AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules.
American journal of respiratory and critical care medicine
Jul 15, 2020
The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unnecessary invasive procedures and optimize surveillance regime...
A deep learning-based automated diagnostic system for classifying mammographic lesions.
Medicine
Jul 2, 2020
BACKGROUND: Screening mammography has led to reduced breast cancer-specific mortality and is recommended worldwide. However, the resultant doctors' workload of reading mammographic scans needs to be addressed. Although computer-aided detection (CAD) ...
An Efficient Method to Predict Pneumonia from Chest X-Rays Using Deep Learning Approach.
Studies in health technology and informatics
Jun 26, 2020
Pneumonia is a severe health problem causing millions of deaths every year. The aim of this study was to develop an advanced deep learning-based architecture to detect pneumonia using chest X-ray images. We utilized a convolutional neural network (CN...
Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation.
The Lancet. Digital health
Jun 1, 2020
BACKGROUND: The diagnostic performance of CT for pancreatic cancer is interpreter-dependent, and approximately 40% of tumours smaller than 2 cm evade detection. Convolutional neural networks (CNNs) have shown promise in image analysis, but the networ...
Aged
Sensitivity and Specificity
Male
Tomography, X-Ray Computed
Female
Reproducibility of Results
Taiwan
Diagnosis, Differential
Pancreatic Neoplasms
Radiographic Image Interpretation, Computer-Assisted
Middle Aged
Deep Learning
Contrast Media
Humans
Retrospective Studies
Pancreas
Racial Groups
Radiographic Image Enhancement
Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow Considerations.
Journal of thoracic imaging
May 1, 2020
The constantly increasing number of computed tomography (CT) examinations poses major challenges for radiologists. In this article, the additional benefits and potential of an artificial intelligence (AI) analysis platform for chest CT examinations i...
Machine Learning and Deep Neural Network Applications in the Thorax: Pulmonary Embolism, Chronic Thromboembolic Pulmonary Hypertension, Aorta, and Chronic Obstructive Pulmonary Disease.
Journal of thoracic imaging
May 1, 2020
The radiologic community is rapidly integrating a revolution that has not fully entered daily practice. It necessitates a close collaboration between computer scientists and radiologists to move from concepts to practical applications. This article r...
Artificial Intelligence-based Fully Automated Per Lobe Segmentation and Emphysema-quantification Based on Chest Computed Tomography Compared With Global Initiative for Chronic Obstructive Lung Disease Severity of Smokers.
Journal of thoracic imaging
May 1, 2020
OBJECTIVES: The objective of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for the fully automated per lobe segmentation and emphysema quantification (EQ) on chest-computed tomography as it compares to the Globa...
Aged
Aged, 80 and over
Pulmonary Emphysema
Humans
Young Adult
Radiography, Thoracic
Artificial Intelligence
Retrospective Studies
Smokers
Tomography, X-Ray Computed
Middle Aged
Adult
Female
Severity of Illness Index
Lung
Pulmonary Disease, Chronic Obstructive
Radiographic Image Interpretation, Computer-Assisted
Male
Artificial Intelligence in Diagnostic Imaging: Status Quo, Challenges, and Future Opportunities.
Journal of thoracic imaging
May 1, 2020
In this review article, the current and future impact of artificial intelligence (AI) technologies on diagnostic imaging is discussed, with a focus on cardio-thoracic applications. The processing of imaging data is described at 4 levels of increasing...
Machine Learning and Deep Neural Networks Applications in Computed Tomography for Coronary Artery Disease and Myocardial Perfusion.
Journal of thoracic imaging
May 1, 2020
During the latest years, artificial intelligence, and especially machine learning (ML), have experienced a growth in popularity due to their versatility and potential in solving complex problems. In fact, ML allows the efficient handling of big volum...
Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography.
Journal of thoracic imaging
May 1, 2020
PURPOSE: The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC...
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