Differential diagnosis of Erythmato-Squamous Diseases using classification and regression tree

Introduction: Differential diagnosis of Erythmato-Squamous Diseases (ESD) is a major challenge in the field of dermatology. The ESD diseases are placed into six different classes. Data mining is the process for detection of hidden patterns. In the case of ESD, data mining help us to predict the diseases. Different algorithms were developed for this purpose. Objective: we aimed to use the Classification and Regression Tree (CART) to predict differential diagnosis of ESD. Methods: we used the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For this purpose, the dermatology data set from machine learning repository, UCI was obtained. The Clementine 12.0 software from IBM Company was used for modelling. In order to evaluation of the model we calculate the accuracy, sensitivity and specificity of the model. Results: The proposed model had an accuracy of 94.84 (Standard Deviation: 24.42) in order to correct prediction of the ESD disease. Conclusions: Results indicated that using of this classifier could be useful. But, it would be strongly recommended that the combination of machine learning methods could be more useful in terms of prediction of ESD. © 2016 Keivan Maghooli, Mostafa Langarizadeh, Leila Shahmoradi, Mahdi Habibi-koolaee, Mohamad Jebraeily, and Hamid Bouraghi

Investigating and validation numerical measures of corneal topographic data in LASIK surgery outcome using wavelet transform

In this paper we have approached to new method for analyzing the merit of LASIK operation using multiscalar analysis of discrete corneal topographic height data and transform it into a space-scale space using wavelet analysis technique, and to demonstrate the clinical applicability of these computations in the post-LASIK cornea. Forty patients who were candidate for LASIK operation were selected and seen preoperatively and their corneal topographic images were achieved. Then 6 weeks after operation, patients were assessed using corneal topographic analysis (TMS-1), subjective refraction, and the best-corrected visual acuity (VA). After that, Two-dimensional biorthogonal wavelets with the order 6.8 at the scales j=4 revealed the following parameters: root-mean square (RMSDEV) and mean absolute (MEANDEV) deviation and maximum absolute height of the peaks or pitches (MAXPEAK) relative to the reference surface specified with the approximation component of scale j=4. It has shown that MEANDEV and MAXPEAK were correlated with the VA at the follow-up. ©2006 IEEE

Simulation of needle Electromyogram in Poliomyelitis by using line source model (in Persian)

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Simulation and analysis of needle electromyogram in Emery-Dreifuss muscular dystrophy by using line source model

Electromyography (EMG) is a valuable clinical test in detection of muscle and nerve pathology and distinguishing between myogenic and neurogenic conditions from normal condition. By using EMG, one assesses the pathophysiology on the basis of the waveform characteristics of the recorded signal. This requires detailed knowledge of the relationship between the waveform generators and the waveform measurements. In this study, we manipulated parameters of improved line source model for normal EMG generation to simulate Emery-Dreifuss Muscular Dystrophy (EDMD) disease. Common features of simulated signals in normal and EDMD conditions were extracted and quantitative analyses were performed. Finally, the simulation results and clinical results were compared and discussed. The results indicate the ability and validity of line source model in simulation EDMD disease and also confirm that EMG recordings in EDMD generally fulfill the criteria for myopathy.status: publishe

Statistical comparison between features extracted from simulated electromyogram in normal and EDMD situations (in Persian)

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Estimation and evaluation of pseudo-CT images using linear regression models and texture feature extraction from MRI images in the brain region to design external radiotherapy planning

Aim: The aim of this study is to construct and evaluate Pseudo-CT images (P-CTs) for electron density calculation to facilitate external radiotherapy treatment planning. Background: Despite numerous benefits, computed tomography (CT) scan does not provide accurate information on soft tissue contrast, which often makes it difficult to precisely differentiate target tissues from the organs at risk and determine the tumor volume. Therefore, MRI imaging can reduce the variability of results when registering with a CT scan. Materials and methods: In this research, a fuzzy clustering algorithm was used to segment images into different tissues, also linear regression methods were used to design the regression model based on the feature extraction method and the brightness intensity values. The results of the proposed algorithm for dose-volume histogram (DVH), Isodose curves, and gamma analysis were investigated using the RayPlan treatment planning system, and VeriSoft software. Furthermore, various statistical indices such as Mean Absolute Error (MAE), Mean Error (ME), and Structural Similarity Index (SSIM) were calculated. Results: The MAE of a range of 45�55 was found from the proposed methods. The relative difference error between the PTV region of the CT and the Pseudo-CT was 0.5, and the best gamma rate was 95.4 based on the polar coordinate feature and proposed polynomial regression model. Conclusion: The proposed method could support the generation of P-CT data for different parts of the brain region from a collection of MRI series with an acceptable average error rate by different evaluation criteria. © 2020 Greater Poland Cancer Centr