2018 9th Cairo International Biomedical Engineering Conference (CIBEC)最新文献_第4页

Visual Feedback Enabled Training Mannequin For Ophthalmic Blocks: an Evaluative Study 视觉反馈支持的眼角膜块训练模型:一项评估性研究

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641833

N. J. Kumar, Jaichandran V. Venkatakrishnan, B. George, M. Sivaprakasam, Jagadeesh Kumar V

{"title":"Visual Feedback Enabled Training Mannequin For Ophthalmic Blocks: an Evaluative Study","authors":"N. J. Kumar, Jaichandran V. Venkatakrishnan, B. George, M. Sivaprakasam, Jagadeesh Kumar V","doi":"10.1109/CIBEC.2018.8641833","DOIUrl":"https://doi.org/10.1109/CIBEC.2018.8641833","url":null,"abstract":"Recently, a training mannequin suitable for ophthalmic blocks has been developed. It provides a new feature of visual feedback to the trainee. A validation of the efficacy of this training system with needle angle visualization, ocular structure proximity, and procedural warning features was conducted in an evaluative study with 41 participants; 21 post graduate trainees and 20 ophthalmic consultants from a tertiary ophthalmic care facility in Chennai, India. The participant’s performance was evaluated and analyzed using an appropriate scoring scheme in two sessions with and without visual feedback. The participants were also requested to provide feedback on the anatomical likeness and usage. A two tailed signed rank Wilcoxon test verified the statistical significance of the visual feedback (Pz(4.55, 0.05)= 0.9999, $Plt $0.001). The mean score of the participants showed an increase 58.86% and 25.5% for graduate trainee and consultants respectively and a mean reduction of 85.41% in the warning indications provided was also substantiated the efficacy of the visual feedback.","PeriodicalId":407809,"journal":{"name":"2018 9th Cairo International Biomedical Engineering Conference (CIBEC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115996266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Image Reconstruction using Self-Prior Information for Sparse-View Computed Tomography 基于自先验信息的稀疏视图计算机断层扫描图像重建

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641789

Mona Selim, E. Rashed, M. Atiea, H. Kudo

引用次数: 2

3D Model Construction and Analysis of Female Genital Organs Using Monte Carlo Simulation for Early Detection of Cervical Intraepithelial Neoplasia 宫颈上皮内瘤变早期检测的女性生殖器官三维模型构建与蒙特卡罗模拟分析

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641808

Samar M. Abd El-fattah, Sherif H. El-Gohary, Noha Hassan

{"title":"3D Model Construction and Analysis of Female Genital Organs Using Monte Carlo Simulation for Early Detection of Cervical Intraepithelial Neoplasia","authors":"Samar M. Abd El-fattah, Sherif H. El-Gohary, Noha Hassan","doi":"10.1109/CIBEC.2018.8641808","DOIUrl":"https://doi.org/10.1109/CIBEC.2018.8641808","url":null,"abstract":"In this paper, Monte Carlo simulations of light propagation in realistic MRI based anatomical 3D models of the female genital organs, which are considered heterogeneous media, are presented. Three models representing a normal case with no cervical abnormalities and two abnormal cases representing early and late stage cervical cancer were used in the computations. The magnitude and the distribution of light intensity through the three models are computed for different sizes of the region of interest encompassing parts of structures surrounding the cervix, different penetration depths and different placements of the light source (trans-vaginal and transrectal). Results show that trans-rectal simulations are as useful as trans-vaginal simulations and that light absorption is highly dependent on the size and location of the cervical tumor with respect to the light source and the penetration depth with respect to the beginning of the cervix. The visualization of the light intensity maps in the cervix, surrounding organs and cervical tumors may provide insights into photodynamic therapy planning as well as into photoacoustic imaging of cervical cancer.","PeriodicalId":407809,"journal":{"name":"2018 9th Cairo International Biomedical Engineering Conference (CIBEC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116085819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Hybrid Machine Learning Approach for the Phenotypic Classification of Metagenomic Colon Cancer Reads Based on Kmer Frequency and Biomarker Profiling 基于Kmer频率和生物标志物分析的结肠癌宏基因组表型分类的混合机器学习方法

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641805

Ali Kishk, Asmaa Elzizy, D. Galal, El Razek, Esraa Fawzy, Gehad Ahmed, M. Gawish, S. Hamad, M. El-Hadidi

{"title":"A Hybrid Machine Learning Approach for the Phenotypic Classification of Metagenomic Colon Cancer Reads Based on Kmer Frequency and Biomarker Profiling","authors":"Ali Kishk, Asmaa Elzizy, D. Galal, El Razek, Esraa Fawzy, Gehad Ahmed, M. Gawish, S. Hamad, M. El-Hadidi","doi":"10.1109/CIBEC.2018.8641805","DOIUrl":"https://doi.org/10.1109/CIBEC.2018.8641805","url":null,"abstract":"Human Microbiome plays a critical role in health and the environment. Colorectal cancer (CRC) is the most common cause of death in many countries, and hence early diagnosis of CRC may help in increasing the survival rate. Tracking changes in the microbiome structure of human gut opens new gates towards the detection and prediction of the risk of CRC. Recently, machine learning became a powerful technique in many bioinformatics fields, one of which is metagenomics. Metagenomics is defined as the study of a collection of microbial genomes isolated directly and sequenced from its natural habitats. Applications of machine learning in metagenomics are numerous, among them are phenotype classification, taxonomic assignment, and sequence annotation. Phenotype classification is assigning a phenotypic class to each sample such as diseased or healthy, according to the available metadata. Phenotype classification in metagenomics is usually done on organism taxonomic units (OTU) tables extracted as a core step in the metagenomic analysis. On the other hand, Natural Language Processing (NLP) methods such as kmer frequency, can provide features for the machine learning model. In this study, we combined a biomarker profiling and a kmer frequency table approaches to classify colorectal cancer from metagenomic data.","PeriodicalId":407809,"journal":{"name":"2018 9th Cairo International Biomedical Engineering Conference (CIBEC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117140419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Impedance Analysis of Different Shapes of the Normal and Malignant White Blood Cells 正常和恶性白细胞不同形态的阻抗分析

