AIMC Topic: Sensitivity and Specificity

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Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach.

Journal of pediatric surgery Mar 18, 2019
BACKGROUND: Cervical spine injuries (CSI) are a major concern in young pediatric trauma patients. The consequences of missed injuries and difficulties in injury clearance for non-verbal patients have led to a tendency to image young children. Imaging...
Male Infant Child, Preschool Age Factors Registries Cervical Vertebrae Spinal Injuries Glasgow Coma Scale Algorithms Retrospective Studies Female Sensitivity and Specificity Humans Machine Learning
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Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma.

Oral oncology Mar 16, 2019
OBJECTIVES: To develop and validate an algorithm to predict occult nodal metastasis in clinically node negative oral cavity squamous cell carcinoma (OCSCC) using machine learning. To compare algorithm performance to a model based on tumor depth of in...
Aged Sensitivity and Specificity Male Middle Aged ROC Curve Models, Theoretical Lymphatic Metastasis Neoplasm Grading Databases, Factual Mouth Neoplasms Humans Prognosis Artificial Intelligence Female Machine Learning Carcinoma, Squamous Cell Algorithms
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Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT.

European radiology Mar 15, 2019
PURPOSE: To develop a deep learning-based computer-aided diagnosis (CAD) system for use in the CT diagnosis of cervical lymph node metastasis (LNM) in patients with thyroid cancer.
Tomography, X-Ray Computed Female Sensitivity and Specificity Male Humans Neural Networks, Computer Algorithms Retrospective Studies Lymphatic Metastasis Area Under Curve Radiographic Image Interpretation, Computer-Assisted Diagnosis, Computer-Assisted Deep Learning Thyroid Neoplasms Neck
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Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method.

European journal of radiology Mar 15, 2019
PURPOSE: To evaluate the performance of a multi-parametric MRI (mp-MRI)-based radiomics signature for discriminating between clinically significant prostate cancer (csPCa) and insignificant PCa (ciPCa).
Retrospective Studies Humans ROC Curve Aged Machine Learning Magnetic Resonance Imaging Algorithms Sensitivity and Specificity Prostatic Neoplasms Male Neoplasm Grading
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Adaptive Fruitfly Based Modified Region Growing Algorithm for Cardiac Fat Segmentation Using Optimal Neural Network.

Journal of medical systems Mar 15, 2019
Epicardial adipose tissue is a visceral fat that has remained an entity of concern for decades owing to its high correlation with coronary heart disease. It continues to stump medical practitioners on the pretext of its relevance with pericardial fat...
Humans Sensitivity and Specificity Image Processing, Computer-Assisted Neural Networks, Computer Adipose Tissue Algorithms Pericardium Tomography, X-Ray Computed
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A deep neural network improves endoscopic detection of early gastric cancer without blind spots.

Endoscopy Mar 12, 2019
BACKGROUND: Gastric cancer is the third most lethal malignancy worldwide. A novel deep convolution neural network (DCNN) to perform visual tasks has been recently developed. The aim of this study was to build a system using the DCNN to detect early g...
Gastroscopy Humans Neural Networks, Computer Stomach Neoplasms Sensitivity and Specificity Clinical Competence Early Detection of Cancer Diagnosis, Differential Observer Variation
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Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network.

Clinical rheumatology Mar 8, 2019
INTRODUCTION: Plain hand radiographs are the first-line and most commonly used imaging methods for diagnosis or differential diagnosis of rheumatoid arthritis (RA) and for monitoring disease activity. In this study, we used plain hand radiographs and...
Sensitivity and Specificity Arthritis, Rheumatoid Radiography Hand Humans Neural Networks, Computer
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A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task.

European journal of cancer (Oxford, England : 1990) Mar 8, 2019
BACKGROUND: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively ...
Skin Neoplasms Dermoscopy Sensitivity and Specificity Dermatologists Humans Neural Networks, Computer Melanoma Dermatology Image Interpretation, Computer-Assisted
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NMD-12: A new machine-learning derived screening instrument to detect mild cognitive impairment and dementia.

PloS one Mar 8, 2019
INTRODUCTION: Using machine learning techniques, we developed a brief questionnaire to aid neurologists and neuropsychologists in the screening of mild cognitive impairment (MCI) and dementia.
Neuropsychological Tests Diagnosis, Differential Area Under Curve Aged Cognitive Dysfunction Aged, 80 and over Geriatric Assessment Dementia Male Adult ROC Curve Sensitivity and Specificity Middle Aged Mass Screening Humans Diagnosis, Computer-Assisted Machine Learning Surveys and Questionnaires Female
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Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI.

NeuroImage. Clinical Mar 6, 2019
Neuromelanin sensitive magnetic resonance imaging (NMS-MRI) has been crucial in identifying abnormalities in the substantia nigra pars compacta (SNc) in Parkinson's disease (PD) as PD is characterized by loss of dopaminergic neurons in the SNc. Curre...
Supranuclear Palsy, Progressive Image Processing, Computer-Assisted Magnetic Resonance Imaging Sensitivity and Specificity Humans Male Parkinson Disease Substantia Nigra Biomarkers Middle Aged Diagnosis, Differential Female Deep Learning Neuroimaging Melanins Multiple System Atrophy
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