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https://www.readbyqxmd.com/read/28542342/development-of-machine-learning-models-for-diagnosis-of-glaucoma
#1
Seong Jae Kim, Kyong Jin Cho, Sejong Oh
The study aimed to develop machine learning models that have strong prediction power and interpretability for diagnosis of glaucoma based on retinal nerve fiber layer (RNFL) thickness and visual field (VF). We collected various candidate features from the examination of retinal nerve fiber layer (RNFL) thickness and visual field (VF). We also developed synthesized features from original features. We then selected the best features proper for classification (diagnosis) through feature evaluation. We used 100 cases of data as a test dataset and 399 cases of data as a training and validation dataset...
2017: PloS One
https://www.readbyqxmd.com/read/28542318/predicting-congenital-heart-defects-a-comparison-of-three-data-mining-methods
#2
Yanhong Luo, Zhi Li, Husheng Guo, Hongyan Cao, Chunying Song, Xingping Guo, Yanbo Zhang
Congenital heart defects (CHD) is one of the most common birth defects in China. Many studies have examined risk factors for CHD, but their predictive abilities have not been evaluated. In particular, few studies have attempted to predict risks of CHD from, necessarily unbalanced, population-based cross-sectional data. Therefore, we developed and validated machine learning models for predicting, before and during pregnancy, women's risks of bearing children with CHD. We compared the results of these models in a large-scale, comprehensive population-based retrospective cross-sectional epidemiological survey of birth defects in six counties in Shanxi Province, China, covering 2006 to 2008...
2017: PloS One
https://www.readbyqxmd.com/read/28541917/an-unobtrusive-computerized-assessment-framework-for-unilateral-peripheral-facial-paralysis
#3
Zhe-Xiao Guo, Guo Dan, Jianghuai Xiang, Jun Wang, Wanzhang Yang, Huijun Ding, Oliver Deussen, Yongjin Zhou
Unilateral peripheral facial paralysis (UPFP) is a form of facial nerve paralysis and clinically classified according to conditions of facial symmetry. Prompt and precise assessment is crucial to neural rehabilitation of UPFP. The prevalent House-Brackmann (HB) grading system relies on subjective judgments with significant inter-observation variation. Therefore to explore an objective method for UPFP assessment, clinical image sequences are captured using a web camera setup while 5 healthy and 27 UPFP subjects performing a group of pre-defined actions, including keeping expressionless, raising brows, closing eyes, bulging cheek and showing teeth in turn...
May 24, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28541202/linear-support-tensor-machine-with-lsk-channels-pedestrian-detection-in-thermal-infrared-images
#4
Sujoy Kumar Biswas, Peyman Milanfar
Pedestrian detection in thermal infrared images poses unique challenges because of the low resolution and noisy nature of the image. Here we propose a mid-level attribute in the form of the multidimensional template, or tensor, using Local Steering Kernel (LSK) as low-level descriptors for detecting pedestrians in far infrared images. LSK is specifically designed to deal with intrinsic image noise and pixel level uncertainty by capturing local image geometry succinctly instead of collecting local orientation statistics (e...
May 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28540682/the-limitations-of-existing-approaches-in-improving-microrna-target-prediction-accuracy
#5
Rasiah Loganantharaj, Thomas A Randall
MicroRNAs (miRNAs) are small (18-24 nt) endogenous RNAs found across diverse phyla involved in posttranscriptional regulation, primarily downregulation of mRNAs. Experimentally determining miRNA-mRNA interactions can be expensive and time-consuming, making the accurate computational prediction of miRNA targets a high priority. Since miRNA-mRNA base pairing in mammals is not perfectly complementary and only a fraction of the identified motifs are real binding sites, accurately predicting miRNA targets remains challenging...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28539587/component-analysis-of-somatosensory-evoked-potentials-for-identifying-spinal-cord-injury-location
#6
Yazhou Wang, Guangsheng Li, Keith D K Luk, Yong Hu
This study aims to determine whether the time-frequency components (TFCs) of somatosensory evoked potentials (SEPs) can be used to identify the specific location of a compressive spinal cord injury using a classification technique. Waveforms of SEPs after compressive injuries at various locations (C4, C5 and C6) in rat spinal cords were decomposed into a series of TFCs using a high-resolution time-frequency analysis method. A classification method based on support vector machine (SVM) was applied to the distributions of these TFCs among different pathological locations...
May 24, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28539063/a-qsar-classification-model-for-neuraminidase-inhibitors-of-influenza-a-viruses-h1n1-based-on-weighted-penalized-support-vector-machine
#7
Z Y Algamal, M K Qasim, H T M Ali
Descriptor selection is a procedure widely used in chemometrics. The aim is to select the best subset of descriptors relevant to the quantitative structure-activity relationship (QSAR) study being considered. In this paper, a new descriptor selection method for the QSAR classification model is proposed by adding a new weight inside L1-norm. The experimental results from classifying the neuraminidase inhibitors of influenza A viruses (H1N1) demonstrate that the proposed method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance and the number of selected descriptors...
