AIMC Topic: Predictive Value of Tests

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Collateral Automation for Triage in Stroke: Evaluating Automated Scoring of Collaterals in Acute Stroke on Computed Tomography Scans.

Cerebrovascular diseases (Basel, Switzerland) Jun 19, 2019
Computed tomography angiography (CTA) collateral scoring can identify patients most likely to benefit from mechanical thrombectomy and those more likely to have good outcomes and ranges from 0 (no collaterals) to 3 (complete collaterals). In this stu...
Patient Selection Predictive Value of Tests Cerebrovascular Circulation Humans Middle Cerebral Artery Triage Automation Machine Learning Computed Tomography Angiography Radiographic Image Interpretation, Computer-Assisted Blood Flow Velocity Prognosis Cerebral Angiography Stroke Thrombectomy Collateral Circulation Clinical Decision-Making
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Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms.

Radiology Jun 18, 2019
Background Computational models on the basis of deep neural networks are increasingly used to analyze health care data. However, the efficacy of traditional computational models in radiology is a matter of debate. Purpose To evaluate the accuracy and...
Breast Humans Retrospective Studies Deep Learning Electronic Health Records Mammography Female Predictive Value of Tests Reproducibility of Results Radiographic Image Interpretation, Computer-Assisted Sensitivity and Specificity Breast Neoplasms Middle Aged
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Comparison of F-FDG avidity at PET of benign and malignant pure ground-glass opacities: a paradox? Part II: artificial neural network integration of the PET/CT characteristics of ground-glass opacities to predict their likelihood of malignancy.

Clinical radiology Jun 12, 2019
AIM: To assess the ability of artificial neural networks (ANNs) to predict the likelihood of malignancy of pure ground-glass opacities (GGOs), using observations from computed tomography (CT) and 2-[F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission...
Humans Neural Networks, Computer Retrospective Studies Female Predictive Value of Tests Positron Emission Tomography Computed Tomography Radiographic Image Interpretation, Computer-Assisted Radiopharmaceuticals Fluorodeoxyglucose F18 Adult Male Lung Neoplasms Diagnosis, Differential
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Uncovering the structure of self-regulation through data-driven ontology discovery.

Nature communications May 24, 2019
Psychological sciences have identified a wealth of cognitive processes and behavioral phenomena, yet struggle to produce cumulative knowledge. Progress is hamstrung by siloed scientific traditions and a focus on explanation over prediction, two issue...
Self-Control Self Report Humans Knowledge Bases Adult Datasets as Topic Psychometrics Individuality Data Science Predictive Value of Tests
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Pathologist-level classification of histopathological melanoma images with deep neural networks.

European journal of cancer (Oxford, England : 1990) May 23, 2019
BACKGROUND: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports 25-26% ...
Diagnosis, Computer-Assisted Deep Learning Predictive Value of Tests Biopsy Pathologists Humans Melanoma Skin Neoplasms Reproducibility of Results Microscopy Image Interpretation, Computer-Assisted Observer Variation Nevus
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Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning.

Epilepsy & behavior : E&B May 20, 2019
OBJECTIVE: The objective of this study was to build a supervised machine learning-based classifier, which can accurately predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy.
Female Young Adult Adult Male Middle Aged Adolescent Treatment Outcome Follow-Up Studies Magnetic Resonance Imaging Humans Machine Learning Algorithms Prospective Studies Predictive Value of Tests Electroencephalography Epilepsy Anticonvulsants
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Ventricular geometry-regularized QRSd predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography.

The international journal of cardiovascular imaging May 18, 2019
Up to one-third of patients selected by current guidelines do not respond to cardiac resynchronization therapy (CRT), the aim of this study was to find out novel analytical approaches to improve pre-implantation CRT response prediction. Among 31 pre-...
Treatment Outcome Support Vector Machine Predictive Value of Tests Signal Processing, Computer-Assisted Patient Selection Heart Failure Cardiac Resynchronization Therapy Ventricular Remodeling Image Interpretation, Computer-Assisted Ventricular Function, Left Echocardiography Middle Aged Female Electrocardiography Humans Aged Clinical Decision-Making Aged, 80 and over Male
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Cancer taxonomy: pathology beyond pathology.

European journal of cancer (Oxford, England : 1990) May 17, 2019
The way we categorise and classify cancer types dictates not only the way we diagnose and treat patients but also many of our decisions on biomarker and drug development. In addition, cancer taxonomy proves the ground truth for future discoveries in ...
Predictive Value of Tests Biopsy Pathologists Microscopy Image Interpretation, Computer-Assisted Observer Variation Humans Classification Artificial Intelligence Reproducibility of Results Neoplasms Diagnosis, Computer-Assisted
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Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies.

Virchows Archiv : an international journal of pathology May 16, 2019
Histopathologic grading of prostate cancer using Gleason patterns (GPs) is subject to a large inter-observer variability, which may result in suboptimal treatment of patients. With the introduction of digitization and whole-slide images of prostate b...
Humans Automation, Laboratory Pattern Recognition, Automated Reproducibility of Results Prostatic Neoplasms Male Neoplasm Grading Deep Learning Biopsy Predictive Value of Tests Image Interpretation, Computer-Assisted Observer Variation
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A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images.

JACC. Cardiovascular imaging May 15, 2019
OBJECTIVES: This study investigated whether a deep convolutional neural network (DCNN) could provide improved detection of regional wall motion abnormalities (RWMAs) and differentiate among groups of coronary infarction territories from conventional ...
Aged Aged, 80 and over Male Ventricular Function, Left Echocardiography Humans Retrospective Studies Female Reproducibility of Results Myocardial Infarction Image Interpretation, Computer-Assisted Observer Variation Middle Aged Diagnosis, Computer-Assisted Deep Learning Predictive Value of Tests
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