AIMC Topic: Sensitivity and Specificity
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Machine learning based cancer detection using various image modalities.
Computer methods and programs in biomedicine
Mar 1, 2018
Using Natural Language Processing of Free-Text Radiology Reports to Identify Type 1 Modic Endplate Changes.
Journal of digital imaging
Feb 1, 2018
Electronic medical record (EMR) systems provide easy access to radiology reports and offer great potential to support quality improvement efforts and clinical research. Harnessing the full potential of the EMR requires scalable approaches such as nat...
Preprocessing Prediction of Advanced Algorithms for Medical Imaging.
Journal of digital imaging
Feb 1, 2018
Advanced medical imaging algorithms (such as bone removal, vessel segmentation, or a lung nodule detection) can provide extremely valuable information to the radiologists, but they might sometimes be very time consuming. Being able to run the algorit...
[Single-chain antibodies from alpaca for the detection of Fasciola hepatica antigens].
Revista peruana de medicina experimental y salud publica
Jan 1, 2018
OBJECTIVES.: To produce recombinant single-chain antibodies from alpaca that will bind to the excreted-secreted (ES) Fasciola hepatica antigen with high affinity and specificity, so as to develop new diagnostic technologies of human and animal fascio...
[Evaluation of the lateral flow Aspergillus assay for the diagnosis of invasive aspergillosis, experience in a university hospital].
Revista chilena de infectologia : organo oficial de la Sociedad Chilena de Infectologia
Jan 1, 2018
BACKGROUND: The incidence of invasive aspergillosis is increasing. Its diagnosis is based on clinical and microbiological criteria which include the determination of serological markers such as galactomannan. Recently, the Aspergillus lateral flow de...
[Automated Classification of Calcification and Stent on Computed Tomography Coronary Angiography Using Deep Learning].
Nihon Hoshasen Gijutsu Gakkai zasshi
Jan 1, 2018
In computed tomography coronary angiography (CTCA), calcification and stent make it difficult to evaluate intravascular lumen. This is a cause of low positive-predictive value of coronary stenosis. Therefore, it is expected to develop a computer-aide...
Machine-Learning Prediction of Drug-Induced Cardiac Arrhythmia: Analysis of Gene Expression and Clustering.
Critical reviews in biomedical engineering
Jan 1, 2018
A marked delay in the electrical repolarization of heart ventricles is characterized by prolongation of the Q-T wave (QT) interval on a surface electrocardiogram. Such a delay can lead to potentially life-threatening cardiac arrhythmia (torsades de p...
Humans
Machine Learning
False Positive Reactions
Female
Reproducibility of Results
Torsades de Pointes
Signal Processing, Computer-Assisted
Sensitivity and Specificity
Rats
Male
Heart
Animals
Electrocardiography
Drug Design
Drug Evaluation, Preclinical
Support Vector Machine
Arrhythmias, Cardiac
Gene Expression Profiling
Heart Ventricles
Cluster Analysis
MAP Kinase Signaling System
Mice
Gene Expression Regulation
Clinical Trials as Topic
Automation of Detection of Cervical Cancer Using Convolutional Neural Networks.
Critical reviews in biomedical engineering
Jan 1, 2018
Classification of digital cervical images acquired during visual inspection with acetic acid (VIA) is an important step in automated image-based cervical cancer detection. Many algorithms have been developed for classification of cervical images base...
Plasma fatty acid profile as biomarker of coronary artery disease: a pilot study using fourth generation artificial neural networks.
Journal of biological regulators and homeostatic agents
Jan 1, 2018
Many studies, focused on identifying new biomarkers for coronary artery disease (CAD) risk computation and monitoring, suggested a potential diagnostic role for fatty acids (FA). In the present study, we explored the potential diagnostic role of FA b...
Automatic Detection of Depression by Using a Neural Network.
Studies in health technology and informatics
Jan 1, 2018
Depression is the most common psychiatric disorder worldwide, which affects more than 300 million people. We aimed to detect depressed patients and healthy people automatically. We work on the PHQ-9 questionnaires and reduced it to a PHQ-5 questionna...
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