AIMC Topic: Radiologists
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Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms.
The Lancet. Digital health
Sep 1, 2021
BACKGROUND: Artificial intelligence (AI) algorithms can be trained to recognise tuberculosis-related abnormalities on chest radiographs. Various AI algorithms are available commercially, yet there is little impartial evidence on how their performance...
Ethics and standards in the use of artificial intelligence in medicine on behalf of the Royal Australian and New Zealand College of Radiologists.
Journal of medical imaging and radiation oncology
Aug 1, 2021
INTRODUCTION: The Royal Australian and New Zealand College of Radiologists (RANZCR) led the medical community in Australia and New Zealand in considering the impact of machine learning and artificial intelligence (AI) in health care. RANZCR identifie...
Automated Detection and Quantification of COVID-19 Airspace Disease on Chest Radiographs: A Novel Approach Achieving Expert Radiologist-Level Performance Using a Deep Convolutional Neural Network Trained on Digital Reconstructed Radiographs From Computed Tomography-Derived Ground Truth.
Investigative radiology
Aug 1, 2021
OBJECTIVES: The aim of this study was to leverage volumetric quantification of airspace disease (AD) derived from a superior modality (computed tomography [CT]) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to (1)...
Improving reference standards for validation of AI-based radiography.
The British journal of radiology
Jul 1, 2021
OBJECTIVE: Demonstrate the importance of combining multiple readers' opinions, in a context-aware manner, when establishing the reference standard for validation of artificial intelligence (AI) applications for, chest radiographs. By comparing indiv...
Performance and educational training of radiographers in lung nodule or mass detection: Retrospective comparison with different deep learning algorithms.
Medicine
Jun 11, 2021
The aim of this investigation was to compare the diagnostic performance of radiographers and deep learning algorithms in pulmonary nodule/mass detection on chest radiograph.A test set of 100 chest radiographs containing 53 cases with no pathology (no...
Deep Learning: An Update for Radiologists.
Radiographics : a review publication of the Radiological Society of North America, Inc
Jan 1, 2021
Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the image fea...
Deep learning assistance for tuberculosis diagnosis with chest radiography in low-resource settings.
Journal of X-ray science and technology
Jan 1, 2021
Tuberculosis (TB) is a major health issue with high mortality rates worldwide. Recently, tremendous researches of artificial intelligence (AI) have been conducted targeting at TB to reduce the diagnostic burden. However, most researches are conducted...
Rethinking the Approach to Artificial Intelligence for Medical Image Analysis: The Case for Precision Diagnosis.
Journal of the American College of Radiology : JACR
Jan 1, 2021
To date, widely generalizable artificial intelligence (AI) programs for medical image analysis have not been demonstrated, including for mammography. Rather than pursuing a strategy of collecting ever-larger databases in the attempt to build generali...
Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections.
Journal of X-ray science and technology
Jan 1, 2021
BACKGROUND: Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment.
Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images.
Diagnostic and interventional radiology (Ankara, Turkey)
Jan 1, 2021
PURPOSE: Chest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associ...
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