Diagnostic Accuracy and Performance Analysis of a Scanner-Integrated Artificial Intelligence Model for the Detection of Intracranial Hemorrhages in a Traumatology Emergency Department.

Intracranial hemorrhages require an immediate diagnosis to optimize patient management and outcomes, and CT is the modality of choice in the emergency setting. We aimed to evaluate the performance of the first scanner-integrated artificial intelligence algorithm to detect brain hemorrhages in a routine clinical setting. This retrospective study includes 435 consecutive non-contrast head CT scans. Automatic brain hemorrhage detection was calculated as a separate reconstruction job in all cases. The radiological report (RR) was always conducted by a radiology resident and finalized by a senior radiologist. Additionally, a team of two radiologists reviewed the datasets retrospectively, taking additional information like the clinical record, course, and final diagnosis into account. This consensus reading served as a reference. Statistics were carried out for diagnostic accuracy. Brain hemorrhage detection was executed successfully in 432/435 (99%) of patient cases. The AI algorithm and reference standard were consistent in 392 (90.7%) cases. One false-negative case was identified within the 52 positive cases. However, 39 positive detections turned out to be false positives. The diagnostic performance was calculated as a sensitivity of 98.1%, specificity of 89.7%, positive predictive value of 56.7%, and negative predictive value (NPV) of 99.7%. The execution of scanner-integrated AI detection of brain hemorrhages is feasible and robust. The diagnostic accuracy has a high specificity and a very high negative predictive value and sensitivity. However, many false-positive findings resulted in a relatively moderate positive predictive value.

Differences in Intrusive Memory Experiences in Post-traumatic Stress Disorder after Single, Re- and Prolonged Traumatization.

Intrusive memory experiences (IMEs) are a common symptom of post-traumatic stress disorder (PTSD). Sensory perceptions of IMEs in the PTSD context vary substantially. The present research examined 20 patients with a single trauma, 20 re-traumatized patients and 80 Holocaust-traumatized patients who suffered from PTSD. Our results revealed significant differences in IME frequency based on the types of trauma experience. The findings suggest that patients with prolonged (Holocaust) traumata suffered from visual (65%) and combined visual/acoustic intrusive memories (29%), whereas visual memory experiences were most frequent (90%) among single-trauma patients. The trauma experience and the intrusive memory trigger stimulus were interdependent. The type of trauma critically affects the traumatic experience. Future studies should focus on these findings to improve PTSD therapeutic options.

Spot quantification in two dimensional gel electrophoresis image analysis: comparison of different approaches and presentation of a novel compound fitting algorithm.

BACKGROUND: Various computer-based methods exist for the detection and quantification of protein spots in two dimensional gel electrophoresis images. Area-based methods are commonly used for spot quantification: an area is assigned to each spot and the sum of the pixel intensities in that area, the so-called volume, is used a measure for spot signal. Other methods use the optical density, i.e. the intensity of the most intense pixel of a spot, or calculate the volume from the parameters of a fitted function. RESULTS: In this study we compare the performance of different spot quantification methods using synthetic and real data. We propose a ready-to-use algorithm for spot detection and quantification that uses fitting of two dimensional Gaussian function curves for the extraction of data from two dimensional gel electrophoresis (2-DE) images. The algorithm implements fitting using logical compounds and is computationally efficient. The applicability of the compound fitting algorithm was evaluated for various simulated data and compared with other quantification approaches. We provide evidence that even if an incorrect bell-shaped function is used, the fitting method is superior to other approaches, especially when spots overlap. Finally, we validated the method with experimental data of urea-based 2-DE of Aß peptides andre-analyzed published data sets. Our methods showed higher precision and accuracy than other approaches when applied to exposure time series and standard gels. CONCLUSION: Compound fitting as a quantification method for 2-DE spots shows several advantages over other approaches and could be combined with various spot detection methods.The algorithm was scripted in MATLAB (Mathworks) and is available as a supplemental file.

A fast and robust method for automated analysis of axonal transport.

Cargo movement along axons and dendrites is indispensable for the survival and maintenance of neuronal networks. Key parameters of this transport such as particle velocities and pausing times are often studied using kymograph construction, which converts the transport along a line of interest from a time-lapse movie into a position versus time image. Here we present a method for the automatic analysis of such kymographs based on the Hough transform, which is a robust and fast technique to extract lines from images. The applicability of the method was tested on simulated kymograph images and real data from axonal transport of synaptophysin and tetanus toxin as well as the velocity analysis of synaptic vesicle sharing between adjacent synapses in hippocampal neurons. Efficiency analysis revealed that the algorithm is able to detect a wide range of velocities and can be used at low signal-to-noise ratios. The present work enables the quantification of axonal transport parameters with high throughput with no a priori assumptions and minimal human intervention.

Systematic heterogeneity of fractional vesicle pool sizes and release rates of hippocampal synapses.

