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Introduction: Pedicle screw loosening is a major problem in spine surgery. Computed tomography (CT) is the gold standard to diagnose screw loosening. Disadvantages of CT include low sensitivity and specificity for the detection of loosened screws as well as the need for radiation exposure. The aim of this study was to provide a proof of concept of a novel, non-invasive, inductive sensing device for transcutaneous detection of screw loosening using a pulse induction metal detector. Materials/Methods: Two fresh frozen human cadavers were initially instrumented in the lumbar spinal region (L1 to L5). After assessment of the sensing device behavior using a wooden beam and 3D printed place holders of predefined distances, the ability of implant detection and screw stability determination were assessed during two experiments. Pedicle screw loosening was induced using 3D printed drill/loosening guides during the instrumentation of the lumbar spine. Screw stability was determined by applying weight to the spinous processes of interest and measuring the relative movement of the pedicle screw using the inductive sensor coil. Results: The sensitivity of our detection coil for an implant movement measurement showed to be high at close distances (60mV voltage change per mm movement), with signal amplitude vanishing at sensing distances of 50mm or greater. Signal amplitude significantly (p < .05) differed with the number of instrumented levels. When differentiating between instrumentation with and without loosened screws, significant (p < .05) mean differences were found in half of all comparative cases. All these differences were smaller than the predefined signal voltage threshold of (60 mV/mm). Discussion/Conclusion: In this study, the feasibility of a new, inductive and non-invasive sensor concept was tested. While the basic principle of the approach is promising, our implementation was not successful in demonstrating sufficient sensitivity for the required detectability. It appears conceivable that the concept can be successfully implemented with more sensitive sensors and more complex evaluation methods.