AIMC Topic: Solitary Pulmonary Nodule

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Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing artificial intelligence-driven radiomics analysis.

BMC medical imaging Jan 17, 2025
OBJECTIVE: In clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially when 3D consolidation-to-tumor ratio (CTR) ≥ 50%, as malignant ones are more invasive. This study aim...
Artificial Intelligence Retrospective Studies Tomography, X-Ray Computed Female Humans Middle Aged ROC Curve Lung Neoplasms Logistic Models Aged Adult Radiomics Male Multiple Pulmonary Nodules Solitary Pulmonary Nodule Diagnosis, Differential
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Deep learning-based image domain reconstruction enhances image quality and pulmonary nodule detection in ultralow-dose CT with adaptive statistical iterative reconstruction-V.

European radiology Jan 10, 2025
OBJECTIVES: To evaluate the image quality and lung nodule detectability of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) post-processed using a deep learning image reconstruction (DLIR)-based image domain comp...
Radiation Dosage Male Middle Aged Deep Learning Image Processing, Computer-Assisted Lung Neoplasms Multiple Pulmonary Nodules Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Tomography, X-Ray Computed Female Aged Humans
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Malignancy risk stratification for pulmonary nodules: comparing a deep learning approach to multiparametric statistical models in different disease groups.

European radiology Jan 2, 2025
OBJECTIVES: Incidentally detected pulmonary nodules present a challenge in clinical routine with demand for reliable support systems for risk classification. We aimed to evaluate the performance of the lung-cancer-prediction-convolutional-neural-netw...
Female Multiple Pulmonary Nodules Aged Solitary Pulmonary Nodule Humans Middle Aged Adult Models, Statistical Retrospective Studies Risk Assessment Male Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Deep Learning Lung Neoplasms
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Towards safe and reliable deep learning for lung nodule malignancy estimation using out-of-distribution detection.

Computers in biology and medicine Dec 30, 2024
Artificial Intelligence (AI) models may fail or suffer from reduced performance when applied to unseen data that differs from the training data distribution, referred to as dataset shift. Automatic detection of out-of-distribution (OOD) data contribu...
Tomography, X-Ray Computed Deep Learning Lung Neoplasms Solitary Pulmonary Nodule Humans
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Fusion-driven semi-supervised learning-based lung nodules classification with dual-discriminator and dual-generator generative adversarial network.

BMC medical informatics and decision making Dec 24, 2024
BACKGROUND: The detection and classification of lung nodules are crucial in medical imaging, as they significantly impact patient outcomes related to lung cancer diagnosis and treatment. However, existing models often suffer from mode collapse and po...
Humans Neural Networks, Computer Tomography, X-Ray Computed Lung Neoplasms Solitary Pulmonary Nodule Supervised Machine Learning
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Spectrum of errors in nodule detection and characterization using machine learning: A pictorial essay.

Current problems in diagnostic radiology Dec 10, 2024
In academic and research settings, computer-aided nodule detection software has been shown to increase accuracy, efficiency, and throughput. However, radiologists need to be familiar with the spectrum of errors that can occur when these algorithms ar...
Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Algorithms Tomography, X-Ray Computed Diagnostic Errors Lung Neoplasms Humans Machine Learning
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Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision.

IEEE transactions on neural networks and learning systems Dec 2, 2024
Accurate lung lesion segmentation from computed tomography (CT) images is crucial to the analysis and diagnosis of lung diseases, such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and the lack of high-quality labeli...
Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Supervised Machine Learning COVID-19 Algorithms Databases, Factual Tomography, X-Ray Computed SARS-CoV-2 Lung Image Processing, Computer-Assisted Lung Neoplasms Humans Neural Networks, Computer
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External validation of the performance of commercially available deep-learning-based lung nodule detection on low-dose CT images for lung cancer screening in Japan.

Japanese journal of radiology Nov 30, 2024
PURPOSE: Artificial intelligence (AI) algorithms for lung nodule detection have been developed to assist radiologists. However, external validation of its performance on low-dose CT (LDCT) images is insufficient. We examined the performance of the co...
Middle Aged Aged Japan Multiple Pulmonary Nodules Lung Aged, 80 and over Radiation Dosage Solitary Pulmonary Nodule Deep Learning Early Detection of Cancer Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted Sensitivity and Specificity Male Humans Retrospective Studies Tomography, X-Ray Computed Female
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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...
Reproducibility of Results Risk Factors Aged Biomarkers, Tumor Male Middle Aged Lung Neoplasms Multiple Pulmonary Nodules Decision Support Techniques Retrospective Studies Tomography, X-Ray Computed Female Humans Artificial Intelligence Solitary Pulmonary Nodule Neoplasm Invasiveness Predictive Value of Tests
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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...
Observer Variation Radiography, Thoracic Retrospective Studies Tomography, X-Ray Computed Female Reproducibility of Results Humans Lung Neoplasms Aged Artificial Intelligence Solitary Pulmonary Nodule Sensitivity and Specificity Radiologists Male Radiographic Image Interpretation, Computer-Assisted Middle Aged
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