We present a centennial review of the history of the term known as the cosmological constant. First introduced to the general theory of relativity by Einstein in 1917 in order to describe a universe...

Background: Craniosynostosis is a condition caused by the premature fusion of skull sutures, leading to irregular growth patterns of the head. Three-dimensional photogrammetry is a radiation-free alternative to the diagnosis using computed tomography. While statistical shape models have been proposed to quantify head shape, no shape-model-based classification approach has been presented yet. Methods: We present a classification pipeline that enables an automated diagnosis of three types of craniosynostosis. The pipeline is based on a statistical shape model built from photogrammetric surface scans. We made the model and pathology-specific submodels publicly available, making it the first publicly available craniosynostosis-related head model, as well as the first focusing on infants younger than 1.5 years. To the best of our knowledge, we performed the largest classification study for craniosynostosis to date. Results: Our classification approach yields an accuracy of 97.8 %, comparable to other state-of-the-art methods using both computed tomography scans and stereophotogrammetry. Regarding the statistical shape model, we demonstrate that our model performs similar to other statistical shape models of the human head. Conclusion: We present a state-of-the-art shape-model-based classification approach for a radiation-free diagnosis of craniosynostosis. Our publicly available shape model enables the assessment of craniosynostosis on realistic and synthetic data.

Background: Middle latency auditory evoked responses (MLAER) as a measure of depth of sedation are critically dependent on data quality and the analysis technique used. Manual peak labeling is subject to observer bias. This study investigated whether a user-independent index based on wavelet transform can be derived to discriminate between awake and unresponsive states during propofol sedation. Methods: After obtaining ethics committee approval and written informed consent, 13 volunteers and 40 patients were studied. In all subjects, propofol was titrated to loss of response to verbal command. The volunteers were allowed to recover, then propofol was titrated again to the same end point, and subjects were finally allowed to recover. From three MLAER waveforms at each stage, latencies and amplitudes of peaks Pa and Nb were measured manually. In addition, wavelet transform for analysis of MLAER was applied. Wavelet transform gives both frequency and time information by calculation of coefficients related to different frequency contents of the signal. Three coefficients of the so-called wavelet detail level 4 were transformed into a single index (Db3d4) using logistic regression analysis, which was also used for calculation of indices for Pa, Nb, and Pa/Nb latencies. Prediction probabilities for discrimination between awake and unresponsive states were calculated for all MLAER indices. Results: During propofol infusion, subjects were unresponsive, and MLAER components were significantly depressed when compared with the awake states (P < 0.001). The wavelet index Db3d4 was positive for awake and negative for unresponsive subjects with a prediction probability of 0.92. Conclusion: These data show that automated wavelet analysis may be used to differentiate between awake and unresponsive states. The threshold value for the wavelet index allows easy recognition of awake versus unresponsive subjects. In addition, it is independent of subjective peak identification and offers the advantage of easy implementation into monitoring devices.