Abstract
BackgroundThe BONEBRIDGE® (Med-El GmbH) is a bone-conduction device comprising an external audio processor and an internal Bone Conduction-Floating Mass Transducer (BC-FMT) surgically anchored to the temporal bone. Due to the implant's size, its placement may be challenging in certain anatomies, necessitating thorough surgical planning. Manual planning methods are laborious, time-intensive, and prone to errors. This study aimed to develop and validate an automated algorithm for determining skull thickness, aiding in the surgical planning of the BONEBRIDGE and other devices requiring similar bone thickness estimations. Materials and methodsTwelve cadaveric temporal bones underwent clinical computed tomography (CT). A custom Python algorithm was developed to automatically segment bone from soft tissue, generate 3D models, and perform ray-tracing to estimate bone thickness. Two thickness colormaps were generated for each sample: the cortical thickness to the first air cell and the total thickness down to the dura. The algorithm was validated against expert manual measurements to achieve consensus interpretation. ResultsThe algorithm estimated bone-to-air thicknesses (mean = 4.7 mm, 95% Confidence Interval [CI] of 4.3–5.0 mm) that closely matched the expert measurements (mean = 4.7 mm, CI of 4.4–5.0 mm), with a mean absolute difference (MAD) of 0.3 mm. Similarly, the algorithm's estimations to the dura (6.0 mm, CI of 5.4–6.5 mm) were comparable to the expert markings (5.9 mm, CI of 5.4–6.5 mm), with a MAD of 0.3 mm. ConclusionsThe first automated algorithm to calculate skull thickness to both the air cells and dura in the temporal bone was developed. Colormaps were optimized to aid with the surgical planning of BONEBRIDGE implantation, however the tool can be generalized to aid in the surgical planning of any bone thickness application. The tool was published as a freely available extension to the open-source 3D Slicer software program (www.slicer.org).
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