AIMC Topic: Solitary Pulmonary Nodule

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A Predictive Model Integrating AI Recognition Technology and Biomarkers for Lung Nodule Assessment.

The Thoracic and cardiovascular surgeon Nov 26, 2024
BACKGROUND:  Lung cancer is the most prevalent and lethal cancer globally, necessitating accurate differentiation between benign and malignant pulmonary nodules to guide treatment decisions. This study aims to develop a predictive model that integrat...
Neoplasm Invasiveness Predictive Value of Tests Middle Aged Lung Neoplasms Multiple Pulmonary Nodules Solitary Pulmonary Nodule Retrospective Studies Tomography, X-Ray Computed Female Reproducibility of Results Risk Factors Aged Biomarkers, Tumor Male Humans Artificial Intelligence Decision Support Techniques
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Effect of Artificial Intelligence as a Second Reader on the Lung Nodule Detection and Localization Accuracy of Radiologists and Non-radiology Physicians in Chest Radiographs: A Multicenter Reader Study.

Academic radiology Nov 25, 2024
RATIONALE AND OBJECTIVES: Missed nodules in chest radiographs (CXRs) are common occurrences. We assessed the effect of artificial intelligence (AI) as a second reader on the accuracy of radiologists and non-radiology physicians in lung nodule detecti...
Radiography, Thoracic Radiologists Radiographic Image Interpretation, Computer-Assisted Male Observer Variation Middle Aged Lung Neoplasms Solitary Pulmonary Nodule Female Reproducibility of Results Aged Sensitivity and Specificity Humans Artificial Intelligence Retrospective Studies Tomography, X-Ray Computed
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An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study.

European radiology experimental Nov 20, 2024
BACKGROUND: To retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose computed tomography (ULDCT) performed at the emergency department (ED).
Humans Artificial Intelligence Female Aged Male Middle Aged Algorithms Prospective Studies Retrospective Studies Tomography, X-Ray Computed Solitary Pulmonary Nodule Emergency Service, Hospital Radiation Dosage Deep Learning Lung Neoplasms Multiple Pulmonary Nodules
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Prediction of benign and malignant pulmonary nodules using preoperative CT features: using PNI-GARS as a predictor.

Frontiers in immunology Nov 20, 2024
PURPOSE: The aim of this study was to develop and validate a prediction model for classification of pulmonary nodules based on preoperative CT imaging.
Male Tomography, X-Ray Computed Solitary Pulmonary Nodule Middle Aged Humans Female Lung Neoplasms Aged Machine Learning Multiple Pulmonary Nodules Adult Retrospective Studies
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A novel benign and malignant classification model for lung nodules based on multi-scale interleaved fusion integrated network.

Scientific reports Nov 11, 2024
One of the precursors of lung cancer is the presence of lung nodules, and accurate identification of their benign or malignant nature is important for the long-term survival of patients. With the development of artificial intelligence, deep learning ...
Neural Networks, Computer Algorithms Deep Learning Lung Neoplasms Solitary Pulmonary Nodule Humans
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Lung nodule classification using radiomics model trained on degraded SDCT images.

Computer methods and programs in biomedicine Oct 23, 2024
BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) screening has shown promise in reducing lung cancer mortality; however, it suffers from high false positive rates and a scarcity of available annotated datasets. To overcome these challeng...
Lung Neoplasms Logistic Models Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Humans Reproducibility of Results Machine Learning Radiomics Algorithms Image Processing, Computer-Assisted Radiation Dosage
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An anthropomorphic diagnosis system of pulmonary nodules using weak annotation-based deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Oct 10, 2024
The accurate categorization of lung nodules in CT scans is an essential aspect in the prompt detection and diagnosis of lung cancer. The categorization of grade and texture for nodules is particularly significant since it can aid radiologists and cli...
Lung Neoplasms Solitary Pulmonary Nodule Diagnosis, Differential Radiographic Image Interpretation, Computer-Assisted Humans Tomography, X-Ray Computed Deep Learning
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Impact of artificial intelligence assistance on pulmonary nodule detection and localization in chest CT: a comparative study among radiologists of varying experience levels.

Scientific reports Sep 28, 2024
The study aimed to evaluate the impact of AI assistance on pulmonary nodule detection rates among radiology residents and senior radiologists, along with assessing the effectiveness of two different commercialy available AI software systems in improv...
Adult Male Middle Aged Lung Neoplasms Tomography, X-Ray Computed Female Humans Aged Multiple Pulmonary Nodules Artificial Intelligence Software Solitary Pulmonary Nodule Radiologists Radiographic Image Interpretation, Computer-Assisted
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GC-WIR : 3D global coordinate attention wide inverted ResNet network for pulmonary nodules classification.

BMC pulmonary medicine Sep 20, 2024
PURPOSE: Currently, deep learning methods for the classification of benign and malignant lung nodules encounter challenges encompassing intricate and unstable algorithmic models, limited data adaptability, and an abundance of model parameters.To tack...
Multiple Pulmonary Nodules Solitary Pulmonary Nodule Deep Learning Imaging, Three-Dimensional Humans Lung Neoplasms Neural Networks, Computer Algorithms Tomography, X-Ray Computed
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Artificial intelligence-assisted quantitative CT parameters in predicting the degree of risk of solitary pulmonary nodules.

Annals of medicine Sep 19, 2024
INTRODUCTION: Artificial intelligence (AI) shows promise for evaluating solitary pulmonary nodules (SPNs) on computed tomography (CT). Accurately determining cancer invasiveness can guide treatment. We aimed to investigate quantitative CT parameters ...
Retrospective Studies Humans Tomography, X-Ray Computed Lung Neoplasms Aged, 80 and over Adult Male Middle Aged Female Aged Carcinoma, Non-Small-Cell Lung Solitary Pulmonary Nodule Neoplasm Invasiveness Artificial Intelligence
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