AIMC Topic: Lung

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Unsupervised 3D Lung Segmentation by Leveraging 2D Segment Anything Model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Lung segmentation is the first important step for lung nodule detection and lung cancer analysis. Deep neural networks have achieved state-of-the-art for most tasks in medical image analysis, including lung segmentation. However, training a deep lear...
Humans Neural Networks, Computer Algorithms Imaging, Three-Dimensional Lung Neoplasms Unsupervised Machine Learning Lung Deep Learning Tomography, X-Ray Computed
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[Research of electrical impedance tomography based on multilayer artificial neural network optimized by Hadamard product for human-chest models].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Jun 25, 2024
Electrical impedance tomography (EIT) is a non-radiation, non-invasive visual diagnostic technique. In order to improve the imaging resolution and the removing artifacts capability of the reconstruction algorithms for electrical impedance imaging in ...
Tomography Electric Impedance Thorax Humans Neural Networks, Computer Lung Image Processing, Computer-Assisted Algorithms
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[Artificial intelligence research advances in discrimination and diagnosis of pulmonary ground-glass nodules].

Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases Jun 12, 2024
Lung cancer, which accounts for about 18% of all cancer-related deaths worldwide, has a dismal 5-year survival rate of less than 20%. Survival rates for early-stage lung cancers (stages IA1, IA2, IA3, and IB, according to the TNM staging system) are ...
Humans Artificial Intelligence Lung Deep Learning Lung Neoplasms Algorithms Tomography, X-Ray Computed Multiple Pulmonary Nodules
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A back propagation neural network based respiratory motion modelling method.

The international journal of medical robotics + computer assisted surgery : MRCAS Jun 1, 2024
BACKGROUND: This study presents the development of a backpropagation neural network-based respiratory motion modelling method (BP-RMM) for precisely tracking arbitrary points within lung tissue throughout free respiration, encompassing deep inspirati...
Movement Four-Dimensional Computed Tomography Reproducibility of Results Lung Motion Respiration Image Processing, Computer-Assisted Neural Networks, Computer Artificial Intelligence Humans Algorithms
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Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis.

Cancer medicine Apr 1, 2024
BACKGROUND: The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based o...
Lung Artificial Intelligence Lung Neoplasms Prognosis Early Detection of Cancer Tomography, X-Ray Computed Humans
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Advanced Technologies in Radiation Research.

Radiation research Apr 1, 2024
The U.S. Government is committed to maintaining a robust research program that supports a portfolio of scientific experts who are investigating the biological effects of radiation exposure. On August 17 and 18, 2023, the Radiation and Nuclear Counter...
Skin Radiation Injuries National Institute of Allergy and Infectious Diseases (U.S.) Lung United States Humans Artificial Intelligence
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A systematic review on deep learning-based automated cancer diagnosis models.

Journal of cellular and molecular medicine Mar 1, 2024
Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated dia...
Deep Learning Lung Diagnosis, Computer-Assisted Neural Networks, Computer Tomography, X-Ray Computed Humans Neoplasms
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Deep Learning-Based Kernel Adaptation Enhances Quantification of Emphysema on Low-Dose Chest CT for Predicting Long-Term Mortality.

Investigative radiology Mar 1, 2024
OBJECTIVES: The aim of this study was to ascertain the predictive value of quantifying emphysema using low-dose computed tomography (LDCT) post deep learning-based kernel adaptation on long-term mortality.
Tomography, X-Ray Computed Male Deep Learning Pulmonary Emphysema Lung Humans Retrospective Studies Emphysema
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Accurate diagnosis of COVID-19 from lung CT images using transfer learning.

European review for medical and pharmacological sciences Feb 1, 2024
OBJECTIVE: In this study, it is aimed to classify data by feature extraction from tomographic images for the diagnosis of COVID-19 using image processing and transfer learning.
Humans Datasets as Topic Machine Learning Artificial Intelligence Lung COVID-19 Tomography, X-Ray Computed COVID-19 Testing
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Deep Learning-based Fibrosis Extent on Computed Tomography Predicts Outcome of Fibrosing Interstitial Lung Disease Independent of Visually Assessed Computed Tomography Pattern.

Annals of the American Thoracic Society Feb 1, 2024
Radiologic pattern has been shown to predict survival in patients with fibrosing interstitial lung disease. The additional prognostic value of fibrosis extent by quantitative computed tomography (CT) is unknown. We hypothesized that fibrosis extent...
Lung Humans Prognosis Deep Learning Lung Diseases, Interstitial Retrospective Studies Idiopathic Pulmonary Fibrosis Tomography, X-Ray Computed
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