AIMC Topic: Multiple Pulmonary Nodules

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Comparison of Short-Term Outcomes Between Robot-Assisted and Video-Assisted Segmentectomy for Small Pulmonary Nodules: A Propensity Score-Matching Study.

Annals of surgical oncology Feb 11, 2023
BACKGROUND: Our study aimed to compare the short-term outcomes between robot-assisted segmentectomy (RAS) and video-assisted segmentectomy (VAS) for small pulmonary nodules.
Mastectomy, Segmental Thoracic Surgery, Video-Assisted Propensity Score Humans Retrospective Studies Robotics Pneumonectomy Multiple Pulmonary Nodules
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ASO Author Reflections: Which Technique is Superior for Segmentectomy of Small Pulmonary Nodules: Robot-Assisted or Video-Assisted?

Annals of surgical oncology Feb 10, 2023
Humans Lung Neoplasms Robotics Pneumonectomy Carcinoma, Non-Small-Cell Lung Thoracic Surgery, Video-Assisted Multiple Pulmonary Nodules
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Ultra-low-dose CT lung screening with artificial intelligence iterative reconstruction: evaluation via automatic nodule-detection software.

Clinical radiology Feb 3, 2023
AIM: To test the feasibility of ultra-low-dose (ULD) computed tomography (CT) combined with an artificial intelligence iterative reconstruction (AIIR) algorithm for screening pulmonary nodules using computer-assisted diagnosis (CAD).
Artificial Intelligence Software Algorithms Humans Multiple Pulmonary Nodules Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Lung Radiation Dosage
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A Novel Deep Learning Model Based on Multi-Scale and Multi-View for Detection of Pulmonary Nodules.

Journal of digital imaging Dec 21, 2022
Lung cancer manifests as pulmonary nodules in the early stage. Thus, the early and accurate detection of these nodules is crucial for improving the survival rate of patients. We propose a novel two-stage model for lung nodule detection. In the candid...
Lung Neoplasms Multiple Pulmonary Nodules Solitary Pulmonary Nodule Deep Learning China Radiographic Image Interpretation, Computer-Assisted Humans Lung
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Reducing uncertainty in cancer risk estimation for patients with indeterminate pulmonary nodules using an integrated deep learning model.

Computers in biology and medicine Sep 29, 2022
OBJECTIVE: Patients with indeterminate pulmonary nodules (IPN) with an intermediate to a high probability of lung cancer generally undergo invasive diagnostic procedures. Chest computed tomography image and clinical data have been in estimating the p...
Multiple Pulmonary Nodules Uncertainty Retrospective Studies Deep Learning Humans Lung Neoplasms
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Validation of deep learning-based computer-aided detection software use for interpretation of pulmonary abnormalities on chest radiographs and examination of factors that influence readers' performance and final diagnosis.

Japanese journal of radiology Sep 19, 2022
PURPOSE: To evaluate the performance of a deep learning-based computer-aided detection (CAD) software for detecting pulmonary nodules, masses, and consolidation on chest radiographs (CRs) and to examine the effect of readers' experience and data char...
Multiple Pulmonary Nodules Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Deep Learning Lung Neoplasms Retrospective Studies Sensitivity and Specificity Software Computers Humans
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Applying a CT texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis.

Journal of applied clinical medical physics Aug 23, 2022
OBJECTIVE: To investigate the feasibility and accuracy of applying a computed tomography (CT) texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images for classifying pulmonary nodules.
Retrospective Studies Tomography, X-Ray Computed Lung Neoplasms Humans Multiple Pulmonary Nodules Artificial Intelligence Solitary Pulmonary Nodule Deep Learning
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Radiologists with and without deep learning-based computer-aided diagnosis: comparison of performance and interobserver agreement for characterizing and diagnosing pulmonary nodules/masses.

European radiology Jun 25, 2022
OBJECTIVES: To compare the performance of radiologists in characterizing and diagnosing pulmonary nodules/masses with and without deep learning (DL)-based computer-aided diagnosis (CAD).
Observer Variation Solitary Pulmonary Nodule Radiologists Deep Learning Sensitivity and Specificity Lung Neoplasms Humans Computers Diagnosis, Computer-Assisted Multiple Pulmonary Nodules Reproducibility of Results
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Editorial Comment: Artificial Intelligence for Lung Nodules-Progress, but Is It Enough?

AJR. American journal of roentgenology May 25, 2022
Multiple Pulmonary Nodules Artificial Intelligence Lung Humans Neural Networks, Computer
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Artificial Intelligence Improves Radiologist Performance for Predicting Malignancy at Chest CT.

Radiology May 24, 2022
Humans Radiologists Thorax Tomography, X-Ray Computed Multiple Pulmonary Nodules Artificial Intelligence
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