AIMC Topic: Multiple Pulmonary Nodules

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Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT.

Radiology May 26, 2020
Background Deep learning may help to improve computer-aided detection of volume (CADv) measurement of pulmonary nodules at chest CT. Purpose To determine the efficacy of a deep learning method for improving CADv for measuring the solid and ground-gla...
Neural Networks, Computer Retrospective Studies Tomography, X-Ray Computed Female Humans Male Middle Aged Lung Lung Neoplasms Multiple Pulmonary Nodules Aged Aged, 80 and over Adult Sensitivity and Specificity
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External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules.

Thorax Mar 5, 2020
BACKGROUND: Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intelligence (AI) offers an opportunity to improve risk prediction. Here we compare the performance of an AI...
Neural Networks, Computer Artificial Intelligence Algorithms Databases, Factual Humans Area Under Curve Neoplasm Staging Cell Transformation, Neoplastic Incidence Cohort Studies ROC Curve Lung Neoplasms Multiple Pulmonary Nodules Early Detection of Cancer Neoplasm Invasiveness Predictive Value of Tests Prognosis Adult Retrospective Studies Male Female Middle Aged Aged Risk Assessment
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Test-retest reproducibility of a deep learning-based automatic detection algorithm for the chest radiograph.

European radiology Jan 3, 2020
OBJECTIVES: To perform test-retest reproducibility analyses for deep learning-based automatic detection algorithm (DLAD) using two stationary chest radiographs (CRs) with short-term intervals, to analyze influential factors on test-retest variations,...
Middle Aged Deep Learning Lung Neoplasms Multiple Pulmonary Nodules Female Reproducibility of Results Aged Male Radiographic Image Interpretation, Computer-Assisted Pneumonectomy Radiography, Thoracic Humans Algorithms Retrospective Studies
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Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance.

European radiology Dec 10, 2019
OBJECTIVES: To evaluate the diagnostic performance of a deep learning algorithm for automated detection of small F-FDG-avid pulmonary nodules in PET scans, and to assess whether novel block sequential regularized expectation maximization (BSREM) reco...
Multiple Pulmonary Nodules Solitary Pulmonary Nodule Positron Emission Tomography Computed Tomography Radiopharmaceuticals Fluorodeoxyglucose F18 Middle Aged Humans Deep Learning Retrospective Studies Image Processing, Computer-Assisted Artificial Intelligence Female Lung Neoplasms Aged, 80 and over Algorithms Adult Aged Sensitivity and Specificity Male
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Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades' development course and future prospect.

Journal of cancer research and clinical oncology Nov 30, 2019
PURPOSE: Lung cancer is the commonest cause of cancer deaths worldwide, and its mortality can be reduced significantly by performing early diagnosis and screening. Since the 1960s, driven by the pressing needs to accurately and effectively interpret ...
Humans Algorithms Decision Support Techniques Tomography, X-Ray Computed Positron-Emission Tomography Deep Learning Image Processing, Computer-Assisted Lung Neoplasms Multiple Pulmonary Nodules
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Intraoperative localization of small pulmonary nodules to assist surgical resection: A novel approach using a surgical navigation puncture robot system.

Thoracic cancer Nov 26, 2019
BACKGROUND: Localization and resection of nonvisible, nonpalpable pulmonary nodules during video-assisted thoracoscopic surgery is challenging. In this study we developed a surgical navigation puncture robot system in order to locate small pulmonary ...
Punctures Intraoperative Care Solitary Pulmonary Nodule Tomography, X-Ray Computed Robotics Animals Surgery, Computer-Assisted Swine Thoracic Surgery, Video-Assisted Multiple Pulmonary Nodules
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Deep Learning for the Classification of Small (≤2 cm) Pulmonary Nodules on CT Imaging: A Preliminary Study.

Academic radiology Nov 25, 2019
RATIONALE AND OBJECTIVES: We aimed to present a deep learning-based malignancy prediction model (CT-lungNET) that is simpler and faster to use in the diagnosis of small (≤2 cm) pulmonary nodules on nonenhanced chest CT and to preliminarily evaluate i...
Lung Neoplasms Multiple Pulmonary Nodules Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Humans Tomography, X-Ray Computed Deep Learning
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Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs.

Radiology Nov 12, 2019
Background Multicenter studies are required to validate the added benefit of using deep convolutional neural network (DCNN) software for detecting malignant pulmonary nodules on chest radiographs. Purpose To compare the performance of radiologists in...
Young Adult Adult Sensitivity and Specificity Male Middle Aged Lung Lung Neoplasms Multiple Pulmonary Nodules Retrospective Studies Female Humans Reproducibility of Results Neural Networks, Computer Solitary Pulmonary Nodule Aged Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic
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Novelties in imaging in pulmonary fibrosis and nodules. A narrative review.

Pulmonology Nov 7, 2019
In recent months two major fields of interest in pulmonary imaging have stood out: pulmonary fibrosis and pulmonary nodules. New guidelines have been released to define pulmonary fibrosis and subsequent studies have proved the value of these changes....
Lung Neoplasms Multiple Pulmonary Nodules Pulmonary Fibrosis Artificial Intelligence Humans Algorithms Tomography, X-Ray Computed Lung
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MR-Forest: A Deep Decision Framework for False Positive Reduction in Pulmonary Nodule Detection.

IEEE journal of biomedical and health informatics Oct 15, 2019
With the development of deep learning methods such as convolutional neural network (CNN), the accuracy of automated pulmonary nodule detection has been greatly improved. However, the high computational and storage costs of the large-scale network hav...
Multiple Pulmonary Nodules Humans Solitary Pulmonary Nodule Tomography, X-Ray Computed Decision Trees Diagnostic Errors Radiographic Image Interpretation, Computer-Assisted Deep Learning
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