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Computational and Mathematical Methods in Medicine

Sebastian Polak, Barbara Wiśniowska, Aleksander Mendyk, Adam Pacławski, Jakub Szlęk
Human heart electrophysiology is complex biological phenomenon, which is indirectly assessed by the measured ECG signal. ECG trace is further analyzed to derive interpretable surrogates including QT interval, QRS complex, PR interval, and T wave morphology. QT interval and its modification are the most commonly used surrogates of the drug triggered arrhythmia, but it is known that the QT interval itself is determined by other nondrug related parameters, physiological and pathological. In the current study, we used the computational intelligence algorithms to analyze correlations between various simulated physiological parameters and QT interval...
2018: Computational and Mathematical Methods in Medicine
Rui Hao, Yan Qiang, Xiaofei Yan
The accurate segmentation of pulmonary nodules is an important preprocessing step in computer-aided diagnoses of lung cancers. However, the existing segmentation methods may cause the problem of edge leakage and cannot segment juxta-vascular pulmonary nodules accurately. To address this problem, a novel automatic segmentation method based on an LBF active contour model with information entropy and joint vector is proposed in this paper. Our method extracts the interest area of pulmonary nodules by a standard uptake value (SUV) in Positron Emission Tomography (PET) images, and automatic threshold iteration is used to construct an initial contour roughly...
2018: Computational and Mathematical Methods in Medicine
ZhiFei Lai, HuiFang Deng
Histopathological image classification is one of the most important steps for disease diagnosis. We proposed a method for multiclass histopathological image classification based on deep convolutional neural network referred to as coding network. It can gain better representation for the histopathological image than only using coding network. The main process is that training a deep convolutional neural network is to extract high-level feature and fuse two convolutional layers' high-level feature as multiscale high-level feature...
2017: Computational and Mathematical Methods in Medicine
Abdullah-Al Nahid, Yinan Kong
Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians' time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings...
2017: Computational and Mathematical Methods in Medicine
Sebastian Schaetz, Dirk Voit, Jens Frahm, Martin Uecker
Purpose: To develop generic optimization strategies for image reconstruction using graphical processing units (GPUs) in magnetic resonance imaging (MRI) and to exemplarily report on our experience with a highly accelerated implementation of the nonlinear inversion (NLINV) algorithm for dynamic MRI with high frame rates. Methods: The NLINV algorithm is optimized and ported to run on a multi-GPU single-node server. The algorithm is mapped to multiple GPUs by decomposing the data domain along the channel dimension...
2017: Computational and Mathematical Methods in Medicine
Hui Zhang, Tangxin Li, Linqing Zheng, Xiangya Huang
[This corrects the article DOI: 10.1155/2017/9803018.].
2017: Computational and Mathematical Methods in Medicine
Yuan Gao, Yinglan Gong, Ling Xia
Atrial fibrosis is characterized by expansion of extracellular matrix and increase in the number of fibroblasts which has been associated with the development and maintenance of atrial arrhythmias. However, the mechanisms how the fibrosis contributes to atrial arrhythmia remain incompletely understood. In this study, we used a proposed fibroblast model coupled with the human atrial myocyte to investigate the effects of fibrosis on atrial excitability and repolarization at both cellular and macroscopic levels...
2017: Computational and Mathematical Methods in Medicine
Xiangyu Zhang, Hailun Jiang, Wei Li, Jian Wang, Maosheng Cheng
Protein tyrosine phosphatase 1B (PTP1B) is an attractive target for treating cancer, obesity, and type 2 diabetes. In our work, the way of combined ligand- and structure-based approach was applied to analyze the characteristics of PTP1B enzyme and its interaction with competitive inhibitors. Firstly, the pharmacophore model of PTP1B inhibitors was built based on the common feature of sixteen compounds. It was found that the pharmacophore model consisted of five chemical features: one aromatic ring (R) region, two hydrophobic (H) groups, and two hydrogen bond acceptors (A)...
2017: Computational and Mathematical Methods in Medicine
Davide Verotta, Janus Haagensen, Alfred M Spormann, Katherine Yang
Mathematical modeling holds great potential for quantitatively describing biofilm growth in presence or absence of chemical agents used to limit or promote biofilm growth. In this paper, we describe a general mathematical/statistical framework that allows for the characterization of complex data in terms of few parameters and the capability to (i) compare different experiments and exposures to different agents, (ii) test different hypotheses regarding biofilm growth and interaction with different agents, and (iii) simulate arbitrary administrations of agents...
2017: Computational and Mathematical Methods in Medicine
Syed Muhammad Usman, Muhammad Usman, Simon Fong
Epileptic seizures occur due to disorder in brain functionality which can affect patient's health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational methods are used for predicting epileptic seizures from Electroencephalograms (EEG) signals. However, preprocessing of EEG signals for noise removal and features extraction are two major issues that have an adverse effect on both anticipation time and true positive prediction rate...