Hippocampal neurons in tissue culture develop functional synapses that exhibit considerable variation in synaptic vesicle content (20-350 vesicles). We examined absolute and fractional parameters of synaptic vesicle exocytosis of individual synapses. Their correlation to vesicle content was determined by activity-dependent discharge of FM-styryl dyes. At high frequency stimulation (30 Hz), synapses with large recycling pools released higher amounts of dye, but showed a lower fractional release compared to synapses that contained fewer vesicles. This effect gradually vanished at lower frequencies when stimulation was triggered at 20 Hz and 10 Hz, respectively. Live-cell antibody staining with anti-synaptotagmin-1-cypHer 5, and overexpression of synaptopHluorin as well as photoconversion of FM 1-43 followed by electron microscopy, consolidated the findings obtained with FM-styryl dyes. We found that the readily releasable pool grew with a power function with a coefficient of 2/3, possibly indicating a synaptic volume/surface dependency. This observation could be explained by assigning the rate-limiting factor for vesicle exocytosis at high frequency stimulation to the available active zone surface that is proportionally smaller in synapses with larger volumes.

Distinct intracellular vesicle transport mechanisms are selectively modified by spastin and spastin mutations.

Spastin is a microtubule severing ATPase that regulates intracellular and axonal transport of vesicles. Intracellular vesicle trafficking was analyzed in differentiated SH-SY5Y-neuroblastoma cells, transfected with spastin wild-type and three spastin mutations (ΔN, K388R, S44L) to investigate spastin-mediated effects on the velocity of vesicles, stained with LysoTracker Red®. The vesicle velocity varied considerably between mutations and detailed analysis revealed up to five distinct velocity classes. Microtubule severing by overexpressed wild-type spastin caused reduced vesicle velocity. S44L and ΔN mutations, which were functionally impaired, showed similar velocities as control cells. K388R-transfected cells exhibited an intermediate velocity profile. The results support the idea that spastin mutations not only alter axonal transport, but in addition regulate intracellular trafficking in the cell soma as well.

Statistical methods for detecting and comparing periodic data and their application to the nycthemeral rhythm of bodily harm: A population based study.

BACKGROUND: Animals, including humans, exhibit a variety of biological rhythms. This article describes a method for the detection and simultaneous comparison of multiple nycthemeral rhythms. METHODS: A statistical method for detecting periodic patterns in time-related data via harmonic regression is described. The method is particularly capable of detecting nycthemeral rhythms in medical data. Additionally a method for simultaneously comparing two or more periodic patterns is described, which derives from the analysis of variance (ANOVA). This method statistically confirms or rejects equality of periodic patterns. Mathematical descriptions of the detecting method and the comparing method are displayed. RESULTS: Nycthemeral rhythms of incidents of bodily harm in Middle Franconia are analyzed in order to demonstrate both methods. Every day of the week showed a significant nycthemeral rhythm of bodily harm. These seven patterns of the week were compared to each other revealing only two different nycthemeral rhythms, one for Friday and Saturday and one for the other weekdays.

Background determination-based detection of scattered peaks.

In many instances of signal and image processing, it is indispensable to precisely distinguish scattered peaks from a background, e.g., camera signals in microscopy. Here we addressed the detection of Gaussian signals in simulated line profiles (LP) comparable with e.g., fluorescence microscopy data. In a first step, we measured the applicability of histogram-based global background estimation. We find that the method is valid for typical scattered Gaussian signals if they are averagely separated by interpeak distances of 5.5 standard deviations. This enabled us to design global background determination-based peak detection (GBPD). GBPD was compared with two local background determination-based signal detection methods that had been designed for analysis of electrophysiological data and microscopy images, respectively. We were able to prove via receiver-operator characteristic (ROC) comparisons of signal-to-noise ratio (SNR), interpeak distance, and filtering behavior that, when applicable, GBPD brings advantages in knowledge needed a priori, performance at any SNR, controllability and spatial resolution.

Determination of axonal transport velocities via image cross- and autocorrelation.

On their way to the synapse and back, neuronal proteins are carried in cargo vesicles along axons and dendrites. Here, we demonstrate that the key parameters of axonal transport, i.e., particle velocities and pausing times can be read out from CCD-camera images automatically. In the present study, this is achieved via cross- and autocorrelation of kymograph columns. The applicability of the method was measured on simulated kymographs and data from axonal transport timeseries of mRFP-labeled synaptophysin. In comparing outcomes of velocity determinations via a performance parameter that is analogous to the signal-to-noise ratio (SNR) definition, we find that outcomes are dependent on sampling, particle numbers and signal to noise of the kymograph. Autocorrelation of individual columns allows exact determination of pausing time populations. In contrast to manual tracking, correlation does not require experience, a priori assumptions or disentangling of individual particle trajectories and can operate at low SNR.