AIMC Topic: Sensitivity and Specificity
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Development of an artificial intelligence system to classify pathology and clinical features on retinal fundus images.
Clinical & experimental ophthalmology
Nov 15, 2018
IMPORTANCE: Artificial intelligence (AI) algorithms are under development for use in diabetic retinopathy photo screening pathways. To be clinically acceptable, such systems must also be able to classify other fundus abnormalities and clinical featur...
A machine learning approach for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted MRI parameters.
European journal of radiology
Nov 13, 2018
PURPOSE: To propose a computer-assisted method for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted magnetic resonance imaging (PWI).
Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage.
Journal of neurointerventional surgery
Nov 10, 2018
BACKGROUND AND PURPOSE: Delayed cerebral ischemia (DCI) is a severe complication in patients with aneurysmal subarachnoid hemorrhage. Several associated predictors have been previously identified. However, their predictive value is generally low. We ...
What Does Deep Learning See? Insights From a Classifier Trained to Predict Contrast Enhancement Phase From CT Images.
AJR. American journal of roentgenology
Nov 7, 2018
OBJECTIVE: Deep learning has shown great promise for improving medical image classification tasks. However, knowing what aspects of an image the deep learning system uses or, in a manner of speaking, sees to make its prediction is difficult.
A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using F-FDG PET of the Brain.
Radiology
Nov 6, 2018
Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to t...
Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms.
BMC ophthalmology
Nov 6, 2018
BACKGROUND: Convolution neural networks have been considered for automatic analysis of fundus images to detect signs of diabetic retinopathy but suffer from low sensitivity.
A Predictive Model for Guillain-Barré Syndrome Based on Ensemble Methods.
Computational intelligence and neuroscience
Nov 5, 2018
Nowadays, Machine Learning methods have proven to be highly effective on the identification of various types of diseases, in the form of predictive models. Guillain-Barré syndrome (GBS) is a potentially fatal autoimmune neurological disorder that has...
Using an artificial neural network to predict traumatic brain injury.
Journal of neurosurgery. Pediatrics
Nov 2, 2018
In BriefPediatric traumatic brain injury (TBI) is common, but not all injuries require hospitalization. A computational tool for ruling-in patients who will have clinically relevant TBI (CRTBI) would be valuable, providing an evidence-based mechanism...
Patch-based classification of thyroid nodules in ultrasound images using direction independent features extracted by two-threshold binary decomposition.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Oct 31, 2018
Ultrasound imaging of the thyroid gland is considered to be the best diagnostic choice for evaluating thyroid nodules in early stages, since it has been marked as cost-effective, non-invasive and risk-free. Computer aided diagnosis (CAD) systems can ...
Diabetic Retinopathy Diagnosis from Retinal Images Using Modified Hopfield Neural Network.
Journal of medical systems
Oct 31, 2018
Disease diagnosis from medical images has become increasingly important in medical science. Abnormality identification in retinal images has become a challenging task in medical science. Effective machine learning and soft computing methods should be...
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