AIMC Topic: Sensitivity and Specificity

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Performance of an artificial intelligence algorithm for reporting urine cytopathology.

Cancer cytopathology Aug 14, 2019
BACKGROUND: Unlike Papanicolaou tests, there are no commercially available computer-assisted automated screening systems for urine specimens. Despite The Paris System for Reporting Urinary Cytology, there still is poor interobserver agreement with ur...
Image Interpretation, Computer-Assisted Humans Observer Variation Carcinoma, Transitional Cell Female Aged Urine Deep Learning Urothelium Urinary Bladder Neoplasms Aged, 80 and over Feasibility Studies Sensitivity and Specificity Middle Aged
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Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks.

European journal of cancer (Oxford, England : 1990) Aug 14, 2019
BACKGROUND: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer s...
Nevus Sensitivity and Specificity Algorithms Humans Male Biopsy Surveys and Questionnaires Female Melanoma Neural Networks, Computer Dermatologists Diagnosis, Differential Skin Neoplasms Hospitals, University Dermoscopy
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Lung segmentation method with dilated convolution based on VGG-16 network.

Computer assisted surgery (Abingdon, England) Aug 12, 2019
Lung cancer has become one of the life-threatening killers. Lung disease need to be assisted by CT images taken doctor's diagnosis, and the segmented CT image of the lung parenchyma is the first step to help doctor diagnosis. For the problem of accur...
Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Sensitivity and Specificity Lung Neoplasms Humans Neural Networks, Computer
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An Intelligent Segmentation and Diagnosis Method for Diabetic Retinopathy Based on Improved U-NET Network.

Journal of medical systems Aug 12, 2019
Due to insufficient samples, the generalization performance of deep network is insufficient. In order to solve this problem, an improved U-net based image automatic segmentation and diagnosis algorithm was proposed, in which the max-pooling operation...
Humans Diabetic Retinopathy Image Processing, Computer-Assisted Neural Networks, Computer Retina Algorithms Sensitivity and Specificity
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Toward a Better Estimation of Functional Brain Network for Mild Cognitive Impairment Identification: A Transfer Learning View.

IEEE journal of biomedical and health informatics Aug 9, 2019
Mild cognitive impairment (MCI) is an intermediate stage of brain cognitive decline, associated with increasing risk of developing Alzheimer's disease (AD). It is believed that early treatment of MCI could slow down the progression of AD, and functio...
Brain Sensitivity and Specificity Cognitive Dysfunction Humans Magnetic Resonance Imaging Machine Learning Image Interpretation, Computer-Assisted
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Digital whole-slide image analysis for automated diatom test in forensic cases of drowning using a convolutional neural network algorithm.

Forensic science international Aug 8, 2019
Diatom examinations have been widely used to perform drowning diagnosis in forensic practice. However, current methods for recognizing diatoms, which use light or electron microscopy, are time-consuming and laborious and often result in false positiv...
Diatoms Forensic Pathology Drowning Sensitivity and Specificity Humans Lung Neural Networks, Computer Deep Learning Algorithms
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Prediction of melanoma evolution in melanocytic nevi via artificial intelligence: A call for prospective data.

European journal of cancer (Oxford, England : 1990) Aug 8, 2019
Recent research revealed the superiority of artificial intelligence over dermatologists to diagnose melanoma from images. However, 30-50% of all melanomas and more than half of those in young patients evolve from initially benign lesions. Despite its...
Nevus, Pigmented Mutation Skin Cell Transformation, Neoplastic Algorithms Prospective Studies Melanoma Sensitivity and Specificity Early Detection of Cancer Skin Neoplasms Dermoscopy Humans Artificial Intelligence
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Comparison of text processing methods in social media-based signal detection.

Pharmacoepidemiology and drug safety Aug 7, 2019
PURPOSE: Adverse event (AE) identification in social media (SM) can be performed using various types of natural language processing (NLP) and machine learning (ML). These methods can be categorized by complexity and precision level. Co-occurrence-bas...
Drug-Related Side Effects and Adverse Reactions Social Media Data Collection Natural Language Processing False Positive Reactions Retrospective Studies Datasets as Topic Feasibility Studies Pharmacovigilance Adverse Drug Reaction Reporting Systems False Negative Reactions Sensitivity and Specificity
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Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions.

JAMA network open Aug 2, 2019
IMPORTANCE: Following recent US Food and Drug Administration approval, adoption of whole slide imaging in clinical settings may be imminent, and diagnostic accuracy, particularly among challenging breast biopsy specimens, may benefit from computerize...
Humans Neural Networks, Computer Machine Learning Biopsy Registries Carcinoma, Intraductal, Noninfiltrating Reference Standards Female Sensitivity and Specificity Breast Neoplasms Carcinoma, Ductal
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Machine Learning for Patient-Specific Quality Assurance of VMAT: Prediction and Classification Accuracy.

International journal of radiation oncology, biology, physics Aug 1, 2019
PURPOSE: To assess the accuracy of machine learning to predict and classify quality assurance (QA) results for volumetric modulated arc therapy (VMAT) plans.
Reproducibility of Results Sensitivity and Specificity Head and Neck Neoplasms Poisson Distribution Humans Machine Learning Female Data Accuracy Quality Assurance, Health Care Regression Analysis Workload Radiotherapy Planning, Computer-Assisted Genital Neoplasms, Female Radiotherapy, Intensity-Modulated
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