commonly used statistical techniques to test hypotheses, summarize findings, and provide answers within a specified range of probability. Based on this knowledge, they are able to critically evaluate research before deciding whether or not to adopt the findings into practice. Recently, there has been an increased use of artificial intelligence (AI) to analyze information and derive findings in orthopaedic research. These techniques use a set of statistical tools that are increasingly complex and may be unfamiliar to the orthopaedic surgeon. It is unclear if this shift towards less familiar techniques is widely accepted in the orthopaedic community. This study aimed to provide an exploration of understanding and acceptance of AI use in research among orthopaedic surgeons.