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https://www.readbyqxmd.com/read/27925583/modeling-electrode-place-discrimination-in-cochlear-implant-stimulation
#1
Xiao Gao, David Bruce Grayden, Mark D McDonnell
OBJECTIVE: By modeling the cochlear implant (CI) electrode-to-nerve interface and quantifying electrode discriminability in the model, we address the questions of how many individual channels can be distinguished by CI recipients and the extent to which performance might be improved by inserting electrodes deeper into the cochlea. METHOD: We adapt an artificial neural network to model electrode discrimination, as well as a commonly used psychophysical measure (four-interval forcedchoice) in CI stimulation and predict how well the locations of the stimulating electrodes can be inferred from simulated auditory nerve spiking patterns...
December 1, 2016: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/27922974/training-and-validating-a-deep-convolutional-neural-network-for-computer-aided-detection-and-classification-of-abnormalities-on-frontal-chest-radiographs
#2
Mark Cicero, Alexander Bilbily, Errol Colak, Tim Dowdell, Bruce Gray, Kuhan Perampaladas, Joseph Barfett
OBJECTIVES: Convolutional neural networks (CNNs) are a subtype of artificial neural network that have shown strong performance in computer vision tasks including image classification. To date, there has been limited application of CNNs to chest radiographs, the most frequently performed medical imaging study. We hypothesize CNNs can learn to classify frontal chest radiographs according to common findings from a sufficiently large data set. MATERIALS AND METHODS: Our institution's research ethics board approved a single-center retrospective review of 35,038 adult posterior-anterior chest radiographs and final reports performed between 2005 and 2015 (56% men, average age of 56, patient type: 24% inpatient, 39% outpatient, 37% emergency department) with a waiver for informed consent...
December 5, 2016: Investigative Radiology
https://www.readbyqxmd.com/read/27920762/artificial-intelligence-vs-statistical-modeling-and-optimization-of-continuous-bead-milling-process-for-bacterial-cell-lysis
#3
Shafiul Haque, Saif Khan, Mohd Wahid, Sajad A Dar, Nipunjot Soni, Raju K Mandal, Vineeta Singh, Dileep Tiwari, Mohtashim Lohani, Mohammed Y Areeshi, Thavendran Govender, Hendrik G Kruger, Arshad Jawed
For a commercially viable recombinant intracellular protein production process, efficient cell lysis and protein release is a major bottleneck. The recovery of recombinant protein, cholesterol oxidase (COD) was studied in a continuous bead milling process. A full factorial response surface methodology (RSM) design was employed and compared to artificial neural networks coupled with genetic algorithm (ANN-GA). Significant process variables, cell slurry feed rate (A), bead load (B), cell load (C), and run time (D), were investigated and optimized for maximizing COD recovery...
2016: Frontiers in Microbiology
https://www.readbyqxmd.com/read/27919554/quantitative-structure-property-relationships-for-predicting-sorption-of-pharmaceuticals-to-sewage-sludge-during-waste-water-treatment-processes
#4
L Berthod, D C Whitley, G Roberts, A Sharpe, R Greenwood, G A Mills
Understanding the sorption of pharmaceuticals to sewage sludge during waste water treatment processes is important for understanding their environmental fate and in risk assessments. The degree of sorption is defined by the sludge/water partition coefficient (Kd). Experimental Kd values (n=297) for active pharmaceutical ingredients (n=148) in primary and activated sludge were collected from literature. The compounds were classified by their charge at pH7.4 (44 uncharged, 60 positively and 28 negatively charged, and 16 zwitterions)...
December 2, 2016: Science of the Total Environment
https://www.readbyqxmd.com/read/27919382/a-clinical-decision-support-system-for-prediction-of-pregnancy-outcome-in-pregnant-women-with-systemic-lupus-erythematosus
#5
Khadijeh Paydar, Sharareh R Niakan Kalhori, Mahmoud Akbarian, Abbas Sheikhtaheri
OBJECTIVE: Pregnancy among systemic lupus erythematosus (SLE)-affected women is highly associated with poor obstetric outcomes. Predicting the risk of foetal outcome is essential for maximizing the success of pregnancy. This study aimed to develop a clinical decision support system (CDSS) to predict pregnancy outcomes among SLE-affected pregnant women. METHODS: We performed a retrospective analysis of 149 pregnant women with SLE, who were followed at Shariati Hospital (104 pregnancies) and a specialized clinic (45 pregnancies) from 1982 to 2014...
