No abstract

Background Retrieval analysis has long served the orthopaedic community as a tool for understanding implant failure modes; however, what retrieval studies can reveal about the nature of prosthetic joint infection (PJI) remains unknown. We hypothesize that records from a comprehensive joint retrieval program should corroborate clinically-reported temporal characteristics of prosthesis-related infection. Methods We examined 2527 records documenting a decade of explanted hip and knee components to quantify the following: (1) the relative contribution of infection to revision arthroplasty; (2) the effects of joint type, revision status, and reason for retrieval on indwelling time; and (3) whether the temporal distribution of infected explants reflects clinical experience. Results In this series, 20% (507/2527) of explants were performed for infection, with PJI being more commonly implicated in the retrieval of revision implants than of primaries. Infected prostheses were explanted 23.2 months sooner on average than those retrieved for other causes. Within the subset of infected devices, revision components were explanted 11.2 months sooner than primaries, with no appreciable difference observed between hips and knees. Retrieval-based temporal distributions were most similar to PJI studies with endpoint enrollment or long duration follow-up, suggesting a later average onset of infection than reported in comparable clinical studies with short (<10-year) follow-up. Conclusions Infection represents a major cause of revision arthroplasty, and is associated with shorter indwelling times in revision components than in primaries. Studies with less than 10 years of follow-up are likely to under-report late PJI.

No abstract

No abstract

Stereo reconstruction is an important tool for generating 3D surface observations of deformable tissues that can be used to non-rigidly update intraoperative image guidance. As compared to traditional image processing-based stereo matching techniques, emerging machine learning approaches aim to deliver shorter processing times, more accurate surface reconstructions, and greater robustness to the suboptimal qualities of intraoperative tissue imaging (e.g., occlusion, reflection, and minimally textured surfaces). This work evaluates the popular PSMNet convolutional neural network as tool for generating disparity maps from the video feed of the da Vinci Xi Surgical System. Reconstruction accuracy and speed were assessed for a series of 44 stereoendoscopic frame pairs showing key structures in a silicone renal phantom. Surface representation accuracy was found to be on the order of 1mm for reconstructions of the kidney and inferior vena cava, and disparity maps were produced in under 2s when inference was performed on a standard modern GPU. These preliminary results suggest that PSMNet and similar trained models may be useful tools for integrating intraoperative stereo reconstruction into advanced navigation platforms and warrant further development of the overall data pipeline and testing with biological tissues in representative surgical conditions.