Development of an applicative model to filter outliers from intra-operative acquired data in Total Knee Replacement using Computer Assisted Surgery

In orthopaedic surgery, with a special focus on Total Knee Arthroplasty (TKA), planning the intervention is of fundamental importance. The rate of success of the surgery is strictly connected to the right alignment of the prosthesis. In order to improve overall alignment and to minimize situations of gross positioning, systems for computer-assisted total knee arthroplasty have been introduced. These systems use computer technology for surgical planning, for guiding or performing surgical interventions. The surgeons use computer-assisted systems to acquire the surface of the bone, then the software reconstructs the patient-specific morphology and plans the most appropriate cut, depending on the characteristic of the intervention and on the surgeon’s specifications. The efficacy of this technology relies much on the correct use of the system by the surgeon, who must understand the basic principles behind the software and try not to commit errors during the registration phase. The work herein presented shows a method to detect and filter wrong acquisition from intra-operative data, to improve the performances of the system and lead to good results even if the registration phase was not optimal. Several methods from the literature were analysed, and the most suitable one for our purpose was found to be an algorithm already used in many medical images application: Random Sample Consensus (RANSAC) algorithm. An implementation of the algorithm was carried out and tried over many acquisitions from real intra-operative data and from plastic specimens. The optimization process to find the best parameters of the algorithm were carried out and led to the definition of a complete model, which describes the surface of the bone and finds eventual outliers from the data acquired by the surgeon in the operating room. The present algorithm shows an excellent compromise between precision and computational costs and is meant to be applied in the BLU-IGS’ software, a CAS system provided by Orthokey Italia s.r.l.