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641788

Sameh Sherif, Y. Ghallab, A. Seddik, Hamdy Abdelhamid, Y. Ismail

引用次数: 2

Simultaneous and Successive Three Dimensional Hydrodynamic Focusing in Flow Cytometer 流式细胞仪同步连续三维流体动力聚焦

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641818

Mina A. Nagi, Nourhan A. Elbeheiry, Homam M. Shershira

引用次数: 0

An Efficient Computer Aided Detection for 3D Neurostructural Reconstruction of Magnetic Resonance Images 磁共振图像三维神经结构重建的高效计算机辅助检测

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641798

M. Mabrouk, S. Marzouk, Heba M.Afify

{"title":"An Efficient Computer Aided Detection for 3D Neurostructural Reconstruction of Magnetic Resonance Images","authors":"M. Mabrouk, S. Marzouk, Heba M.Afify","doi":"10.1109/CIBEC.2018.8641798","DOIUrl":"https://doi.org/10.1109/CIBEC.2018.8641798","url":null,"abstract":"The comprehensive framework for analyzing brain images performs by the integration between three dimensional (3D) reconstruction and neuroimaging approach to realize brain diseases progressions. Computer Aided Detection (CAD) technology has numerous achievements in brain tumor processing for improving the quality of brain visualization to support neuroradiologists without the need for surgical biopsy or resection. Despite the advance in the radiological diagnosis of neuroimaging data, magnetic resonance imaging (MRI) has some restrictions that related to human errors and incomplete interpretation of brain tumor regions. Also, MRI scan produces 2D images of the brain that was very difficult to handle different types of tumor. Therefore, many algorithms are used computer-based classification to accurately distinguish between tumor regions from the brain MR images that provided an early diagnosis of brain diseases. This study investigated the CAD system using 3D image reconstruction of MR brain and tumor structures efficiently under MATLAB platform to recognize the location, volume, and type of brain tumors. In addition, the proposed system applied the Fuzzy C-Means (FCM) algorithm as image segmentation and support vector machine (SVM) as image classification for tumor detection of MR brain images. Results confirmed that this 3D model depicted an advanced view for estimating of human brain diseases.","PeriodicalId":407809,"journal":{"name":"2018 9th Cairo International Biomedical Engineering Conference (CIBEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124755840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Copyright notice] (版权)

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/cibec.2018.8641813

引用次数: 0

Haplotype Block Partitioning for NARAC Dataset Using Interval Graph Modeling of Clusters Algorithm 基于区间图建模聚类算法的NARAC数据集单倍型块划分

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641758

Fatma S. Ibrahim, Mohamed N. Saad, A. M. Said, Hesham F. A. Hamed

{"title":"Haplotype Block Partitioning for NARAC Dataset Using Interval Graph Modeling of Clusters Algorithm","authors":"Fatma S. Ibrahim, Mohamed N. Saad, A. M. Said, Hesham F. A. Hamed","doi":"10.1109/CIBEC.2018.8641758","DOIUrl":"https://doi.org/10.1109/CIBEC.2018.8641758","url":null,"abstract":"Recently, genome-wide association studies (GWAS) depend on haplotype blocks rather than individual single-nucleotide polymorphism (SNP) because they are more powerful in association analysis. The computation of a genotyped dataset is considered as a challenge because of its massive size and its complexity. Several algorithms have been proposed for partitioning the genotype data into haplotype blocks. Most existing algorithms part genotype data into small blocks and ignore the middle regions of low linkage disequilibrium (LD) between strong related SNPs. Other methods produce redundant blocks by identifying haplotype block if all inside SNPs associated with the start and end SNPs of the block. This study has adopted the latest haplotype block partitioning method that based on the interval graph modeling of clusters algorithm. The proposed algorithm was applied on the North American Rheumatoid Arthritis Consortium (NARAC) dataset and then compared to confidence interval test (CIT), four-gamete test (FGT), and the solid spine of linkage disequilibrium (SSLD) methods. The dataset is preprocessed, and missing SNPs are imputed. This study demonstrates the distinctions between haplotype block partitioning methods and detects the haplotype blocks for NARAC dataset. The comparative study gives a better understanding of each method and produces different outcomes with different parameters.","PeriodicalId":407809,"journal":{"name":"2018 9th Cairo International Biomedical Engineering Conference (CIBEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128971437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An Enhanced Hybrid Model for Skin Diagnosis Using Deep Convolution Neural Network 基于深度卷积神经网络的皮肤诊断增强混合模型

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641806

D. Shoieb, S. Youssef

引用次数: 2