May 25, 2017: SAR and QSAR in Environmental Research
https://www.readbyqxmd.com/read/28537025/prediction-of-dissolved-oxygen-concentration-in-hypoxic-river-systems-using-support-vector-machine-a-case-study-of-wen-rui-tang-river-china
#8
Xiaoliang Ji, Xu Shang, Randy A Dahlgren, Minghua Zhang
Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China...
May 23, 2017: Environmental Science and Pollution Research International
https://www.readbyqxmd.com/read/28534798/alzheimer-s-disease-diagnosis-using-landmark-based-features-from-longitudinal-structural-mr-images
#9
Jun Zhang, Mingxia Liu, Le An, Yaozong Gao, Dinggang Shen
Structural magnetic resonance imaging (MRI) has been proven to be an effective tool for Alzheimer's disease (AD) diagnosis. While conventional MRI-based AD diagnosis typically uses images acquired at a single time point, a longitudinal study is more sensitive in detecting early pathological changes of AD, making it more favorable for accurate diagnosis. In general, there are two challenges faced in MRI-based diagnosis. First, extracting features from structural MR images requires timeconsuming nonlinear registration and tissue segmentation, whereas the longitudinal study with involvement of more scans further exacerbates the computational costs...
May 16, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28533960/body-composition-estimation-from-selected-slices-equations-computed-from-a-new-semi-automatic-thresholding-method-developed-on-whole-body-ct%C3%A2-scans
#10
Alizé Lacoste Jeanson, Ján Dupej, Chiara Villa, Jaroslav Brůžek
BACKGROUND: Estimating volumes and masses of total body components is important for the study and treatment monitoring of nutrition and nutrition-related disorders, cancer, joint replacement, energy-expenditure and exercise physiology. While several equations have been offered for estimating total body components from MRI slices, no reliable and tested method exists for CT scans. For the first time, body composition data was derived from 41 high-resolution whole-body CT scans. From these data, we defined equations for estimating volumes and masses of total body AT and LT from corresponding tissue areas measured in selected CT scan slices...
2017: PeerJ
https://www.readbyqxmd.com/read/28530547/application-of-machine-learning-approaches-for-protein-protein-interactions-prediction
#11
Mengying Zhang, Qiang Su, Yi Lu, Manman Zhao, Bing Niu
BACKGROUND: Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. OBJECTIVE: In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed...
May 22, 2017: Medicinal Chemistry
https://www.readbyqxmd.com/read/28529761/developing-and-evaluating-a-mobile-driver-fatigue-detection-network-based-on-electroencephalograph-signals
#12
Jinghai Yin, Jianfeng Hu, Zhendong Mu
The rapid development of driver fatigue detection technology indicates important significance of traffic safety. The authors' main goals of this Letter are principally three: (i) A middleware architecture, defined as process unit (PU), which can communicate with personal electroencephalography (EEG) node (PEN) and cloud server (CS). The PU receives EEG signals from PEN, recognises the fatigue state of the driver, and transfer this information to CS. The CS sends notification messages to the surrounding vehicles...
February 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28529759/high-frequency-based-features-for-low-and-high-retina-haemorrhage-classification
#13
Salim Lahmiri
Haemorrhages (HAs) presence in fundus images is one of the most important indicators of diabetic retinopathy that causes blindness. In this regard, accurate grading of HAs in fundus images is crucial for appropriate medical treatment. The purpose of this Letter is to assess the relative performance of statistical features obtained with three different multi-resolution analysis (MRA) techniques and fed to support vector machine in grading retinal HAs. Considered MRA techniques are the common discrete wavelet transform (DWT), empirical mode decomposition (EMD), and variational mode decomposition (VMD)...
February 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28529536/mid-infrared-spectroscopy-combined-with-chemometrics-to-detect-sclerotinia-stem-rot-on-oilseed-rape-brassica-napus-l-leaves
#14
Chu Zhang, Xuping Feng, Jian Wang, Fei Liu, Yong He, Weijun Zhou
BACKGROUND: Detection of plant diseases in a fast and simple way is crucial for timely disease control. Conventionally, plant diseases are accurately identified by DNA, RNA or serology based methods which are time consuming, complex and expensive. Mid-infrared spectroscopy is a promising technique that simplifies the detection procedure for the disease. Mid-infrared spectroscopy was used to identify the spectral differences between healthy and infected oilseed rape leaves. Two different sample sets from two experiments were used to explore and validate the feasibility of using mid-infrared spectroscopy in detecting Sclerotinia stem rot (SSR) on oilseed rape leaves...
