AIMC Topic: Sensitivity and Specificity
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Depression recognition according to heart rate variability using Bayesian Networks.
Journal of psychiatric research
Sep 11, 2017
BACKGROUND: Doctors mainly use scale tests and subjective judgment in the clinical diagnosis of depression. Researches have demonstrated that depression is associated with the dysfunction of the autonomic nervous system (ANS), where its modulation ca...
Multi-label Inductive Matrix Completion for Joint MGMT and IDH1 Status Prediction for Glioma Patients.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Sep 4, 2017
MGMT promoter methylation and IDH1 mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditionally, the statuses of MGMT and IDH1 are obtained via surgical biopsy, which is...
Deformable Image Registration based on Similarity-Steered CNN Regression.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Sep 4, 2017
Existing deformable registration methods require exhaustively iterative optimization, along with careful parameter tuning, to estimate the deformation field between images. Although some learning-based methods have been proposed for initiating deform...
An artificial neural network to predict resting energy expenditure in obesity.
Clinical nutrition (Edinburgh, Scotland)
Sep 1, 2017
BACKGROUND & AIMS: The resting energy expenditure (REE) determination is important in nutrition for adequate dietary prescription. The gold standard i.e. indirect calorimetry is not available in clinical settings. Thus, several predictive equations h...
Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment.
Scientific reports
Aug 25, 2017
Rhegmatogenous retinal detachment (RRD) is a serious condition that can lead to blindness; however, it is highly treatable with timely and appropriate treatment. Thus, early diagnosis and treatment of RRD is crucial. In this study, we applied deep le...
Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images.
Journal of healthcare engineering
Aug 9, 2017
Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depe...
An Alzheimers disease related genes identification method based on multiple classifier integration.
Computer methods and programs in biomedicine
Aug 8, 2017
BACKGROUND AND OBJECTIVE: Alzheimers disease (AD) is a fatal neurodegenerative disease and the onset of AD is insidious. Full understanding of the AD-related genes (ADGs) has not been completed. The National Center for Biotechnology Information (NCBI...
Non-proliferative diabetic retinopathy symptoms detection and classification using neural network.
Journal of medical engineering & technology
Aug 8, 2017
Diabetic retinopathy (DR) causes blindness in the working age for people with diabetes in most countries. The increasing number of people with diabetes worldwide suggests that DR will continue to be major contributors to vision loss. Early detection ...
Disease Progression
Decision Support Systems, Clinical
Image Interpretation, Computer-Assisted
Pattern Recognition, Automated
Humans
Fluorescein Angiography
Symptom Assessment
Aged
Diabetic Retinopathy
Sensitivity and Specificity
Neural Networks, Computer
Male
Female
Middle Aged
Reproducibility of Results
An Evaluation of Artificial Neural Networks in Predicting Pancreatic Cancer Survival.
Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
Aug 3, 2017
OBJECTIVE: This study aims to evaluate the development of an artificial neural network (ANN) method for predicting the survival likelihood of pancreatic adenocarcinoma patients. The ANN predictive model should produce results with a 90% sensitivity.
Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences.
Medical image analysis
Aug 2, 2017
Dynamic contrast-enhanced MRI (DCE-MRI) is an imaging protocol where MRI scans are acquired repetitively throughout the injection of a contrast agent. The analysis of dynamic scans is widely used for the detection and quantification of blood-brain ba...
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