AIMC Topic: Multiple Pulmonary Nodules

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Development and Validation of a Risk Stratification Model of Pulmonary Ground-Glass Nodules Based on Complementary Lung-RADS 1.1 and Deep Learning Scores.

Frontiers in public health May 23, 2022
PURPOSE: To assess the value of novel deep learning (DL) scores combined with complementary lung imaging reporting and data system 1.1 (cLung-RADS 1.1) in managing the risk stratification of ground-glass nodules (GGNs) and therefore improving the eff...
Deep Learning Lung Neoplasms Multiple Pulmonary Nodules Tomography, X-Ray Computed Risk Assessment Lung Humans Retrospective Studies
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A Novel Deep Learning Network and Its Application for Pulmonary Nodule Segmentation.

Computational intelligence and neuroscience May 17, 2022
Pulmonary nodules are the early manifestation of lung cancer, which appear as circular shadow of no more than 3 cm on the computed tomography (CT) image. Accurate segmentation of the contours of pulmonary nodules can help doctors improve the efficien...
Humans Tomography, X-Ray Computed Deep Learning Lung Neoplasms Multiple Pulmonary Nodules
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Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population.

PloS one May 5, 2022
OBJECTIVE: In this study, we evaluated a commercially available computer assisted diagnosis system (CAD). The deep learning algorithm of the CAD was trained with a lung cancer screening cohort and developed for detection, classification, quantificati...
Sensitivity and Specificity Lung Deep Learning Computers Retrospective Studies Tomography, X-Ray Computed Early Detection of Cancer Radiographic Image Interpretation, Computer-Assisted Lung Neoplasms Multiple Pulmonary Nodules Solitary Pulmonary Nodule Humans
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Pulmonary nodules detection based on multi-scale attention networks.

Scientific reports Jan 27, 2022
Pulmonary nodules are the main manifestation of early lung cancer. Therefore, accurate detection of nodules in CT images is vital for lung cancer diagnosis. A 3D automatic detection system of pulmonary nodules based on multi-scale attention networks ...
Humans Radiographic Image Interpretation, Computer-Assisted Datasets as Topic Tomography, X-Ray Computed Imaging, Three-Dimensional Lung Multiple Pulmonary Nodules Deep Learning Early Detection of Cancer
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Clinical impact of a deep learning system for automated detection of missed pulmonary nodules on routine body computed tomography including the chest region.

European radiology Jan 9, 2022
OBJECTIVES: To evaluate the clinical impact of a deep learning system (DLS) for automated detection of pulmonary nodules on computed tomography (CT) images as a second reader.
Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Deep Learning Lung Neoplasms Humans Multiple Pulmonary Nodules Retrospective Studies Tomography, X-Ray Computed Sensitivity and Specificity
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Development of a deep learning-based method to diagnose pulmonary ground-glass nodules by sequential computed tomography imaging.

Thoracic cancer Jan 6, 2022
BACKGROUND: Early identification of the malignant propensity of pulmonary ground-glass nodules (GGNs) can relieve the pressure from tracking lesions and personalized treatment adaptation. The purpose of this study was to develop a deep learning-based...
Humans Lung Neoplasms Multiple Pulmonary Nodules Solitary Pulmonary Nodule Retrospective Studies Tomography, X-Ray Computed Deep Learning
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Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm.

PloS one Dec 31, 2021
OBJECTIVE: To explore the feasibility of using random forest (RF) machine learning algorithm in assessing normal and malignant peripheral pulmonary nodules based on in vivo endobronchial optical coherence tomography (EB-OCT).
Inflammation Multiple Pulmonary Nodules Aged, 80 and over Tomography, Optical Coherence Middle Aged Tomography, X-Ray Computed Humans Adult ROC Curve Female Male Machine Learning Image Processing, Computer-Assisted Aged Algorithms
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Re: Clinical evaluation of a deep-learning-based computer-aided detection system for the detection of pulmonary nodules in a large teaching hospital. A reply.

Clinical radiology Dec 14, 2021
Humans Computers Hospitals, Teaching Deep Learning Multiple Pulmonary Nodules Solitary Pulmonary Nodule
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Re: Clinical evaluation of a deep-learning-based computer-aided detection system for the detection of pulmonary nodules in a large teaching hospital.

Clinical radiology Dec 11, 2021
Deep Learning Multiple Pulmonary Nodules Humans Solitary Pulmonary Nodule Hospitals, Teaching Computers
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Clinical evaluation of a deep-learning-based computer-aided detection system for the detection of pulmonary nodules in a large teaching hospital.

Clinical radiology Aug 14, 2021
AIM: To evaluate a deep-learning-based computer-aided detection (DL-CAD) software system for pulmonary nodule detection on computed tomography (CT) images and assess its added value in the clinical practice of a large teaching hospital.
Multiple Pulmonary Nodules Solitary Pulmonary Nodule Humans Radiographic Image Interpretation, Computer-Assisted Aged, 80 and over Retrospective Studies Hospitals, Teaching Female Young Adult Tomography, X-Ray Computed Lung Adult Reproducibility of Results Sensitivity and Specificity Deep Learning Aged Male Adolescent Middle Aged
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