Electromagnetic tracker feasibility in the design of a dental superstructure for edentulous patients

The success of the osseointegration concept and the Brånemark protocol is highly associated to the accuracy in the production of an implant-supported prosthesis. One of most critical steps for long-term success of these prosthesis is the accuracy obtained during the impression procedure, which is affected by factors such as the impression material, implant position, angulation and depth. This paper investigates the feasibility of 3D electromagnetic motion tracking systems as an acquisition method for modeling full-arch implant-supported prosthesis. To this extent, we propose an implant acquisition method at the patient mouth and a calibration procedure, based on a 3D electromagnetic tracker that obtains combined measurements of implant’s position and angulation, eliminating the use of any impression material. Three calibration algorithms (namely linear interpolation, higher-order polynomial and Hardy multiquadric) were tested to compensate for the electromagnetic tracker distortions introduced by the presence of nearby metals. Moreover, implants from different suppliers were also tested to study its impact on tracking accuracy. The calibration methodology and the algorithms employed proved to implement a suitable strategy for the evaluation of novel dental impression techniques. However, in the particular case of the evaluated electromagnetic tracking system, the order of magnitude of the obtained errors invalidates its use for the full-arch modeling of implant-supported prosthesis.

Computer-aided recognition of dental implants in X-ray images

Dental implant recognition in patients without available records is a time-consuming and not straightforward task. The traditional method is a complete user-dependent process, where the expert compares a 2D X-ray image of the dental implant with a generic database. Due to the high number of implants available and the similarity between them, automatic/semi-automatic frameworks to aide implant model detection are essential. In this study, a novel computer-aided framework for dental implant recognition is suggested. The proposed method relies on image processing concepts, namely: (i) a segmentation strategy for semi-automatic implant delineation; and (ii) a machine learning approach for implant model recognition. Although the segmentation technique is the main focus of the current study, preliminary details of the machine learning approach are also reported. Two different scenarios are used to validate the framework: (1) comparison of the semi-automatic contours against implant’s manual contours of 125 X-ray images; and (2) classification of 11 known implants using a large reference database of 601 implants. Regarding experiment 1, 0.97±0.01, 2.24±0.85 pixels and 11.12±6 pixels of dice metric, mean absolute distance and Hausdorff distance were obtained, respectively. In experiment 2, 91% of the implants were successfully recognized while reducing the reference database to 5% of its original size. Overall, the segmentation technique achieved accurate implant contours. Although the preliminary classification results prove the concept of the current work, more features and an extended database should be used in a future work.