AI Medical Compendium Journal:
AJR. American journal of roentgenology

Showing 151 to 160 of 169 articles

A Review of the Role of Augmented Intelligence in Breast Imaging: From Automated Breast Density Assessment to Risk Stratification.

AJR. American journal of roentgenology Nov 13, 2018
OBJECTIVE: The goal of augmented intelligence is to increase efficiency and effectiveness in practice. To achieve this, augmented intelligence technologies are being asked to perform a range of tasks, from simple to complex and quantitative. The deve...
Humans Artificial Intelligence Machine Learning Breast Neoplasms Risk Assessment Predictive Value of Tests Breast Density Algorithms Prognosis Female
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How Cognitive Machines Can Augment Medical Imaging.

AJR. American journal of roentgenology Nov 13, 2018
OBJECTIVE: Artificial intelligence (AI) neural networks rapidly convert disparate facts and data into highly predictive analytic models. Machine learning maps image-patient phenotype correlations opaque to standard statistics. Deep learning performs ...
Image Processing, Computer-Assisted Natural Language Processing Diagnostic Imaging Humans Neural Networks, Computer Artificial Intelligence Electronic Health Records Deep Learning Machine Learning Algorithms
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Machine Learning in Neurooncology Imaging: From Study Request to Diagnosis and Treatment.

AJR. American journal of roentgenology Nov 7, 2018
OBJECTIVE: Machine learning has potential to play a key role across a variety of medical imaging applications. This review seeks to elucidate the ways in which machine learning can aid and enhance diagnosis, treatment, and follow-up in neurooncology.
Humans Machine Learning Algorithms Brain Neoplasms Neuroimaging
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What Does Deep Learning See? Insights From a Classifier Trained to Predict Contrast Enhancement Phase From CT Images.

AJR. American journal of roentgenology Nov 7, 2018
OBJECTIVE: Deep learning has shown great promise for improving medical image classification tasks. However, knowing what aspects of an image the deep learning system uses or, in a manner of speaking, sees to make its prediction is difficult.
Sensitivity and Specificity Deep Learning Image Processing, Computer-Assisted Humans Algorithms Tomography, X-Ray Computed
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F-FDG PET/CT-Guided Real-Time Automated Robotic Arm-Assisted Needle Navigation for Percutaneous Biopsy of Hypermetabolic Bone Lesions: Diagnostic Performance and Clinical Impact.

AJR. American journal of roentgenology Nov 1, 2018
OBJECTIVE: The purpose of this study is to establish the feasibility, safety, diagnostic performance, and clinical impact of real-time intraprocedural F-FDG PET/CT-guided automated robotic arm-assisted biopsy of hypermetabolic marrow or bone lesions.
Adult Male Middle Aged Female Aged Aged, 80 and over Adolescent Bone Neoplasms Positron Emission Tomography Computed Tomography Robotics Radiopharmaceuticals Humans Fluorodeoxyglucose F18 Image-Guided Biopsy Prospective Studies
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JOURNAL CLUB: Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information.

AJR. American journal of roentgenology Oct 24, 2018
OBJECTIVE: When treatment decisions are being made for patients with acute ischemic stroke, timely and accurate outcome prediction plays an important role. The optimal rehabilitation strategy also relies on long-term outcome predictions. The decision...
Prognosis Retrospective Studies Tomography, X-Ray Computed Computed Tomography Angiography Machine Learning Decision Trees Humans Demography Algorithms Predictive Value of Tests Biomarkers Stroke Brain Ischemia Female Male Middle Aged
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State of the Art: Machine Learning Applications in Glioma Imaging.

AJR. American journal of roentgenology Oct 17, 2018
OBJECTIVE: Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MR...
Brain Neoplasms Glioma Image Interpretation, Computer-Assisted Machine Learning Patient Care Planning Forecasting Humans Prognosis
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Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods.

AJR. American journal of roentgenology Oct 17, 2018
OBJECTIVE: Machine learning (ML) and artificial intelligence (AI) are rapidly becoming the most talked about and controversial topics in radiology and medicine. Over the past few years, the numbers of ML- or AI-focused studies in the literature have ...
Machine Learning Algorithms Humans Image Processing, Computer-Assisted Radiology Statistics as Topic
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Detection of Traumatic Pediatric Elbow Joint Effusion Using a Deep Convolutional Neural Network.

AJR. American journal of roentgenology Oct 9, 2018
OBJECTIVE: The purpose of this study is to determine whether a deep convolutional neural network (DCNN) trained on a dataset of limited size can accurately diagnose traumatic pediatric elbow effusion on lateral radiographs.
Elbow Joint Observer Variation Elbow Injuries Child, Preschool Child Female Radiography Young Adult Sensitivity and Specificity Diagnosis, Computer-Assisted Adolescent Male Infant Humans Neural Networks, Computer Retrospective Studies
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Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

AJR. American journal of roentgenology May 24, 2018
OBJECTIVE: The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers.
Middle Aged Female Iohexol Predictive Value of Tests Humans Cone-Beam Computed Tomography Sensitivity and Specificity Radiographic Image Interpretation, Computer-Assisted Contrast Media Machine Learning Breast Neoplasms Prospective Studies
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