January 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/27919375/software-intelligent-system-for-effective-solutions-for-hearing-impaired-subjects
#6
Rajkumar S, Muttan S, Sapthagirivasan V, Jaya V, Vignesh S S
PURPOSE: The anatomy and physiology of the ear is complex in nature, which makes it a challenge for audiologists to prescribe solutions for varied hearing-impaired subjects. There is a need to increase the satisfaction level of hearing-aid users by adopting better strategies that involve modern technological advancements. AIM: To design and develop a decision support Software Intelligent System (SIS) that performs audiological investigations to assess the degree of hearing loss and to suggest appropriate hearing-aid gain values...
January 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/27916817/real-time-classification-of-patients-with-balance-disorders-vs-normal-subjects-using-a-low-cost-small-wireless-wearable-gait-sensor
#7
Bhargava Teja Nukala, Taro Nakano, Amanda Rodriguez, Jerry Tsay, Jerry Lopez, Tam Q Nguyen, Steven Zupancic, Donald Y C Lie
Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU) was used to collect the gait data for four patients diagnosed with balance disorders and additionally three normal subjects, each performing the Dynamic Gait Index (DGI) tests while wearing the custom wireless gait analysis sensor (WGAS)...
November 29, 2016: Biosensors
https://www.readbyqxmd.com/read/27915125/artificial-neural-networks-as-a-powerful-numerical-tool-to-classify-specific-features-of-a-tooth-based-on-3d-scan-data
#8
Stefan Raith, Eric Per Vogel, Naeema Anees, Christine Keul, Jan-Frederik Güth, Daniel Edelhoff, Horst Fischer
Chairside manufacturing based on digital image acquisition is gainingincreasing importance in dentistry. For the standardized application of these methods, it is paramount to have highly automated digital workflows that can process acquired 3D image data of dental surfaces. Artificial Neural Networks (ANNs) arenumerical methods primarily used to mimic the complex networks of neural connections in the natural brain. Our hypothesis is that an ANNcan be developed that is capable of classifying dental cusps with sufficient accuracy...
November 27, 2016: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/27910759/acidified-hot-water-extraction-of-adenosine-cordycepin-and-polysaccharides-from-the-chinese-caterpillar-mushroom-ophiocordyceps-sinensis-cs1197-ascomycetes-application-of-an-artificial-neural-network-and-evaluation-of-antioxidant-and-antibacterial-activities
#9
M G Shashidhar, B Manohar
The effects of process variables (temperature, time, and pH) on the extraction of adenosine, cordycepin, and polysaccharides from Ophiocordyceps sinensis CS1197 were studied using response surface methodology (RSM) and an artificial neural network (ANN). The ANN model resulted in low root mean square errors (0.022, 0.079, 0.018) and high R2 values (0.995, 0.934, 0.997) for adenosine, cordycepin, and polysaccharide yields, respectively, which implied good agreement between the predicted and actual data. An overall desirability of 0...
2016: International Journal of Medicinal Mushrooms
https://www.readbyqxmd.com/read/27907231/use-of-autopsy-to-determine-live-or-stillbirth-new-approaches-in-decision-support-systems
#10
Riza Yilmaz, Okan Erkaymaz, Erdogan Kara, Kivanc Ergen
Fetal deaths are important cases for forensic medicine, as well as for criminal and civil law. From a legal perspective, the determination of whether a deceased infant was stillborn is a difficult process. Such a determination is generally made during autopsy; however, no standardized procedures for this determination exist. Therefore, new facilitative approaches are needed. In this study, three new support systems based on 10 autopsy parameters were tested for their ability to correctly determine whether deceased infants were alive or stillborn...
