AIMC Topic: Sensitivity and Specificity

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Automated detection of the head-twitch response using wavelet scalograms and a deep convolutional neural network.

Scientific reports May 20, 2020
Hallucinogens induce the head-twitch response (HTR), a rapid reciprocal head movement, in mice. Although head twitches are usually identified by direct observation, they can also be assessed using a head-mounted magnet and a magnetometer. Procedures ...
Magnets Behavior, Animal Head Movements Behavior Observation Techniques Support Vector Machine Drug Evaluation, Preclinical Hallucinogens Models, Animal Magnetometry Animals Male Mice Reproducibility of Results Sensitivity and Specificity
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Using machine learning to improve our understanding of injury risk and prediction in elite male youth football players.

Journal of science and medicine in sport May 18, 2020
OBJECTIVES: The purpose of this study was to examine whether the use of machine learning improved the ability of a neuromuscular screen to identify injury risk factors in elite male youth football players.
Incidence Child Soccer Athletic Injuries Sensitivity and Specificity Male England Adolescent Humans Machine Learning Prospective Studies Risk Factors
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Automatic Grading System for Diabetic Retinopathy Diagnosis Using Deep Learning Artificial Intelligence Software.

Current eye research May 15, 2020
: To describe the development and validation of an artificial intelligence-based, deep learning algorithm (DeepDR) for the detection of diabetic retinopathy (DR) in retinal fundus photographs. : Five hundred fundus images, which had detailed labellin...
Predictive Value of Tests Area Under Curve Exudates and Transudates Fluorescein Angiography Reproducibility of Results Humans Microaneurysm Sensitivity and Specificity Diabetic Retinopathy Retinal Hemorrhage Diagnosis, Computer-Assisted Neural Networks, Computer Photography Deep Learning Software
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Likelihood Ratios for Deep Neural Networks in Face Comparison.

Journal of forensic sciences May 12, 2020
In this study, we aim to compare the performance of systems and forensic facial comparison experts in terms of likelihood ratio computation to assess the potential of the machine to support the human expert in the courtroom. In forensics, transparenc...
Software Sensitivity and Specificity Models, Statistical Automated Facial Recognition Forensic Sciences Likelihood Functions Humans Neural Networks, Computer
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A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations.

The Lancet. Digital health May 12, 2020
BACKGROUND: Screening for chronic kidney disease is a challenge in community and primary care settings, even in high-income countries. We developed an artificial intelligence deep learning algorithm (DLA) to detect chronic kidney disease from retinal...
Middle Aged Image Interpretation, Computer-Assisted Female Humans Eye Diseases Singapore Reproducibility of Results Deep Learning Algorithms Sensitivity and Specificity Photography Fundus Oculi China Cross-Sectional Studies Renal Insufficiency, Chronic Male
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The usefulness of the Deep Learning method of variational autoencoder to reduce measurement noise in glaucomatous visual fields.

Scientific reports May 12, 2020
The aim of the study was to investigate the usefulness of processing visual field (VF) using a variational autoencoder (VAE). The training data consisted of 82,433 VFs from 16,836 eyes. Testing dataset 1 consisted of test-retest VFs from 104 eyes wit...
Adult Sensitivity and Specificity Intraocular Pressure Male Middle Aged Humans Female Reproducibility of Results Visual Field Tests Eye Glaucoma, Open-Angle Disease Progression Deep Learning Visual Fields Linear Models
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Applications of radiomics and machine learning for radiotherapy of malignant brain tumors.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] May 11, 2020
BACKGROUND: Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use o...
Progression-Free Survival Positron-Emission Tomography Radiotherapy Planning, Computer-Assisted DNA Modification Methylases Neuroimaging Radiosurgery Promoter Regions, Genetic DNA Repair Enzymes Tumor Suppressor Proteins Glioblastoma Brain Neoplasms Isocitrate Dehydrogenase Radiation Oncology Deep Learning Neoplasm Grading Humans Imaging Genomics Image Processing, Computer-Assisted Neoplasm Recurrence, Local Magnetic Resonance Imaging Sensitivity and Specificity Computational Biology DNA Methylation Glioma Diagnosis, Differential Neoplasm Proteins
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Artificial Intelligence for the Computer-aided Detection of Periapical Lesions in Cone-beam Computed Tomographic Images.

Journal of endodontics May 8, 2020
INTRODUCTION: The aim of this study was to use a Deep Learning (DL) algorithm for the automated segmentation of cone-beam computed tomographic (CBCT) images and the detection of periapical lesions.
Sensitivity and Specificity Artificial Intelligence Cone-Beam Computed Tomography Computers
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Endoscopic three-categorical diagnosis of Helicobacter pylori infection using linked color imaging and deep learning: a single-center prospective study (with video).

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association May 7, 2020
BACKGROUND: Helicobacter pylori (H. pylori) eradication is required to reduce incidence related to gastric cancer. Recently, it was found that even after the successful eradication of H. pylori, an increased, i.e., moderate, risk of gastric cancer pe...
Feasibility Studies Image Enhancement Gastric Mucosa Atrophy Endoscopy, Digestive System Area Under Curve Color Helicobacter Infections Prospective Studies Helicobacter pylori Female Aged Sensitivity and Specificity Diagnosis, Computer-Assisted Deep Learning Predictive Value of Tests Male Humans Middle Aged
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Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging.

Advances in rheumatology (London, England) May 7, 2020
BACKGROUND: Currently, magnetic resonance imaging (MRI) is used to evaluate active inflammatory sacroiliitis related to axial spondyloarthritis (axSpA). The qualitative and semiquantitative diagnosis performed by expert radiologists and rheumatologis...
Diagnosis, Computer-Assisted Humans Magnetic Resonance Imaging Sacroiliac Joint Machine Learning Sacroiliitis Retrospective Studies Spondylarthritis Sensitivity and Specificity
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