Panoramic Radiography in the Evaluation of the Relationship of Maxillary Molar Teeth and Maxillary Sinuses on the Deep Learning Models Improved with the Findings Obtained by Cone Beam Computed Tomography.

BACKGROUND: Panoramic radiography (PR) is available to determine the contact relationship between maxillary molar teeth (MMT) and the maxillary sinus floor (MSF). However, as PRs do not provide clear and detailed anatomical information, advanced imaging methods can be used. AIM: The aim of this study was to evaluate the diagnostic performance of deep learning (DL) applications that assess the relationship of the MSF to the first maxillary molar teeth (fMMT) and second maxillary molar teeth (sMMT) on PRs with data confirmed by cone beam computed tomography (CBCT). METHODS: A total of 2162 fMMT and sMMT were included in this retrospective study. The contact relationship of teeth with MSF was compared among DL methods. RESULTS: DL methods, such as GoogLeNet, VGG16, VGG19, DarkNet19, and DarkNet53, were used to evaluate the contact relationship between MMT and MSF, and 85.89% accuracy was achieved by majority voting. In addition, 88.72%, 81.19%, 89.39%, and 83.14% accuracy rates were obtained in right fMMT, right sMMT, left fMMT, and left sMMT, respectively. CONCLUSION: DL models showed high accuracy values in detecting the relationship of fMMT and sMMT with MSF.

The effect of silicon loss and fabrication tolerance on spectral properties of porous silicon Fabry-Perot cavities in sensing applications.

In this paper, we investigate the effect of non-uniformities (enlargement of current passage, non-equal surface current densities, etc.) in axial as well as transverse directions of a porous silicon Fabry-Perot (FP) cavity as well as loss nature of bulk silicon on spectral properties of this cavity, even that cavity is created with an anisotropic etching process. Without correct and comprehensive characterization of such cavities by incorporating these non-uniformities and inherent lossy nature of a cavity, detection and identification of biological and chemical molecules by that cavity may yield unpredictable and misleading results. From our simulations, we note the following two key points. First, effects of the refractive index and the thickness of microcavity region of a lossless or lossy FP cavity on resonance wavelength is more prevailing than those of first and last layers. Second, the effect of some small loss inside the FP cavity is not detectable by the measurement of resonance wavelength whereas the same influence is noticeable by the measurement of reflectivity. We carried out some measurements from two different regions on the fabricated cavities to validate our simulation results. From a practical point of view in correct detection and/or identification of lossy biological or chemical vapor by FP cavities, we conclude that not only the measurement of resonance wavelength as well as its shift but also the reflectivity value at the resonance wavelength or some specific wavelengths should be utilized.

Online estimation of lower and upper bounds for heart sound boundaries in chest sound using Convex-hull algorithm.

Heart sound localization in chest sound is an essential part for many heart sound cancellation algorithms. The main difficulty for heart sound localization methods is the precise determination of the onset and offset boundaries of the heart sound segment. This paper presents a novel method to estimate lower and upper bounds for the onset and offset of the heart sound segment, which can be used as anchor points for more precise estimation. For this purpose, first chest sound is divided into frames and then entropy and smoothed entropy features of these frames are extracted, and used in the Convex-hull algorithm to estimate the upper and lower bounds for heart sound boundaries. The Convex-hull algorithm constructs a special type of envelope function for entropy features and if the maximal difference between the envelope function and the entropy is larger than a certain threshold, this point is considered as a heart sound bound. The results of the proposed method are compared with a baseline method which is a modified version of a well-known heart sound localization method. The results show that the proposed method outperforms the baseline method in terms of accuracy and detection error rate. Also, the experimental results show that smoothing entropy features significantly improves the performance of both baseline and proposed methods.