December 1, 2016: Journal of Forensic Sciences
https://www.readbyqxmd.com/read/27899332/artificial-neural-network-for-suppression-of-banding-artifacts-in-balanced-steady-state-free-precession-mri
#11
Ki Hwan Kim, Sung-Hong Park
The balanced steady-state free precession (bSSFP) MR sequence is frequently used in clinics, but is sensitive to off-resonance effects, which can cause banding artifacts. Often multiple bSSFP datasets are acquired at different phase cycling (PC) angles and then combined in a special way for banding artifact suppression. Many strategies of combining the datasets have been suggested for banding artifact suppression, but there are still limitations in their performance, especially when the number of phase-cycled bSSFP datasets is small...
November 27, 2016: Magnetic Resonance Imaging
https://www.readbyqxmd.com/read/27894042/investigation-of-co-combustion-characteristics-of-sewage-sludge-and-coffee-grounds-mixtures-using-thermogravimetric-analysis-coupled-to-artificial-neural-networks-modeling
#12
Jiacong Chen, Jingyong Liu, Yao He, Limao Huang, Shuiyu Sun, Jian Sun, KenLin Chang, Jiahong Kuo, Shaosong Huang, Xunan Ning
Artificial neural network (ANN) modeling was applied to thermal data obtained by non-isothermal thermogravimetric analysis (TGA) from room temperature to 1000°C at three different heating rates in air to predict the TG curves of sewage sludge (SS) and coffee grounds (CG) mixtures. A good agreement between experimental and predicted data verified the accuracy of the ANN approach. The results of co-combustion showed that there were interactions between SS and CG, and the impacts were mostly positive. With the addition of CG, the mass loss rate and the reactivity of SS were increased while charring was reduced...
November 18, 2016: Bioresource Technology
https://www.readbyqxmd.com/read/27892600/artificial-neural-networks-and-geometric-morphometric-methods-as-a-means-for-classification-a-case-study-using-teeth-from-carcharhinus-sp-carcharhinidae
#13
K J Soda, D E Slice, G J P Naylor
Over the past few decades, geometric morphometric methods have become increasingly popular and powerful tools to describe morphological data while over the same period artificial neural networks have had a similar rise in the classification of specimens to preconceived groups. However, there has been little research into how well these two systems operate together, particularly in comparison to preexisting techniques. In this study, geometric morphometric data and multilayer perceptrons, a style of artificial neural network, were used to classify shark teeth from the genus Carcharhinus to species...
November 28, 2016: Journal of Morphology
https://www.readbyqxmd.com/read/27890153/the-arm-force-field-method-to-predict-manual-arm-strength-based-on-only-hand-location-and-force-direction
#14
Nicholas J La Delfa, Jim R Potvin
This paper describes the development of a novel method (termed the 'Arm Force Field' or 'AFF') to predict manual arm strength (MAS) for a wide range of body orientations, hand locations and any force direction. This method used an artificial neural network (ANN) to predict the effects of hand location and force direction on MAS, and included a method to estimate the contribution of the arm's weight to the predicted strength. The AFF method predicted the MAS values very well (r(2) = 0.97, RMSD = 5.2 N, n = 456) and maintained good generalizability with external test data (r(2) = 0...
March 2017: Applied Ergonomics
https://www.readbyqxmd.com/read/27890144/classification-of-a-driver-s-cognitive-workload-levels-using-artificial-neural-network-on-ecg-signals
#15
Amir Tjolleng, Kihyo Jung, Wongi Hong, Wonsup Lee, Baekhee Lee, Heecheon You, Joonwoo Son, Seikwon Park
An artificial neural network (ANN) model was developed in the present study to classify the level of a driver's cognitive workload based on electrocardiography (ECG). ECG signals were measured on 15 male participants while they performed a simulated driving task as a primary task with/without an N-back task as a secondary task. Three time-domain ECG measures (mean inter-beat interval (IBI), standard deviation of IBIs, and root mean squared difference of adjacent IBIs) and three frequencydomain ECG measures (power in low frequency, power in high frequency, and ratio of power in low and high frequencies) were calculated...
