PurposeTo assess the accuracy and usability of an electromagnetic navigation system designed to assist Computed Tomography (CT) guided interventions.Materials and methods120 patients requiring a percutaneous CT intervention (drainage, biopsy, tumor ablation, infiltration, sympathicolysis) were included in this prospective randomized trial. Nineteen radiologists participated. Conventional procedures (CT group) were compared with procedures assisted by a navigation system prototype using an electromagnetic localizer to track the position and orientation of a needle holder (NAV group). The navigation system displays the needle path in real-time on 2D reconstructed CT images extracted from the 3D CT volume. The regional ethics committee approved this study and all patients gave written informed consent. The main outcome was the distance between the planned trajectory and the achieved needle trajectory calculated from the initial needle placement.Results120 patients were analyzable in intention-to-treat (NAV: 60; CT: 60). Accuracy improved when the navigation system was used: distance error (in millimeters: median[P25%; P75%]) with NAV = 4.1[2.7; 9.1], vs. with CT = 8.9[4.9; 15.1] (p<0.001). After the initial needle placement and first control CT, fewer subsequent CT acquisitions were necessary to reach the target using the navigation system: NAV = 2[2; 3]; CT = 3[2; 4] (p = 0.01).ConclusionThe tested system was usable in a standard clinical setting and provided significant improvement in accuracy; furthermore, with the help of navigation, targets could be reached with fewer CT control acquisitions.
The automatic recognition of typical pattern sequences (scenarios), as they are developing, is of crucial importance for computer-aided patient supervision. However, the construction of such scenarios directly from medical expertise is unrealistic in practice. In this paper, we present a methodology for data abstraction and for the extraction of specific events (data mining) to eventually construct such scenarios. Data abstraction and data mining are based on the management of three key concepts, data, information and knowledge, which are instantiated via an ontology specific of our medical domain application. After a detailed description of the proposed methodology, we apply it to the supervision of patients hospitalized in intensive care units. We report the results obtained for the extraction of typical abstracted pattern sequences during the process of weaning from mechanical ventilation.
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