Search

Abstract Background Few studies address the appropriate duration of post-surgical antibiotic therapy for orthopedic infections; with or without infected residual implants. We perform two similar randomized-clinical trials (RCT) to reduce the antibiotic use and associated adverse events. Methods Two unblinded RCTs in adult patients (non-inferiority with a margin of 10%, a power of 80%) with the primary outcomes “remission” and “microbiologically-identical recurrences” after a combined surgical and antibiotic therapy. The main secondary outcome is antibiotic-related adverse events. The RCTs allocate the participants between 3 vs. 6 weeks of post-surgical systemic antibiotic therapy for implant-free infections and between 6 vs. 12 weeks for residual implant-related infections. We need a total of 280 episodes (randomization schemes 1:1) with a minimal follow-up of 12 months. We perform two interim analyses starting approximately after 1 and 2 years. The study approximatively lasts 3 years. Discussion Both parallel RCTs will enable to prescribe less antibiotics for future orthopedic infections in adult patients. Trial registration ClinicalTrial.gov NCT05499481. Registered on 12 August 2022. Protocol version: 2 (19 May 2022)

Abstract Background Computer-assisted techniques for surgical treatment of femoral deformities have become increasingly important. In state-of-the-art 3D deformity assessments, the contralateral side is used as template for correction as it commonly represents normal anatomy. Contributing to this, an iterative closest point (ICP) algorithm is used for registration. However, the anatomical sections of the femur with idiosyncratic features, which allow for a consistent deformity assessment with ICP algorithms being unknown. Furthermore, if there is a side-to-side difference, this is not considered in error quantification. The aim of this study was to analyze the influence and value of the different sections of the femur in 3D assessment of femoral deformities based on the contralateral anatomy. Material and methods 3D triangular surface models were created from CT of 100 paired femurs (50 cadavers) without pathological anatomy. The femurs were divided into sections of eponymous anatomy of a predefined percentage of the whole femoral length. A surface registration algorithm was applied to superimpose the ipsilateral on the contralateral side. We evaluated 3D femoral contralateral registration (FCR) errors, defined as difference in 3D rotation of the respective femoral section before and after registration to the contralateral side. To compare this method, we quantified the landmark-based femoral torsion (LB FT). This was defined as the intra-individual difference in overall femoral torsion using with a landmark-based method. Results Contralateral rotational deviation ranged from 0° to 9.3° of the assessed femoral sections, depending on the section. Among the sections, the FCR error using the proximal diaphyseal area for registration was larger than any other sectional error. A combination of the lesser trochanter and the proximal diaphyseal area showed the smallest error. The LB FT error was significantly larger than any sectional error (p

Abstract In this work, we developed and validated a computer method capable of robustly detecting drill breakthrough events and show the potential of deep learning-based acoustic sensing for surgical error prevention. Bone drilling is an essential part of orthopedic surgery and has a high risk of injuring vital structures when over-drilling into adjacent soft tissue. We acquired a dataset consisting of structure-borne audio recordings of drill breakthrough sequences with custom piezo contact microphones in an experimental setup using six human cadaveric hip specimens. In the following step, we developed a deep learning-based method for the automated detection of drill breakthrough events in a fast and accurate fashion. We evaluated the proposed network regarding breakthrough detection sensitivity and latency. The best performing variant yields a sensitivity of $$93.64 \pm 2.42$$ 93.64 ± 2.42 % for drill breakthrough detection in a total execution time of 139.29 $${\hbox { ms}}$$ ms . The validation and performance evaluation of our solution demonstrates promising results for surgical error prevention by automated acoustic-based drill breakthrough detection in a realistic experiment while being multiple times faster than a surgeon’s reaction time. Furthermore, our proposed method represents an important step for the translation of acoustic-based breakthrough detection towards surgical use.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.