Management of the blunt pediatric cervical trauma - challenge for the trauma surgeon and the options of solving their emergency conditions

ГРУДНОЙ КЛЕТКИ ТРАВМЫЭМФИЗЕМА ПОДКОЖНАЯТРАХЕЯДЕТ

Management of the blunt pediatric cervical trauma - challenge for the trauma surgeon and the options of solving their emergency conditions

ГРУДНОЙ КЛЕТКИ ТРАВМЫЭМФИЗЕМА ПОДКОЖНАЯТРАХЕЯДЕТ

The role of optical coherence tomography criteria and machine learning in multiple sclerosis and optic neuritis diagnosis

BACKGROUND AND OBJECTIVES: Recent studies have suggested that inter-eye differences (IEDs) in peripapillary retinal nerve fiber layer (pRNFL) or ganglion cell+inner plexiform (GCIPL) thickness by spectral-domain optical coherence tomography (SD-OCT) may identify people with a history of unilateral optic neuritis (ON). However, this requires further validation. Machine learning classification may be useful for validating thresholds for OCT IEDs and for examining added utility for visual function tests, such as low-contrast letter acuity (LCLA), in the diagnosis of people with multiple sclerosis (PwMS) and for unilateral ON history. METHODS: Participants were from 11 sites within the International Multiple Sclerosis Visual System (IMSVISUAL) consortium. pRNFL and GCIPL thicknesses were measured using SD-OCT. A composite score combining OCT and visual measures was compared individual measurements to determine the best model to distinguish PwMS from controls. These methods were also used to distinguish those with history of ON among PwMS. ROC curve analysis was performed on a training dataset (2/3 of cohort), then applied to a testing dataset (1/3 of cohort). Support vector machine (SVM) analysis was used to assess whether machine learning models improved diagnostic capability of OCT. RESULTS: Among 1,568 PwMS and 552 controls, variable selection models identified GCIPL IED, average GCIPL thickness (both eyes), and binocular 2.5% LCLA as most important for classifying PwMS vs. controls. This composite score performed best, with AUC=0.89 (95% CI 0.85, 0.93), sensitivity=81% and specificity=80%. The composite score ROC curve performed better than any of the individual measures from the model (p<0.0001). GCIPL IED remained the best single discriminator of unilateral ON history among PwMS (AUC=0.77, 95% CI 0.71,0.83, sensitivity=68%, specificity=77%). SVM analysis performed comparably to standard logistic regression models. CONCLUSIONS: A composite score combining visual structure and function improved the capacity of SD-OCT to distinguish PwMS from controls. GCIPL IED best distinguished those with history of unilateral ON. SVM performed as well as standard statistical models for these classifications. CLASSIFICATION OF EVIDENCE: The study provides Class III evidence that SD-OCT accurately distinguishes multiple sclerosis from normal controls as compared to clinical criteria

Study of the internal quantum efficiency of FBK sensors with optimized entrance windows

Single-photon detection of X-rays in the energy range of 250 eV to 1 keV is difficult for hybrid detectors because of the low quantum efficiency and low signal-to-noise ratio. The low quantum efficiency is caused by the absorption of soft X-rays in the entrance window of the silicon sensors. The entrance window consists of an insensitive layer on the surface and a highly doped layer, which is typically from a few hundred nanometers to a couple of micrometers thick and is comparable to the absorption depth of soft X-ray photons (e.g. the attenuation length of 250 eV X-ray photons is ∼100 nm in silicon). The low signal-to-noise ratio is mainly caused by the small signal amplitude (e.g. ca. 70 electrons for 250 eV X-ray photons in silicon) with respect to the electronic noise. To improve the quantum efficiency, the entrance window must be optimized by minimizing the absorption of soft X-rays in the insensitive layer, and reducing charge recombination at the Si-SiO2 interface and in the highly doped region. Low gain avalanche diodes (LGADs) with a multiplication factor between 5 and 10 increase the signal amplitude and therefore improve the signal-to-noise ratio for soft X-rays, enabling single-photon detection down to 250 eV. Combining LGAD technology with an optimized entrance window technology can thus allow hybrid detectors to become a useful tool also for soft X-ray detection. In this work we present the optimization of the entrance window by studying the internal quantum efficiency of eight different process technology variations. The sensors are characterized using light emitting diodes with a wavelength of 405 nm. At this wavelength, the light has an absorption depth of 125 nm, equivalent to that of 276 eV X-rays. The best variation achieves an internal quantum efficiency of 0.992 for 405 nm UV light. Based on this study, further optimization of the quantum efficiency for soft X-rays detection is planned

Development of LGAD sensors with a thin entrance window for soft X-ray detection

We show the developments carried out to improve the silicon sensor technology for the detection of soft X-rays with hybrid X-ray detectors. An optimization of the entrance window technology is required to improve the quantum efficiency. The LGAD technology can be used to amplify the signal generated by the X-rays and to increase the signal-to-noise ratio, making single photon resolution in the soft X-ray energy range possible. In this paper, we report first results obtained from an LGAD sensor production with an optimized thin entrance window. Single photon detection of soft X-rays down to 452 eV has been demonstrated from measurements, with a signal-to-noise ratio better than 20