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Artificial Intelligence in Medicine | Page 2

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https://read.qxmd.com/read/31164208/dynamic-thresholding-networks-for-schizophrenia-diagnosis
#21
Hongliang Zou, Jian Yang
BACKGROUND AND OBJECTIVE: Functional connectivity (FC) based on resting-state functional magnetic resonance imaging (rs-fMRI) is an effective approach to describe the neural interaction between distributed brain regions. Recent progress in neuroimaging study reported that the connection between regions is time-varying, which may enhance understanding of normal cognition and alterations that result from brain disorders. However, conventional sliding window based dynamic FC (DFC) analysis has several drawbacks, including arbitrary choice of window length, inaccurate descriptor of FC, and the fact that many spurious connections were included in the fully-connected networks due to noise...
May 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/31164207/sparse-support-vector-machines-with-l-0-approximation-for-ultra-high-dimensional-omics-data
#22
Zhenqiu Liu, David Elashoff, Steven Piantadosi
Omics data usually have ultra-high dimension (p) and small sample size (n). Standard support vector machines (SVMs), which minimize the L2 norm for the primal variables, only lead to sparse solutions for the dual variables. L1 based SVMs, directly minimizing the L1 norm, have been used for feature selection with omics data. However, most current methods directly solve the primal formulations of the problem, which are not computationally scalable. The computational complexity increases with the number of features...
May 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/31164206/complexity-perception-classification-method-for-tongue-constitution-recognition
#23
Jiajiong Ma, Guihua Wen, Changjun Wang, Lijun Jiang
The body constitution is much related to the diseases and the corresponding treatment programs in Traditional Chinese Medicine. It can be recognized by the tongue image diagnosis, so that it is essentially regarded as a problem of tongue image classification, where each tongue image is classified into one of nine constitution types. This paper first presents a system framework to automatically identify the constitution through natural tongue images, where deep convolutional neural networks are carefully designed for tongue coating detection, tongue coating calibration, and constitution recognition...
May 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/31164205/reliability-based-robust-multi-atlas-label-fusion-for-brain-mri-segmentation
#24
Liang Sun, Chen Zu, Wei Shao, Junye Guang, Daoqiang Zhang, Mingxia Liu
Label fusion is one of the key steps in multi-atlas based segmentation of structural magnetic resonance (MR) images. Although a number of label fusion methods have been developed in literature, most of those existing methods fail to address two important problems, i.e., (1) compared with boundary voxels, inner voxels usually have higher probability (or reliability) to be correctly segmented, and (2) voxels with high segmentation reliability (after initial segmentation) can help refine the segmentation of voxels with low segmentation reliability in the target image...
May 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/31164204/neural-transfer-learning-for-assigning-diagnosis-codes-to-emrs
#25
Anthony Rios, Ramakanth Kavuluru
OBJECTIVE: Electronic medical records (EMRs) are manually annotated by healthcare professionals and specialized medical coders with a standardized set of alphanumeric diagnosis and procedure codes, specifically from the International Classification of Diseases (ICD). Annotating EMRs with ICD codes is important for medical billing and downstream epidemiological studies. However, manually annotating EMRs is both time-consuming and error prone. In this paper, we explore the use of convolutional neural networks (CNNs) for automatic ICD coding...
May 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/31164203/detection-of-protein-complexes-from-multiple-protein-interaction-networks-using-graph-embedding
#26
Xiaoxia Liu, Zhihao Yang, Shengtian Sang, Hongfei Lin, Jian Wang, Bo Xu
Cellular processes are typically carried out by protein complexes rather than individual proteins. Identifying protein complexes is one of the keys to understanding principles of cellular organization and function. Also, protein complexes are a group of interacting genes underlying similar diseases, which points out the therapeutic importance of protein complexes. With the development of life science and computing science, an increasing amount of protein-protein interaction (PPI) data becomes available, which makes it possible to predict protein complexes from PPI networks...
May 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/31164202/fast-density-peaks-clustering-for-registration-free-pediatric-white-matter-tract-analysis
#27
Xin Fan, Yuzhuo Duan, Shichao Cheng, Yuxi Zhang, Hua Cheng
Clustering white matter (WM) tracts from diffusion tensor imaging (DTI) is primarily important for quantitative analysis on pediatric brain development. A recently developed algorithm, density peaks (DP) clustering, demonstrates great robustness to the complex structural variations of WM tracts without any prior templates. Nevertheless, the calculation of densities, the core step of DP, is time consuming especially when the number of WM fibers is huge. In this paper, we propose a fast algorithm that accelerates the density computation about 50 times over the original one...