2017: Plant Methods
https://www.readbyqxmd.com/read/28526882/prediction-of-cognitive-and-motor-outcome-of-preterm-infants-based-on-automatic-quantitative-descriptors-from-neonatal-mr-brain-images
#15
Pim Moeskops, Ivana Išgum, Kristin Keunen, Nathalie H P Claessens, Ingrid C van Haastert, Floris Groenendaal, Linda S de Vries, Max A Viergever, Manon J N L Benders
This study investigates the predictive ability of automatic quantitative brain MRI descriptors for the identification of infants with low cognitive and/or motor outcome at 2-3 years chronological age. MR brain images of 173 patients were acquired at 30 weeks postmenstrual age (PMA) (n = 86) and 40 weeks PMA (n = 153) between 2008 and 2013. Eight tissue volumes and measures of cortical morphology were automatically computed. A support vector machine classifier was employed to identify infants who exhibit low cognitive and/or motor outcome (<85) at 2-3 years chronological age as assessed by the Bayley scales...
May 19, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28526841/organic-carbon-prediction-in-soil-cores-using-vnir-and-mir-techniques-in-an-alpine-landscape
#16
Xiaolin Jia, Songchao Chen, Yuanyuan Yang, Lianqing Zhou, Wu Yu, Zhou Shi
Diffuse reflectance spectroscopy (DRS), including visible and near-infrared (VNIR) and mid-infrared (MIR) radiation, is a rapid, accurate and cost-effective technique for estimating soil organic carbon (SOC). We examined 24 soil cores (0-100 cm) from the Sygera Mountains on the Qinghai-Tibet Plateau, considering field-moist intact VNIR, air-dried ground VNIR and air-dried ground MIR spectra at 5-cm intervals. Preprocessed spectra were used to predict the SOC in the soil cores using partial least squares regression (PLSR) and a support vector machine (SVM)...
May 19, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28523350/comparison-of-a-radiomic-biomarker-with-volumetric-analysis-for-decoding-tumour-phenotypes-of-lung-adenocarcinoma-with-different-disease-specific-survival
#17
Mei Yuan, Yu-Dong Zhang, Xue-Hui Pu, Yan Zhong, Hai Li, Jiang-Fen Wu, Tong-Fu Yu
OBJECTIVES: To compare a multi-feature-based radiomic biomarker with volumetric analysis in discriminating lung adenocarcinomas with different disease-specific survival on computed tomography (CT) scans. METHODS: This retrospective study obtained institutional review board approval and was Health Insurance Portability and Accountability Act (HIPAA) compliant. Pathologically confirmed lung adenocarcinoma (n = 431) manifested as subsolid nodules on CT were identified...
May 18, 2017: European Radiology
https://www.readbyqxmd.com/read/28521242/a-structured-latent-model-for-ovarian-carcinoma-subtyping-from-histopathology-slides
#18
Aïcha BenTaieb, Hector Li-Chang, David Huntsman, Ghassan Hamarneh
Accurate subtyping of ovarian carcinomas is an increasingly critical and often challenging diagnostic process. This work focuses on the development of an automatic classification model for ovarian carcinoma subtyping. Specifically, we present a novel clinically inspired contextual model for histopathology image subtyping of ovarian carcinomas. A whole slide image is modelled using a collection of tissue patches extracted at multiple magnifications. An efficient and effective feature learning strategy is used for feature representation of a tissue patch...
May 9, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28520730/analysis-of-heterogeneity-in-t2-weighted-mr-images-can-differentiate-pseudoprogression-from-progression-in-glioblastoma
#19
Thomas C Booth, Timothy J Larkin, Yinyin Yuan, Mikko I Kettunen, Sarah N Dawson, Daniel Scoffings, Holly C Canuto, Sarah L Vowler, Heide Kirschenlohr, Michael P Hobson, Florian Markowetz, Sarah Jefferies, Kevin M Brindle
PURPOSE: To develop an image analysis technique that distinguishes pseudoprogression from true progression by analyzing tumour heterogeneity in T2-weighted images using topological descriptors of image heterogeneity called Minkowski functionals (MFs). METHODS: Using a retrospective patient cohort (n = 50), and blinded to treatment response outcome, unsupervised feature estimation was performed to investigate MFs for the presence of outliers, potential confounders, and sensitivity to treatment response...
2017: PloS One
https://www.readbyqxmd.com/read/28515267/presurgical-thalamic-hubness-predicts-surgical-outcome-in-temporal-lobe-epilepsy
#20
Xiaosong He, Gaelle E Doucet, Dorian Pustina, Michael R Sperling, Ashwini D Sharan, Joseph I Tracy
OBJECTIVE: To characterize the presurgical brain functional architecture presented in patients with temporal lobe epilepsy (TLE) using graph theoretical measures of resting-state fMRI data and to test its association with surgical outcome. METHODS: Fifty-six unilateral patients with TLE, who subsequently underwent anterior temporal lobectomy and were classified as obtaining a seizure-free (Engel class I, n = 35) vs not seizure-free (Engel classes II-IV, n = 21) outcome at 1 year after surgery, and 28 matched healthy controls were enrolled...
May 17, 2017: Neurology
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