Previous studies have demonstrated a frequent occurrence of screw/K-wire malpositioning during surgical fracture treatment under 2D fluoroscopy and a correspondingly high revision rate as a result of using intraoperative 3D imaging. In order to facilitate and accelerate the diagnosis of implant malpositioning in 3D data sets, this study investigates two versions of an implant detection software for mobile 3D C-arms in terms of their detection performance based on comparison with manual evaluation. The 3D data sets of patients who had received surgical fracture treatment at five anatomical regions were extracted from the research database. First, manual evaluation of the data sets was performed, and the number of implanted implants was assessed. For 25 data sets, the time required by four investigators to adjust each implant was monitored. Subsequently, the evaluation was performed using both software versions based on the following detection parameters: true-positive-rate, false-negative-rate, false-detection-rate and positive predictive value. Furthermore, the causes of false positive and false negative detected implants depending on the anatomical region were investigated. Two hundred fourteen data sets with overall 1767 implants were included. The detection parameters were significantly improved (p<.001) from version 1 to version 2 of the implant detection software. Automatic evaluation required an average of 4.1±0.4 s while manual evaluation was completed in 136.15±72.9 s (p<.001), with a statistically significant difference between experienced and inexperienced users (p=.005). In summary, version 2 of the implant detection software achieved significantly better results. The time saved by using the software could contribute to optimizing the intraoperative workflow.
Keywords: Cone-beam CT; Intraoperative 3D imaging; Screw detection; Screw malposition.
© 2022. The Author(s).