Current Issue : April - June Volume : 2017 Issue Number : 2 Articles : 5 Articles
Background: Efficacy and high availability of surgery techniques for refractive defect\ncorrection increase the number of patients who undergo to this type of surgery.\nRegardless of that, with increasing age, more and more patients must undergo cataract\nsurgery. Accurate evaluation of corneal power is an extremely important element\naffecting the precision of intraocular lens (IOL) power calculation and errors in this\nprocedure could affect quality of life of patients and satisfaction with the service provided.\nThe available device able to measure corneal power have been tested to be not\nreliable after myopic refractive surgery.\nMethods: Artificial neural networks with error backpropagation and one hidden\nlayer were proposed for corneal power prediction. The article analysed the features\nacquired from the Pentacam HR tomograph, which was necessary to measure the\ncorneal power. Additionally, several billion iterations of artificial neural networks were\nconducted for several hundred simulations of different network configurations and\ndifferent features derived from the Pentacam HR. The analysis was performed on a PC\nwith Intel�® Xeon�® X5680 3.33 GHz CPU in Matlab�® Version 7.11.0.584 (R2010b) with\nSignal Processing Toolbox Version 7.1 (R2010b), Neural Network Toolbox 7.0 (R2010b)\nand Statistics Toolbox (R2010b).\nResults and conclusions: A total corneal power prediction error was obtained for\n172 patients (113 patients forming the training set and 59 patients in the test set) with\nan average age of 32 �± 9.4 years, including 67% of men. The error was at an average\nlevel of 0.16 �± 0.14 diopters and its maximum value did not exceed 0.75 dioptres. The\nPentacam parameters (measurement results) providing the above result are tangential\nanterial/posterior. The corneal net power and equivalent k-reading power. The analysis\ntime for a single patient (a single eye) did not exceed 0.1 s, whereas the time of network\ntraining was about 3 s for 1000 iterations (the number of neurons in the hidden\nlayer was 400)....
Background: Endovascular intervention using a stent is a mainstream treatment for\ncerebral aneurysms. To assess the effect of intervention strategies on aneurysm hemodynamics,\nwe have developed a fast virtual stenting (FVS) technique to simulate stent\ndeployment in patient-specific aneurysms. However, quantitative validation of the FVS\nagainst experimental data has not been fully addressed. In this study, we performed\nin vitro analysis of a patient-specific model to illustrate the realism and usability of this\nnovel FVS technique.\nMethods: We selected a patient-specific aneurysm and reproduced it in a manufactured\nrealistic aneurismal phantom. Three numerical simulation models of the aneurysm\nwith an Enterprise stent were constructed. Three models were constructed to\nobtain the stented aneurysms: a physical phantom scanned by micro-CT, fast virtual\nstenting technique and finite element method. The flow in the three models was simulated\nusing a computational fluid dynamics software package, and the hemodynamics\nparameters for the three models were calculated and analyzed.\nResults: The computational hemodynamics in the patient-specific aneurysm of the\nthree models resembled the very well. A qualitative comparison revealed high similarity\nin the wall shear stress, streamline, and velocity plane among the three different\nmethods. Quantitative comparisons revealed that the difference ratios of the hemodynamic\nparameters were less than 10%, with the difference ratios for area average of\nwall shear stress in the aneurysm being very low.\nConclusions: In conclusion, the results of the computational hemodynamics indicate\nthat FVS is suitable for evaluation of the hemodynamic factors that affect treatment\noutcomes....
