Institut für Medizintechnik und Medizininformatik

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    Publikation
    ICU Cockpit: a platform for collecting multimodal waveform data, AI-based computational disease modeling and real-time decision support in the intensive care unit
    (Oxford University Press, 13.05.2022) Boss, Jens Michael; Narula, Gagan; Straessle, Christian; Willms, Jan; Suter, Susanne; Buehler, Christof; Muroi, Carl; Mack, David Jule; Seric, Marko; Baumann, Daniel; Keller, Emanuela; Azzati, Jan; Brodbeck, Dominique; Lüthy, Rahel
    ICU Cockpit: a secure, fast, and scalable platform for collecting multimodal waveform data, online and historical data visualization, and online validation of algorithms in the intensive care unit. We present a network of software services that continuously stream waveforms from ICU beds to databases and a web-based user interface. Machine learning algorithms process the data streams and send outputs to the user interface. The architecture and capabilities of the platform are described. Since 2016, the platform has processed over 89 billion data points (N = 979 patients) from 200 signals (0.5–500 Hz) and laboratory analyses (once a day). We present an infrastructure-based framework for deploying and validating algorithms for critical care. The ICU Cockpit is a Big Data platform for critical care medicine, especially for multimodal waveform data. Uniquely, it allows algorithms to seamlessly integrate into the live data stream to produce clinical decision support and predictions in clinical practice.
    01A - Beitrag in wissenschaftlicher Zeitschrift
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    Publikation
    Automatic landmark identification for surgical 3d-navigation – A proposed method for marker-free dental surgical navigation systems
    (De Gruyter, 04.07.2022) Bischofberger, Micha; Schkommodau, Erik; Böhringer, Stephan
    This paper proposes a conceptual method to calculate the pose of a stereo-vision camera relative to an artificial mandible without additional markers. The general method for marker-free navigation has four steps: 1) parallel image acquisition by a stereo-vision camera, 2) automatic identification of 2d point pairs (landmark pairs) in a left and a right image, 3) calculation of related 3d points in the joint camera coordinate system and 4) matching of 3d points generated to a preoperative 3d model (i.e., CT data based). To identify and compare landmarks in the acquired stereo images, well-known algorithms for landmark detection, description and matching were compared within the developed approach. Finally, the BRISK algorithm (Leutenegger S, Chli M, Siegwart RY. BRISK: Binary Robust invariant scalable keypoints. Proceedings of the IEEE International Conference on Computer Vision; 2011: 2548–2555) was used. The proposed method was implemented in MATLAB and validated with one artificial mandible. The accuracy evaluation of the camera positions calculated resulted in an average deviation error of 1.45 mm ± 0.76 mm to the real camera displacement. This value was calculated using only stereo images with over 100 reconstructed landmark pairs each. This provides the basis for marker-free navigation.
    01A - Beitrag in wissenschaftlicher Zeitschrift