AIMC Topic: Sensitivity and Specificity

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On classifying sepsis heterogeneity in the ICU: insight using machine learning.

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2020
OBJECTIVES: Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of the condition despite its emerging importance in prognosis and treatment. This work demonstrates the ...
Sepsis Diagnosis, Differential Organ Dysfunction Scores Intensive Care Units Humans Electronic Health Records Machine Learning Sensitivity and Specificity
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[Detection of inferior myocardial infarction based on densely connected convolutional neural network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Feb 25, 2020
Inferior myocardial infarction is an acute ischemic heart disease with high mortality, which is easy to induce life-threatening complications such as arrhythmia, heart failure and cardiogenic shock. Therefore, it is of great clinical value to carry o...
Sensitivity and Specificity Electrocardiography Inferior Wall Myocardial Infarction Humans Neural Networks, Computer
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Artificial Intelligence Improves Breast Cancer Screening in Study.

JAMA Feb 11, 2020
Humans Artificial Intelligence Breast Neoplasms Early Detection of Cancer Mammography Female Sensitivity and Specificity Diagnostic Errors Radiologists
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Retrospective imaging studies of gastric cancer: Study protocol clinical trial (SPIRIT Compliant).

Medicine Feb 1, 2020
INTRODUCTION: Peritoneal metastasis (PM) is a frequent condition in patients presenting with gastric cancer, especially in younger patients with advanced tumor stages. Computer tomography (CT) is the most common noninvasive modality for preoperative ...
Clinical Trials as Topic Sensitivity and Specificity Image Processing, Computer-Assisted Peritoneal Neoplasms Humans Artificial Intelligence Machine Learning Retrospective Studies Tomography, X-Ray Computed Stomach Neoplasms
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Using a Dual-Input Convolutional Neural Network for Automated Detection of Pediatric Supracondylar Fracture on Conventional Radiography.

Investigative radiology Feb 1, 2020
OBJECTIVES: This study aimed to develop a dual-input convolutional neural network (CNN)-based deep-learning algorithm that utilizes both anteroposterior (AP) and lateral elbow radiographs for the automated detection of pediatric supracondylar fractur...
Deep Learning Child ROC Curve Retrospective Studies Reproducibility of Results Elbow Fractures, Bone Humans Sensitivity and Specificity Feasibility Studies Radiographic Image Interpretation, Computer-Assisted Neural Networks, Computer Area Under Curve Elbow Injuries Algorithms Radiography
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SDResU-Net: Separable and Dilated Residual U-Net for MRI Brain Tumor Segmentation.

Current medical imaging Jan 1, 2020
BACKGROUND: Glioma is one of the most common and aggressive primary brain tumors that endanger human health. Tumors segmentation is a key step in assisting the diagnosis and treatment of cancer disease. However, it is a relatively challenging task to...
Glioma Sensitivity and Specificity Humans Neuroimaging Neural Networks, Computer Brain Neoplasms Magnetic Resonance Imaging Datasets as Topic Brain Image Processing, Computer-Assisted
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Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines.

Current medical imaging Jan 1, 2020
BACKGROUND: Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usual...
Breast Neoplasms Support Vector Machine Early Detection of Cancer Algorithms Breast Diseases Mammography Humans Female Datasets as Topic Sensitivity and Specificity Neural Networks, Computer Radiographic Image Enhancement
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[New Trends in Breast Imaging].

Therapeutische Umschau. Revue therapeutique Jan 1, 2020
New Trends in Breast Imaging The examination of the breast, especially as a screening examination for breast cancer, has so far been carried out primarily by means of mammography and occasionally supplementary ultrasound. These check-ups have become...
Humans Magnetic Resonance Imaging Artificial Intelligence Mammography Female Ultrasonography, Mammary Sensitivity and Specificity Breast Neoplasms
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Machine-Learning Algorithms Based on Screening Tests for Mild Cognitive Impairment.

American journal of Alzheimer's disease and other dementias Jan 1, 2020
BACKGROUND: The mobile screening test system for mild cognitive impairment (mSTS-MCI) was developed and validated to address the low sensitivity and specificity of the Montreal Cognitive Assessment (MoCA) widely used clinically.
Humans Machine Learning Female Aged Sensitivity and Specificity Male Mental Status and Dementia Tests Cognitive Dysfunction Mass Screening Neuropsychological Tests
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Deep learning-based CAD schemes for the detection and classification of lung nodules from CT images: A survey.

Journal of X-ray science and technology Jan 1, 2020
BACKGROUND: Lung cancer is the most common cancer in the world. Computed tomography (CT) is the standard medical imaging modality for early lung nodule detection and diagnosis that improves patient's survival rate. Recently, deep learning algorithms,...
Radiographic Image Interpretation, Computer-Assisted Databases, Factual Tomography, X-Ray Computed Sensitivity and Specificity Surveys and Questionnaires Lung Neoplasms Lung Deep Learning Humans Neural Networks, Computer
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