AIMC Topic: Radiography

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CheXLocNet: Automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks.

PloS one Nov 9, 2020
BACKGROUND: Pneumothorax can lead to a life-threatening emergency. The experienced radiologists can offer precise diagnosis according to the chest radiographs. The localization of the pneumothorax lesions will help to quickly diagnose, which will be ...
Neural Networks, Computer Deep Learning Diagnosis, Computer-Assisted Image Processing, Computer-Assisted Humans Radiography Pneumothorax
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Assessment of the Willingness of Radiologists and Radiographers to Accept the Integration of Artificial Intelligence Into Radiology Practice.

Academic radiology Oct 29, 2020
RATIONALE AND OBJECTIVES: This study aimed to investigate radiologists' and radiographers' knowledge, perception, readiness, and challenges regarding Artificial Intelligence (AI) integration into radiology practice.
Radiology Radiologists Cross-Sectional Studies Radiography Humans Artificial Intelligence
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Deep Mining External Imperfect Data for Chest X-Ray Disease Screening.

IEEE transactions on medical imaging Oct 28, 2020
Deep learning approaches have demonstrated remarkable progress in automatic Chest X-ray analysis. The data-driven feature of deep models requires training data to cover a large distribution. Therefore, it is substantial to integrate knowledge from mu...
Thorax Deep Learning X-Rays Radiography Radiography, Thoracic
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Ankle fracture classification using deep learning: automating detailed AO Foundation/Orthopedic Trauma Association (AO/OTA) 2018 malleolar fracture identification reaches a high degree of correct classification.

Acta orthopaedica Oct 26, 2020
Background and purpose - Classification of ankle fractures is crucial for guiding treatment but advanced classifications such as the AO Foundation/Orthopedic Trauma Association (AO/OTA) are often too complex for human observers to learn and use. We h...
Radiography Ankle Fractures Sweden Deep Learning Humans Algorithms
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Hierarchical fracture classification of proximal femur X-Ray images using a multistage Deep Learning approach.

European journal of radiology Oct 23, 2020
PURPOSE: Suspected fractures are among the most common reasons for patients to visit emergency departments and often can be difficult to detect and analyze them on film scans. Therefore, we aimed to design a Deep Learning-based tool able to help doct...
Deep Learning Humans Radiography Neural Networks, Computer Femur X-Rays
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Evaluating AI Clinically-It's Not Just ROC AUC!

Radiology Oct 20, 2020
Radiography Breast Magnetic Resonance Imaging Area Under Curve Humans Artificial Intelligence
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Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.

Medical image analysis Oct 9, 2020
Machine learning models for radiology benefit from large-scale data sets with high quality labels for abnormalities. We curated and analyzed a chest computed tomography (CT) data set of 36,316 volumes from 19,993 unique patients. This is the largest ...
Humans Neural Networks, Computer Machine Learning Tomography, X-Ray Computed Radiography Lung Diseases
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Automated classification of urinary stones based on microcomputed tomography images using convolutional neural network.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Oct 8, 2020
PURPOSE: The classification of urinary stones is important prior to treatment because the treatments depend on three types of urinary stones, i.e., calcium, uric acid, and mixture stones. We have developed an automatic approach for the classification...
Radiography X-Ray Microtomography Humans Neural Networks, Computer Urinary Calculi
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Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents.

JAMA network open Oct 1, 2020
IMPORTANCE: Chest radiography is the most common diagnostic imaging examination performed in emergency departments (EDs). Augmenting clinicians with automated preliminary read assistants could help expedite their workflows, improve accuracy, and redu...
Internship and Residency Quality of Health Care Thorax Artificial Intelligence Algorithms Humans Radiography Radiographic Image Interpretation, Computer-Assisted ROC Curve Area Under Curve
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Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural network.

European radiology Sep 28, 2020
OBJECTIVES: The goal of the present study was to classify the most common types of plain radiographs using a neural network and to validate the network's performance on internal and external data. Such a network could help improve various radiologica...
Algorithms Deep Learning Radiography Humans Workflow Neural Networks, Computer
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