March 2017: Applied Ergonomics
https://www.readbyqxmd.com/read/27884247/machine-learning-algorithms-to-automate-morphological-and-functional-assessments-in-2d-echocardiography
#16
Sukrit Narula, Khader Shameer, Alaa Mabrouk Salem Omar, Joel T Dudley, Partho P Sengupta
BACKGROUND: Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation. OBJECTIVES: This study investigated the diagnostic value of a machine-learning framework that incorporates speckle-tracking echocardiographic data for automated discrimination of hypertrophic cardiomyopathy (HCM) from physiological hypertrophy seen in athletes (ATH). METHODS: Expert-annotated speckle-tracking echocardiographic datasets obtained from 77 ATH and 62 HCM patients were used for developing an automated system...
November 29, 2016: Journal of the American College of Cardiology
https://www.readbyqxmd.com/read/27876537/artificial-neural-networks-approach-to-pharmacokinetic-model-selection-in-dce-mri-studies
#17
Mohammad-Reza Mohammadian-Behbahani, Ali-Reza Kamali-Asl
PURPOSE: In pharmacokinetic analysis of Dynamic Contrast Enhanced MRI data, a descriptive physiological model should be selected properly out of a set of candidate models. Classical techniques suggested for this purpose suffer from issues like computation time and general fitting problems. This article proposes an approach based on Artificial Neural Networks (ANNs) for solving these problems. METHODS: A set of three physiologically and mathematically nested models generated from the Tofts model were assumed: Model I, II and III...
November 19, 2016: Physica Medica: PM
https://www.readbyqxmd.com/read/27876174/artificial-neural-networks-predicting-head-ct-findings-in-elderly-patients-presenting-with-minor-head-injury-after-a-fall
#18
Michael W Dusenberry, Charles K Brown, Kori L Brewer
OBJECTIVES: To construct an artificial neural network (ANN) model that can predict the presence of acute CT findings with both high sensitivity and high specificity when applied to the population of patients≥age 65years who have incurred minor head injury after a fall. METHODS: An ANN was created in the Python programming language using a population of 514 patients ≥ age 65 years presenting to the ED with minor head injury after a fall. The patient dataset was divided into three parts: 60% for "training", 20% for "cross validation", and 20% for "testing"...
November 3, 2016: American Journal of Emergency Medicine
https://www.readbyqxmd.com/read/27876006/predictors-of-in-hospital-mortality-following-major-lower-extremity-amputations-in-type-2-diabetic-patients-using-artificial-neural-networks
#19
Ana Lopez-de-Andres, Valentin Hernandez-Barrera, Roberto Lopez, Pablo Martin-Junco, Isabel Jimenez-Trujillo, Alejandro Alvaro-Meca, Miguel Angel Salinero-Fort, Rodrigo Jimenez-Garcia
BACKGROUND: Outcome prediction is important in the clinical decision-making process. Artificial neural networks (ANN) have been used to predict the risk of post-operative events, including survival, and are increasingly being used in complex medical decision making. We aimed to use ANN analysis to estimate predictive factors of in-hospital mortality (IHM) in patients with type 2 diabetes (T2DM) after major lower extremity amputation (LEA) in Spain. METHODS: We design a retrospective, observational study using ANN models...
November 22, 2016: BMC Medical Research Methodology
https://www.readbyqxmd.com/read/27875137/visualizing-the-hidden-activity-of-artificial-neural-networks
#20
Paulo E Rauber, Samuel G Fadel, Alexandre X Falcao, Alexandru C Telea
In machine learning, pattern classification assigns high-dimensional vectors (observations) to classes based on generalization from examples. Artificial neural networks currently achieve state-of-the-art results in this task. Although such networks are typically used as black-boxes, they are also widely believed to learn (high-dimensional) higher-level representations of the original observations. In this paper, we propose using dimensionality reduction for two tasks: visualizing the relationships between learned representations of observations, and visualizing the relationships between artificial neurons...
January 2017: IEEE Transactions on Visualization and Computer Graphics
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