AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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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...
Adult Radiography, Thoracic Pulmonary Disease, Chronic Obstructive Smokers Male Female Middle Aged Aged Lung Humans Artificial Intelligence Aged, 80 and over Severity of Illness Index Retrospective Studies Young Adult Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Pulmonary Emphysema
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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...
Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Workload Humans Artificial Intelligence
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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...
Coronary Artery Disease Computed Tomography Angiography Myocardial Perfusion Imaging Neural Networks, Computer Humans Machine Learning Coronary Angiography Radiographic Image Interpretation, Computer-Assisted
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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...
Time Coronary Artery Disease Coronary Vessels Vascular Calcification Humans Deep Learning Computed Tomography Angiography Neural Networks, Computer Coronary Angiography Artificial Intelligence Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Reproducibility of Results
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Machine Learning and Deep Neural Networks Applications in Coronary Flow Assessment: The Case of Computed Tomography Fractional Flow Reserve.

Journal of thoracic imaging May 1, 2020
Coronary computed tomography angiography (cCTA) is a reliable and clinically proven method for the evaluation of coronary artery disease. cCTA data sets can be used to derive fractional flow reserve (FFR) as CT-FFR. This method has respectable result...
Humans Coronary Artery Disease Coronary Vessels Computed Tomography Angiography Neural Networks, Computer Fractional Flow Reserve, Myocardial Machine Learning Coronary Angiography Radiographic Image Interpretation, Computer-Assisted
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Utilizing Artificial Intelligence to Determine Bone Mineral Density Via Chest Computed Tomography.

Journal of thoracic imaging May 1, 2020
PURPOSE: The purpose of this study was to validate the accuracy of an artificial intelligence (AI) prototype application in determining bone mineral density (BMD) from chest computed tomography (CT), as compared with dual-energy x-ray absorptiometry ...
Male Middle Aged Radiographic Image Interpretation, Computer-Assisted Lumbar Vertebrae Aged Aged, 80 and over Adult Radiography, Thoracic Absorptiometry, Photon Humans Retrospective Studies Artificial Intelligence Tomography, X-Ray Computed Bone Density Female Reproducibility of Results
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Machine Learning and Deep Neural Networks: Applications in Patient and Scan Preparation, Contrast Medium, and Radiation Dose Optimization.

Journal of thoracic imaging May 1, 2020
Artificial intelligence (AI) algorithms are dependent on a high amount of robust data and the application of appropriate computational power and software. AI offers the potential for major changes in cardiothoracic imaging. Beyond image processing, m...
Machine Learning Tomography, X-Ray Computed Imaging, Three-Dimensional Contrast Media Humans Neural Networks, Computer Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Radiographic Image Enhancement
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Prediction of Pulmonary to Systemic Flow Ratio in Patients With Congenital Heart Disease Using Deep Learning-Based Analysis of Chest Radiographs.

JAMA cardiology Apr 1, 2020
IMPORTANCE: Chest radiography is a useful noninvasive modality to evaluate pulmonary blood flow status in patients with congenital heart disease. However, the predictive value of chest radiography is limited by the subjective and qualitive nature of ...
Pulmonary Circulation Infant Humans Deep Learning Retrospective Studies Radiographic Image Interpretation, Computer-Assisted Child, Preschool Female Radiography, Thoracic Child Male Heart Defects, Congenital Blood Circulation
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Low-Dose Abdominal CT Using a Deep Learning-Based Denoising Algorithm: A Comparison with CT Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm.

Korean journal of radiology Mar 1, 2020
OBJECTIVE: To compare the image quality of low-dose (LD) computed tomography (CT) obtained using a deep learning-based denoising algorithm (DLA) with LD CT images reconstructed with a filtered back projection (FBP) and advanced modeled iterative reco...
Radiation Dosage Signal-To-Noise Ratio Middle Aged Deep Learning ROC Curve Abdomen Aged Body Mass Index Young Adult Humans Area Under Curve Adult Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Male Female
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Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study.

Abdominal radiology (New York) Mar 1, 2020
PURPOSE: To predict the histologic grade of small clear cell renal cell carcinomas (ccRCCs) using texture analysis and machine learning algorithms.
Machine Learning Retrospective Studies Tomography, X-Ray Computed Female Radiographic Image Interpretation, Computer-Assisted Male Humans Kidney Neoplasms Middle Aged Carcinoma, Renal Cell Pilot Projects Neoplasm Grading
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