Background: The aim of this study is to research the lesion outline and temperature\nfield in different ways in atrial radiofrequency ablation by using finite element method.\nMethods: This study used the method which considered the thermal dosage to\ndetermine the boundary between viable and dead tissue, and compared to the 50 Ã?°C\nisotherm results in analyzing lesion outline. Besides, we used Hyperbolic equation\nwhich considered the relaxation time to calculate the temperature field and contrasted\nit with Pennesââ?¬â?¢ bioheat transfer equation.\nResults: As the result of the comparison of the lesion outline, when the ablation time\nwas 120 s, the isotherm of the thermal dosage was larger than the 50 Ã?°C isotherm\nand with the increasing of the voltage the gap increased. When the ablation voltage\nwas 30 V, the 50 Ã?°C isotherm was larger than the thermal dosage isotherm when the\nablation time was less than 160 s. The isotherms overlapped when the time was 160 s.\nAnd when the ablation time was more than 160 s, the 50 Ã?°C isotherm was less than the\nthermal dosage isotherm. As to the temperature field, when the ablation voltage was\n30 V with the ablation time 120 s the highest temperature decided by Hyperbolic was\n0.761 Ã?°C higher. The highest temperature changed with relaxation time. In most cases,\nthe highest temperature of the Hyperbolic was higher otherwise the relaxation time\nwas 30ââ?¬â??40 s.\nConclusions: It is better to use CEM43 Ã?°C to estimate the lesion outline when the\nablative time within 160 s. For temperature distribution, the Hyperbolic reflects the\ninfluence of heat transmission speed, so the result is more close to the actual situation....
Background: Wearable measurement of center of pressure (COP) coordinates is the\nkey of obtaining the measurement of natural gait. Plantar pressure insole is the right\nsensing unit for plantar pressure monitoring for long-term outdoor measurements and\nthe control of walking assisting exoskeleton robot. Itââ?¬â?¢s necessary to study the configuration\nof pressure sensing cells.\nMethods: This study explored the sensing cell configuration for the plantar pressure\ninsole. The data of plantar pressure of walking is collected for layout variants. The RMSE\nof COP coordinates estimations are used as the evaluation criteria.\nResults: The RMSE of COP coordinates decreases from 8.00 to 3.20 mm as the amount\nof pressure sensing cells increases from 2 to 7. The size of pressure sensing cells contribute\nto reduce the RMSE of COP coordinates and 7 pressure sensing cells, with the\nsize of 2.0ââ?¬â??2.5 cm have the satisfying performance. Adding pressure sensing cell in the\nheel and hallux area increase the accuracy of estimating COP coordinates.\nConclusion: Comparison results indicate that the configuration of 7 pressure sensing\ncells has a satisfying measurement performance....
Background: Flow diverter (FD) intervention is an emerging endovascular technique\nfor treating intracranial aneurysms. High flow-diversion efficiency is desired to accelerate\nthrombotic occlusion inside the aneurysm; however, the risk of post-stenting\nstenosis in the parent artery is posed when flow-diversion efficiency is pursued by\nsimply decreasing device porosity. For improving the prognosis of FD intervention,\nwe develop an optimization method for the design of patient-specific FD devices that\nmaintain high levels of porosity.\nMethods: An automated structure optimization method for FDs with helix-like wires\nwas developed by applying a combination of lattice Boltzmann fluid simulation and\nsimulated annealing procedure. Employing intra-aneurysmal average velocity as the\nobjective function, the proposed method tailored the wire structure of an FD to a\ngiven vascular geometry by rearranging the starting phase of the helix wires.\nResults: FD optimization was applied to two idealized (S and C) vascular models\nand one realistic (R) model. Without altering the original device porosity of 80%, the\nflow-reduction rates of optimized FDs were improved by 5, 2, and 28% for the S, C, and\nR models, respectively. Furthermore, the aneurysmal flow patterns after optimization\nexhibited marked alterations. We confirmed that the disruption of bundle of inflow is of\ngreat help in blocking aneurysmal inflow. Axial displacement tests suggested that the\noptimal FD implanted in the R model possesses good robustness to tolerate uncertain\naxial positioning errors.\nConclusions: The optimization method developed in this study can be used to\nidentify the FD wire structure with the optimal flow-diversion efficiency. For a given\nvascular geometry, custom-designed FD structure can maximally reduce the aneurysmal\ninflow with its porosity maintained at a high level, thereby lowering the risk of\npost-stenting stenosis. This method facilitates the study of patient-specific designs for\nFD devices....
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