Bony healing of tuberosities around shoulder prostheses is difficult to obtain in the elderly patient. We hypothesized that reattachment of the tuberosities, performed in combination with bone grafting, around a specific reverse shoulder fracture-prosthesis (RSFP) would favour improved tuberosity healing and shoulder mobility in elderly patients with displaced proximal humerus fractures. We included 49 patients (50 shoulders)(45 female, 4 male) in this prospective study. Mean (± SD) age 80 ± 4 years (range, 70–88). Clinical evaluation consisted of ROM, VAS (pain), Constant scores, patient satisfaction (Subjective Shoulder Value (SSV)) and noted complications. Radiological evaluation consisted of tuberosity healing and component loosening. Mean follow-up 18 ± 8 months (12–39).BACKGROUND:
METHODS:
Lateralization of reversed shoulder arthroplasty provides improvement in range of motion and decreases inferior scapular notching. The purpose of this study was to verify if the autologous cancelous bone graft harvested from the humeral head does heal constantly in a large cohort of patients followed for a long time Cohort of 92 consecutive patients operated between 2006 and 2010 with a BIORSA for definitive shoulder pseudoparalysis, secondary to cuff tear arthropathy (CTA) or massive, irreparable cuff tear (MCT). The autogenous cancelous graft was harvested from humeral head in all cases. Eight patients were lost for follow up, and four died before 2 years. The remaining 80 patients underwent clinical, radiographic and CT assessment at a minimum FU of 24 months. Mean age was 73 years. Three independent observers evaluated notching, partial or total glenoid or humeral loosening and viability of the graft. Constant-Murley score, range of motion and subjective shoulder value (SSV) were recorded. The mean follow up was 39 months (range 24–74 months).Introduction:
Methods:
For any image guided surgery, independently of the technique which is used (navigation, templates, robotics), it is necessary to get a 3D bone surface model from CT or MR images. Such model is used for planning, registration and visualization. We report that graphical representation of patient bony structure and the surgical tools, inter-connectively with the tracking device and patient-to-image registration, are crucial components in such system. For Total Shoulder Arthroplasty (TSA), there are many challenges. The most of cases that we are working with are pathological cases such as rheumatoid arthritis, osteoarthritis disease. The CT images of these cases often show a fusion area between the glenoid cavity and the humeral head. They also show severe deformations of the humeral head surface that result in a loss of contours. These fusion area and image quality problems are also amplified by well-known CT-scan artefacts like beam-hardening or partial volume effects. The state of the art shows that several segmentation techniques, applied to CT-Scans of the shoulder, have already been disclosed. Unfortunately, their performances, when used on pathological data, are quite poor. In severe cases, bone-on-bone arthritis may lead to erosion-wearing away of the bone. Shoulder replacement surgery, also called shoulder arthroplasty, is a successful, pain-relieving option for many people. During the procedure, the humeral head and the glenoid bone are replaced with metal and plastic components to alleviate pain and improve function. This surgical procedure is very difficult and limited to expert centres. The two main problems are the minimal surgical incision and limited access to the operated structures. The success of such procedure is related to optimal prosthesis positioning. For TSA, separating the humeral head in the 3D scanner images would allow enhancing the vision field for the surgeon on the glenoid surface. So far, none of the existing systems or software packages makes it possible to obtain such 3D surface model automatically from CT images and this is probably one of the reasons for very limited success of Computer Assisted Orthopaedic Surgery (CAOS) applications for shoulder surgery. This kind of application often has been limited due to CT-image segmentation for severe pathologic cases and patient to image registration. The aim of this paper is to present a new image guided planning software based on CT scan of the patient and using bony structure recognition, morphological and anatomical analysis for the operated region. Volumetric preoperative CT datasets have been used to derive a surface model shape of the shoulder. The proposed planning software could be used with a conventional localisation system, which locates in 3D and in real time position and orientation for surgical tools using passive markers associated to rigid bodies that will be fixed on the patient bone and on the surgical instruments. 20 series of patients aged from 42 years to 91 years (mean age of 71 years) were analysed. The first step of this planning software is fully automatic segmentation method based on 3D shape recognition algorithms applied to each object detected in the volume. The second step is a specific processing that only treats the region between the humerus and the glenoid surface in order to separate possible contact areas. The third step is a full morphological analysis of anatomical structure of the bone. The glenoid surface and the glenoid vault are detected and a 3D version and inclination angle of the glenoid surface are computed. These parameters are very important to define an optimal path for drilling and reaming glenoid surface. The surgeon can easily modify the position of the implant in 3D aided by 3D and 2D view of the patient anatomy. The glenoid version/inclination angle and the glenoid vault are computed for each postion in real time to help the surgeon to evaluate the implant position and orientation. In summary, preoperative planning, 3D CT modelling and intraoperative tracking produced improved accuracy of glenoid implantation. The current paper has presented new planning software in the world of image guided surgery focused on shoulder arthroplasty. Within our approach, we propose, to use pattern recognition instead of manual picking of landmarks to avoid user intervention, in addition to potentially reducing the procedure time. A very important role is played by 3D data sets to visualise specific anatomical structures of the patient. The automatic segmentation of arthritic joints with bone recognition is intended to form a solid basis for the registration. The results of this methodology were tested on arthritic patients to prove that it is not just easy and fast to perform but also very accurate so it realises all conditions for the clinical use in OR.