2017: Computational and Mathematical Methods in Medicine
Hoon Sik Choi, Guang Sub Jo, Jong Pyo Chae, Sang Bong Lee, Chul Hang Kim, Bae Kwon Jeong, Hojin Jeong, Yun Hee Lee, In Bong Ha, Ki Mun Kang, Jin Ho Song
We evaluated the changes in the dose distribution of radiation during volumetric arc radiotherapy (VMAT), to determine the right time for adaptive replanning in prostate cancer patients with progressive weight (WT) changes. Five prostate cancer patients treated with VMAT were selected for dosimetric analysis. On the original computed tomography images, nine artificial body contours were created to reflect progressive WT changes. Combined with three different photon energies (6, 10, and 15-MV), 27 comparable virtual VMAT plans were created per patient...
2017: Computational and Mathematical Methods in Medicine
Hao Guo, Mengna Qin, Junjie Chen, Yong Xu, Jie Xiang
High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties...
2017: Computational and Mathematical Methods in Medicine
Tuan Anh Phan, Jianjun Paul Tian
The complexity of the immune responses is a major challenge in current virotherapy. This study incorporates the innate immune response into our basic model for virotherapy and investigates how the innate immunity affects the outcome of virotherapy. The viral therapeutic dynamics is largely determined by the viral burst size, relative innate immune killing rate, and relative innate immunity decay rate. The innate immunity may complicate virotherapy in the way of creating more equilibria when the viral burst size is not too big, while the dynamics is similar to the system without innate immunity when the viral burst size is big...
2017: Computational and Mathematical Methods in Medicine
Dillon Chrimes, Hamid Zamani
Big data analytics (BDA) is important to reduce healthcare costs. However, there are many challenges of data aggregation, maintenance, integration, translation, analysis, and security/privacy. The study objective to establish an interactive BDA platform with simulated patient data using open-source software technologies was achieved by construction of a platform framework with Hadoop Distributed File System (HDFS) using HBase (key-value NoSQL database). Distributed data structures were generated from benchmarked hospital-specific metadata of nine billion patient records...
2017: Computational and Mathematical Methods in Medicine
Geshel David Guerrero López, Mario Francisco Jesús Cepeda Rubio, José Irving Hernández Jácquez, Arturo Vera Hernandez, Lorenzo Leija Salas, Francisco Valdés Perezgasga, Francisco Flores García
Malignant neoplasms are one of the principal world health concerns and breast cancer is the most common type of cancer in women. Advances in cancer detection technologies allow treating it in early stages; however, it is necessary to develop treatments which carry fewer complications and aesthetic repercussions. This work presents a feasibility study for the use of microwave ablation as a novel technique for breast cancer treatment. A microwave applicator design is also being proposed for this purpose. The coupling of the designed antenna was predicted with computer simulation...
2017: Computational and Mathematical Methods in Medicine
Gul Zaman, Il H Jung, Delfim F M Torres, Anwar Zeb
No abstract text is available yet for this article.
2017: Computational and Mathematical Methods in Medicine
Kazuki Ide, Hiroshi Yonekura, Yohei Kawasaki, Koji Kawakami
To optimize delivery of health care services in clinical practice, the use of unnecessary interventions should be reduced. Although recommendations for this reduction have been accepted worldwide, recent studies have revealed that the use of such procedures continues to increase. We conducted a retrospective cohort study using a nationwide claim-based database to evaluate factors influencing preoperative blood testing prior to low-risk surgery, via a Bayesian generalized linear mixed approach. The study period was set from April 1, 2012, to March 31, 2016, and 69,252 surgeries performed at 9,922 institutions were included in the analysis...
2017: Computational and Mathematical Methods in Medicine
Rong Liu, Yongxuan Wang, Xinyu Wu, Jun Cheng
Obtaining a fast and reliable decision is an important issue in brain-computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this study, the EEG signals were firstly analyzed with a power projective base method. Then we were applied a decision-making model, the sequential probability ratio testing (SPRT), for single-trial classification of motor imagery movement events. The unique strength of this proposed classification method lies in its accumulative process, which increases the discriminative power as more and more evidence is observed over time...
2017: Computational and Mathematical Methods in Medicine
Vickie Shim, Andreas Gather, Andreas Höch, David Schreiber, Ronny Grunert, Steffen Peldschus, Christoph Josten, Jörg Böhme
[This corrects the article DOI: 10.1155/2017/9403821.].
2017: Computational and Mathematical Methods in Medicine
Shouliang Qi, Baihua Zhang, Yueyang Teng, Jianhua Li, Yong Yue, Yan Kang, Wei Qian
Using computational fluid dynamics (CFD) method, the feasibility of simulating transient airflow in a CT-based airway tree with more than 100 outlets for a whole respiratory period is studied, and the influence of truncations of terminal bronchi on CFD characteristics is investigated. After an airway model with 122 outlets is extracted from CT images, the transient airflow is simulated. Spatial and temporal variations of flow velocity, wall pressure, and wall shear stress are presented; the flow pattern and lobar distribution of air are gotten as well...
2017: Computational and Mathematical Methods in Medicine
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