May 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/30904129/retinal-blood-vessel-extraction-employing-effective-image-features-and-combination-of-supervised-and-unsupervised-machine-learning-methods
#28
Mahdi Hashemzadeh, Baharak Adlpour Azar
In medicine, retinal vessel analysis of fundus images is a prominent task for the screening and diagnosis of various ophthalmological and cardiovascular diseases. In this research, a method is proposed for extracting the retinal blood vessels employing a set of effective image features and combination of supervised and unsupervised machine learning techniques. Further to the common features used in extracting blood vessels, three strong features having a significant influence on the accuracy of the vessel extraction are utilized...
April 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/30420244/computational-methods-for-gene-regulatory-networks-reconstruction-and-analysis-a-review
#29
REVIEW
Fernando M Delgado, Francisco Gómez-Vela
In the recent years, the vast amount of genetic information generated by new-generation approaches, have led to the need of new data handling methods. The integrative analysis of diverse-nature gene information could provide a much-sought overview to study complex biological systems and processes. In this sense, Gene Regulatory Networks (GRN) arise as an increasingly-promising tool for the modelling and analysis of biological processes. This review is an attempt to summarize the state of the art in the field of GRNs...
April 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/30871687/joint-segmentation-and-classification-of-retinal-arteries-veins-from-fundus-images
#30
Fantin Girard, Conrad Kavalec, Farida Cheriet
OBJECTIVE: Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. This paper proposes a fast, novel method for semantic A/V segmentation combining deep learning and graph propagation...
March 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/30871686/detection-of-abnormal-behaviour-for-dementia-sufferers-using-convolutional-neural-networks
#31
Damla Arifoglu, Abdelhamid Bouchachia
In recent years, there is a rapid increase in the population of elderly people. However, elderly people may suffer from the consequences of cognitive decline, which is a mental health disorder that primarily affects cognitive abilities such as learning, memory, etc. As a result, the elderly people may get dependent on caregivers to complete daily life tasks. Detecting the early indicators of dementia before it gets worsen and warning the caregivers and medical doctors would be helpful for further diagnosis...
March 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/30871685/predicting-lab-values-for-gastrointestinal-bleeding-patients-in-the-intensive-care-unit-a-comparative-study-on-the-impact-of-comorbidities-and-medications
#32
Golnar K Mahani, Mohammad-Reza Pajoohan
Since a significant number of frequent laboratory blood tests are unnecessary and these tests may have complications, developing a system that could identify unnecessary tests is essential. In this paper, a value prediction approach is presented to predict the values of Calcium and Hematocrit laboratory blood tests for upper gastrointestinal bleeding patients and patients with unspecified hemorrhage in their gastrointestinal tract. The data have been extracted from the MIMIC-II database. By considering the issues of MIMIC-II in the process of data extraction and using expert knowledge, comprehensive preprocessing has been performed to validate the data...
March 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/30871684/active-contour-algorithm-with-discriminant-analysis-for-delineating-tumors-in-positron-emission-tomography
#33
Albert Comelli, Alessandro Stefano, Samuel Bignardi, Giorgio Russo, Maria Gabriella Sabini, Massimo Ippolito, Stefano Barone, Anthony Yezzi
In the context of cancer delineation using positron emission tomography datasets, we present an innovative approach which purpose is to tackle the real-time, three-dimensional segmentation task in a full, or at least nearly full automatized way. The approach comprises a preliminary initialization phase where the user highlights a region of interest around the cancer on just one slice of the tomographic dataset. The algorithm takes care of identifying an optimal and user-independent region of interest around the anomalous tissue and located on the slice containing the highest standardized uptake value so to start the successive segmentation task...
March 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/30871683/normal-and-pathological-gait-classification-lstm-model
#34
Margarita Khokhlova, Cyrille Migniot, Alexey Morozov, Olga Sushkova, Albert Dipanda
Computer vision-based clinical gait analysis is the subject of permanent research. However, there are very few datasets publicly available; hence the comparison of existing methods between each other is not straightforward. Even if the test data are in an open access, existing databases contain very few test subjects and single modality measurements, which limit their usage. The contributions of this paper are three-fold. First, we propose a new open-access multi-modal database acquired with the Kinect v.2 camera for the task of gait analysis...
