AIMC Topic: Sensitivity and Specificity

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Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of histologic inflammation associated with ulcerative colitis (with video).

Gastrointestinal endoscopy Sep 27, 2018
BACKGROUND AND AIMS: In the treatment of ulcerative colitis (UC), an incremental benefit of achieving histologic healing beyond that of endoscopic mucosal healing has been suggested; persistent histologic inflammation increases the risk of exacerbati...
Intestinal Mucosa Rectum Automation Colonoscopy Diagnosis, Computer-Assisted Inflammation Colitis, Ulcerative Male Middle Aged Colon Humans Artificial Intelligence Machine Learning Algorithms Retrospective Studies Female Reproducibility of Results Sensitivity and Specificity
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Low-Rank Representation for Multi-center Autism Spectrum Disorder Identification.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Sep 26, 2018
Effective utilization of multi-center data for autism spectrum disorder (ASD) diagnosis recently has attracted increasing attention, since a large number of subjects from multiple centers are beneficial for investigating the pathological changes of A...
Databases, Factual Autism Spectrum Disorder Reproducibility of Results Sensitivity and Specificity Humans Machine Learning Algorithms
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Multi-task SonoEyeNet: Detection of Fetal Standardized Planes Assisted by Generated Sonographer Attention Maps.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Sep 26, 2018
We present a novel multi-task convolutional neural network called Multi-task SonoEyeNet () that learns to generate clinically relevant visual attention maps using sonographer gaze tracking data on input ultrasound (US) video frames so as to assist st...
Algorithms Reproducibility of Results Sensitivity and Specificity Eye Movements Humans Neural Networks, Computer Image Processing, Computer-Assisted Attention Fetus
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Automatic Lacunae Localization in Placental Ultrasound Images via Layer Aggregation.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Sep 26, 2018
Accurate localization of structural abnormalities is a precursor for image-based prenatal assessment of adverse conditions. For clinical screening and diagnosis of abnormally invasive placenta (AIP), a life-threatening obstetric condition, qualitativ...
Humans Machine Learning Algorithms Pregnancy Female Placenta Reproducibility of Results Pattern Recognition, Automated Sensitivity and Specificity Ultrasonography
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Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning based Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Sep 26, 2018
This paper introduces an unsupervised adversarial similarity network for image registration. Unlike existing deep learning registration frameworks, our approach does not require ground-truth deformations and specific similarity metrics. We connect a ...
Magnetic Resonance Imaging Sensitivity and Specificity Deep Learning Image Processing, Computer-Assisted Machine Learning Humans Algorithms Brain Reproducibility of Results
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ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network.

Physiological measurement Sep 24, 2018
OBJECTIVE: The electrocardiogram (ECG) provides an effective, non-invasive approach for clinical diagnosis in patients with cardiac diseases such as atrial fibrillation (AF). AF is the most common cardiac rhythm disturbance and affects ~2% of the gen...
Pattern Recognition, Automated Sensitivity and Specificity Diagnosis, Computer-Assisted Electrocardiography Arrhythmias, Cardiac Humans Neural Networks, Computer
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Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity.

Acta psychiatrica Scandinavica Sep 21, 2018
OBJECTIVE: Structural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications suggest that individual classification is feasible and reliable and, however, is focused on the predicti...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Schizophrenia Gray Matter Sensitivity and Specificity Male Middle Aged Severity of Illness Index Humans Machine Learning Female Reproducibility of Results Adult
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Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis.

The British journal of dermatology Sep 19, 2018
BACKGROUND: Application of deep-learning technology to skin cancer classification can potentially improve the sensitivity and specificity of skin cancer screening, but the number of training images required for such a system is thought to be extremel...
Humans Skin Neoplasms Dermoscopy Skin Dermatologists Image Interpretation, Computer-Assisted Datasets as Topic Sensitivity and Specificity Deep Learning Smartphone Mobile Applications
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Predicting the risk of acute care readmissions among rehabilitation inpatients: A machine learning approach.

Journal of biomedical informatics Sep 18, 2018
INTRODUCTION: Readmission from inpatient rehabilitation facilities to acute care hospitals is a serious problem. This study aims to develop a predictive model based on machine learning algorithms to identify patients at high risk of readmission.
Female Hospitalization Aged Sensitivity and Specificity Humans Machine Learning Algorithms Retrospective Studies Patient Readmission Risk North Carolina Outpatients Rehabilitation Preventive Medicine Support Vector Machine Longitudinal Studies Regression Analysis Inpatients Male Middle Aged ROC Curve Logistic Models
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Machine learning improves classification of preclinical models of pancreatic cancer with chemical exchange saturation transfer MRI.

Magnetic resonance in medicine Sep 17, 2018
PURPOSE: We sought to assess whether machine learning-based classification approaches can improve the classification of pancreatic tumor models relative to more simplistic analysis methods, using T relaxation, CEST, and DCE MRI.
Female Image Interpretation, Computer-Assisted Humans Cell Line, Tumor Mice, SCID Pancreatic Neoplasms Machine Learning Reproducibility of Results Image Processing, Computer-Assisted Image Enhancement Principal Component Analysis Algorithms Sensitivity and Specificity Mice Normal Distribution Area Under Curve Animals Magnetic Resonance Imaging Hypoxia
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