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Showing 4 results for Eskandari
The Survey to Determine the Accuracy Rate of the Neoplasm Coding in Seyed Al-Shohada Hospital of Isfahan
Sakineh Saghaeiannejad Isfahani, Ali Garavand, Kazem Faghiri , Majid Golshani , Hossein Eskandari, Mojtaba Kafashi,
Volume 2, Issue 2 (Summer 2015)
Abstract

Introduction: The use of cancer suffering patients' information is possible when this information was organized and categorized properly through encoding the diagnoses and therapeutic procedures. Therefore the aim of this study was to determine the accuracy rate of the neoplasm coding in Seyed Al-Shohada Hospital of Isfahan city in 2011.

Method: This study was a descriptive and cross-sectional study. The population of this study were medical records created during the second half of 2011,308 ones of which were selected as sample. A self-designed checklist was the research tool, which was used after validation. Data analysis was performed using SPSS v16. Software through descriptive statistics.

Results: Investigating the surveyed records, the researchers found that the accuracy rate of the records in this hospital was 68%. Also the highest accuracy rate of the coding has been associated with neoplasms of the connective tissue (94%).

Conclusion: Regarding the obtained results, it is recommended that coding of morphology & related Z codes of neoplasms be set in coders' work order. It is also recommended that continuous educational coding courses be held in order to increase the accuracy rate of the neoplasm coding.


Multivariate Feature Extraction for Prediction of Future Gene Expression Profile
Parinaz Eskandarian, Jamshid Bagherzadeh Mohasefi, Habibollah Pirnejad, Zahra Niazkhani,
Volume 8, Issue 3 (12-2021)
Abstract
Introduction: The features of a cell can be extracted from its gene expression profile. If the gene expression profiles of future descendant cells are predicted, the features of the future cells are also predicted. The objective of this study was to design an artificial neural network to predict gene expression profiles of descendant cells that will be generated by division/differentiation of hematopoietic stem cells.
Method: The developed neural network takes the parent hematopoietic stem cell’s gene expression profile as input and generates the gene expression profiles of its future descendant cells. A temporal attention was proposed to encode the main time series and a spatial attention was also provided to encode the secondary time series.
Results: To make an acceptable prediction, the gene expression profiles of at least four initial division/differentiation steps must be known. The designed neural network surpasses the existing neural networks in terms of prediction accuracy and number of correctly predicted division/differentiation steps. The proposed scheme can predict hundreds of division/differentiation steps. The proposed scheme’ error in prediction of 1, 4, 16, 64, and 128 division/differentiation steps was 3.04, 3.76, 5.5, 7.83, and 11.06 percent, respectively.
Conclusion: Based on the gene expression profile of a parent hematopoietic stem cell, the gene expression profiles of its descendants can be predicted for hundreds of division/differentiation steps and if necessary, solutions must be sought to encounter future genetic disorders.

Segmentation of the Left Atrial Appendage in the Echocardiographic Images of the Heart Using a Deep Neural Network
Hossein Ghayoumi Zadeh, Ali Fayazi, Khosro Rezaee, Mohammad Hossein Gholizadeh, Mehdi Eskandari,
Volume 8, Issue 3 (12-2021)
Abstract
Introduction: Cardiovascular diseases are one of the leading causes of mortality in today’s industrial world. Occlusion of left atrial appendage (LAA) using the manufactured devices is a growing trend. The objective of this study was to develop a computer-aided diagnosis system for the identification of LAA in echocardiographic images.
Method: The data used in this descriptive analytical study included 3D echocardiographic images of the heart of 32 patients in King’s College Hospital in London. All patients were treated successfully using the LAA closure device. A total of 208 two-dimensional images were first obtained from each 3D echocardiographic image data set. Then, 1914 images in which the LAA region was clearly recognizable were selected for this study. The proposed neural network was compiled based on the YOLOv3 algorithm. Finally, 1369 and 545 images were used for training and testing the algorithm, respectively.
Results: The performance of the algorithm in detecting the LAA on a set of 545 images was compared with the regions detected in similar images by an expert in echocardiography through intersection over union (IOU). The algorithm was able to correctly identify the LAA region in all 545 examined images with an average IOU of 99.37%.
Conclusion: The proposed image-based algorithm could detect LAA region in echocardiographic images with a high accuracy. This method can be used to develop algorithms for automatic analysis of the LAA region to determine the size of the closure device and to plan an efficient procedure in LAA occlusion methods.

Evaluation of a Virtual Reality-Based Wearable Device for Patients with Ankle Movement Disorders
Majid Eslami, Iman Mousavian, Farideh Eskandari Farsani, Reyhane Dadgostar , Mohamad Asadollahi , Negar Rahimi , Atefeh Hatami , Samaneh Mohammadkhani ,
Volume 9, Issue 1 (6-2022)
Abstract
Introduction: Rehabilitation is one of the priorities that should be performed on patients with stroke injuries or accidents leading to disability. This study aimed to evaluate the equipment and designed virtual reality environment to improve the treatment of patients with mobility problems in the lower torso (ankle).
Method: The research consisted of three basic parts. In the section of mechanics, a movement mechanism was developed after design, and with the help of electronic equipment and Arduino (1.18.5), movements were measured. And finally, by designing a virtual reality environment in Unity software, communication with hardware, processors, and sensors was provided.
Results: Paying attention to graphic attractiveness and encouraging users to reuse the virtual reality system is one of the desirable results of this project, which can be effective in motivating users. In addition, considering the existence of distance and time of movement in a virtual reality environment, which depends on the user's movements in the real environment, it is possible to intelligently assess the patient's progress based on the number of sessions and distance traveled.
Conclusion: Virtual reality-based rehabilitation methods can have a good effect on the treatment process due to the graphic attractiveness in this environment, and along with other rehabilitation methods, can be effective in faster recovery of people who need rehabilitation services. This method can help patients return to normal living conditions and reduce the time of this process.

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