March 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/30871682/explainable-artificial-intelligence-for-breast-cancer-a-visual-case-based-reasoning-approach
#35
Jean-Baptiste Lamy, Boomadevi Sekar, Gilles Guezennec, Jacques Bouaud, Brigitte Séroussi
Case-Based Reasoning (CBR) is a form of analogical reasoning in which the solution for a (new) query case is determined using a database of previous known cases with their solutions. Cases similar to the query are retrieved from the database, and then their solutions are adapted to the query. In medicine, a case usually corresponds to a patient and the problem consists of classifying the patient in a class of diagnostic or therapy. Compared to "black box" algorithms such as deep learning, the responses of CBR systems can be justified easily using the similar cases as examples...
March 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/30871681/antigenic-an-improved-prediction-model-of-protective-antigens
#36
M Saifur Rahman, Md Khaledur Rahman, Sanjay Saha, M Kaykobad, M Sohel Rahman
An antigen is a protein capable of triggering an effective immune system response. Protective antigens are the ones that can invoke specific and enhanced adaptive immune response to subsequent exposure to the specific pathogen or related organisms. Such proteins are therefore of immense importance in vaccine preparation and drug design. However, the laboratory experiments to isolate and identify antigens from a microbial pathogen are expensive, time consuming and often unsuccessful. This is why Reverse Vaccinology has become the modern trend of vaccine search, where computational methods are first applied to predict protective antigens or their determinants, known as epitopes...
March 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/30871680/a-frame-reduction-system-based-on-a-color-structural-similarity-css-method-and-bayer-images-analysis-for-capsule-endoscopy
#37
Qasim Al-Shebani, Prashan Premaratne, Darryl J McAndrew, Peter J Vial, Shehan Abey
A capsule endoscopy examination of the human small bowel generates a large number of images that have high similarity. In order to reduce the time it takes to review the high similarity images, clinicians will increase the playback speed, typically to 15 frames per second [1]. Associated with this behaviour is an increased probability of overlooking an image that may contain an abnormality. An alternative option to increasing the playback speed is the application of abnormality detection systems to detect abnormalities such as ulcers, tumors, polyps and bleeding...
March 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/30871679/using-classification-techniques-for-statistical-analysis-of-anemia
#38
Kanak Meena, Devendra K Tayal, Vaidehi Gupta, Aiman Fatima
Anemia in children is becoming a worldwide problem owing to the unawareness among people regarding the disease, its causes and preventive measures. This study develops a decision support system using data mining techniques that are applied to a database containing data about nutritional factors for children. The data set was taken from NFHS-4, a survey conducted by the Government of India in 2015-16. The work attempts to predict anemia among children and establish a relation between mother's health and diet during pregnancy and its effects on anemic status of her child...
March 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/30871678/execution-time-integration-of-clinical-practice-guidelines-to-provide-decision-support-for-comorbid-conditions
#39
Borna Jafarpour, Samina Raza Abidi, William Van Woensel, Syed Sibte Raza Abidi
Patients with multiple medical conditions (comorbidity) pose major challenges to clinical decision support systems, since the different Clinical Practice Guidelines (CPG) often involve adverse interactions, such as drug-drug or drug-disease interactions. Moreover, opportunities often exist for optimizing care and resources across multiple CPG. These challenges have been taken up in the state of the art, with many approaches focusing on the static integration of comorbid CIG. Nevertheless, we observe that many aspects often change dynamically over time, in ways that cannot be foreseen - such as delays in care tasks, resource availability, test outcomes, and acute comorbid conditions...
March 2019: Artificial Intelligence in Medicine
https://read.qxmd.com/read/30871677/machine-learning-models-based-on-the-dimensionality-reduction-of-standard-automated-perimetry-data-for-glaucoma-diagnosis
#40
Su-Dong Lee, Ji-Hyung Lee, Young-Geun Choi, Hee-Cheon You, Ja-Heon Kang, Chi-Hyuck Jun
INTRODUCTION: Visual field testing via standard automated perimetry (SAP) is a commonly used glaucoma diagnosis method. Applying machine learning techniques to the visual field test results, a valid clinical diagnosis of glaucoma solely based on the SAP data is provided. In order to reflect structural-functional patterns of glaucoma on the automated diagnostic models, we propose composite variables derived from anatomically grouped visual field clusters to improve the prediction performance...
March 2019: Artificial Intelligence in Medicine
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