scholarly journals Modeling of Fundus Laser Exposure for Estimating Safe Laser Coagulation Parameters in the Treatment of Diabetic Retinopathy

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 967
Author(s):  
Aleksandr Shirokanev ◽  
Nataly Ilyasova ◽  
Nikita Andriyanov ◽  
Evgeniy Zamytskiy ◽  
Andrey Zolotarev ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Diabetic Retinopathy ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Interpolation Method ◽  
Dimensional Structure ◽  
Laser Coagulation ◽  
Laser Exposure ◽  
Mean Square ◽  
Mean Square Deviation

A personalized medical approach can make diabetic retinopathy treatment more effective. To select effective methods of treatment, deep analysis and diagnostic data of a patient’s fundus are required. For this purpose, flat optical coherence tomography images are used to restore the three-dimensional structure of the fundus. Heat propagation through this structure is simulated via numerical methods. The article proposes algorithms for smooth segmentation of the retina for 3D model reconstruction and mathematical modeling of laser exposure while considering various parameters. The experiment was based on a two-fold improvement in the number of intervals and the calculation of the root mean square deviation between the modeled temperature values and the corresponding coordinates shown for the convergence of the integro-interpolation method (balance method). By doubling the number of intervals for a specific spatial or temporal coordinate, a decrease in the root mean square deviation takes place between the simulated temperature values by a factor of 1.7–5.9. This modeling allows us to estimate the basic parameters required for the actual practice of diabetic retinopathy treatment while optimizing for efficiency and safety. Mathematical modeling is used to estimate retina heating caused by the spread of heat from the vascular layer, where the temperature rose to 45 °C in 0.2 ms. It was identified that the formation of two coagulates is possible when they are located at least 180 μm from each other. Moreover, the distance can be reduced to 160 μm with a 15 ms delay between imaging.

scholarly journals Development a Method for Estimating a Safe Distance between Coagulates to Automatically Plan Retinal Laser Coagulation in Diabetic Retinopathy Treatment

2021 ◽  
Vol 24 (5) ◽  
pp. 89-101
Author(s):  
A. S. Shirokanev
Keyword(s):  
Mathematical Modeling ◽  
Thermal Conductivity ◽  
Diabetic Retinopathy ◽  
Interpolation Method ◽  
Three Dimensional ◽  
Laser Action ◽  
Laser Coagulation ◽  
Laser Exposure ◽  
Safe Distance ◽  
Study Results

Introduction. Diabetes mellitus is a common endocrine disease that can lead to retinal vascular damage caused by the spread of macular edema and the development of diabetic retinopathy. Currently, diabetic retinopathy is treated using retinal laser coagulation. However, since even modern systems do not demonstrate sufficient treatment efficacy, methods for providing laser coagulation support on the basis of patient data analysis are required.Aim. This paper aims to develop and study a method for estimating a safe distance between coagulates via the mathematical modeling of coagulation in order to provide laser coagulation support.Materials and methods. The problem of thermal conductivity is numerically modeled for laser action in a multilayer medium.Results. A method for estimating a safe distance between coagulates has been developed via the mathematical modeling of the thermal conductivity problem. An algorithm was established for reconstructing a three-dimensional fundus structure from OCT images. It was demonstrated that the convergence rate of the integro-interpolation method is higher than that of the finite difference method. The study revealed that the retina heats up to 45 ºС due to heat redistribution from the epithelial layer, as well as laser exposure. According to the study results, the developed method yields a safe distance of 180 µm. By increasing the delay between laser pulses by more than 10 ms, this distance can be reduced to 160 μm.Conclusion. The developed method can calculate distance corresponding to that used in medical practice. Besides safe distance, the use of this method will allow other laser coagulation parameters to be determined non-invasively: laser power and pulse duration recommended to achieve a therapeutic effect. These estimates can be used to automatically produce a preliminary laser coagulation plan to support diabetic retinopathy treatment.

scholarly journals GNINA 1.0: molecular docking with deep learning

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrew T. McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

AbstractMolecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

Fast collocation for Moho estimation from GOCE gravity data: the Iran case study

2020 ◽  
Vol 221 (1) ◽  
pp. 651-664
Author(s):  
H Heydarizadeh Shali ◽  
D Sampietro ◽  
A Safari ◽  
M Capponi ◽  
A Bahroudi
Keyword(s):  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Seismic Data ◽  
Active Faults ◽  
Earth’S Crust ◽  
Arabian Plate ◽  
Moho Depth ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Earth's Crust

SUMMARY The study of the discontinuity between crust and mantle beneath Iran is still an open issue in the geophysical community due to its various tectonic features created by the collision between the Iranian and Arabian Plate. For instance in regions such as Zagros, Alborz or Makran, despite the number of studies performed, both by exploiting gravity or seismic data, the depth of the Moho and also interior structure is still highly uncertain. This is due to the complexity of the crust and to the presence of large short wavelength signals in the Moho depth. GOCE observations are capable and useful products to describe the Earth’s crust structure either at the regional or global scale. Furthermore, it is plausible to retrieve important information regarding the structure of the Earth’s crust by combining the GOCE observations with seismic data and considering additional information. In the current study, we used as observation a grid of second radial derivative of the anomalous gravitational potential computed at an altitude of 221 km by means of the space-wise approach, to study the depth of the Moho. The observations have been reduced for the gravitational effects of topography, bathymetry and sediments. The residual gravity has been inverted accordingly to a simple two-layer model. In particular, this guarantees the uniqueness of the solution of the inverse problem which has been regularized by means of a collocation approach in the frequency domain. Although results of this study show a general good agreement with seismically derived depths with a root mean square deviation of 6 km, there are some discrepancies under the Alborz zone and also Oman sea with a root mean square deviation up 10 km for the former and an average difference of 3 km for the latter. Further comparisons with the natural feature of the study area, for instance, active faults, show that the resulting Moho features can be directly associated with geophysical and tectonic blocks.

scholarly journals A Variable Data Fusion Approach for Electromechanical Impedance-Based Damage Detection

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4204
Author(s):  
Shishir Kumar Singh ◽  
Rohan Soman ◽  
Tomasz Wandowski ◽  
Pawel Malinowski
Keyword(s):  
Data Fusion ◽  
Damage Detection ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Health Monitoring ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Damage Scenarios ◽  
Electromechanical Impedance ◽  
Fusion Approach

There is continuing research in the area of structural health monitoring (SHM) as it may allow a reduction in maintenance costs as well as lifetime extension. The search for a low-cost health monitoring system that is able to detect small levels of damage is still on-going. The present study is one more step in this direction. This paper describes a data fusion technique by combining the information for robust damage detection using the electromechanical impedance (EMI) method. The EMI method is commonly used for damage detection due to its sensitivity to low levels of damage. In this paper, the information of resistance (R) and conductance (G) is studied in a selected frequency band and a novel data fusion approach is proposed. A novel fused parameter (F) is developed by combining the information from G and R. The difference in the new metric under different damage conditions is then quantified using established indices such as the root mean square deviation (RMSD) index, mean absolute percentage deviation (MAPD), and root mean square deviation using k-th state as the reference (RMSDk). The paper presents an application of the new metric for detection of damage in three structures, namely, a thin aluminum (Al) plate with increasing damage severity (simulated with a drilled hole of increasing size), a glass fiber reinforced polymer (GFRP) composite beam with increasing delamination and another GFRP plate with impact-induced damage scenarios. Based on the experimental results, it is apparent that the variable F increases the robustness of the damage detection as compared to the quantities R and G.

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