AIMC Topic: Radiography, Thoracic
Clear Filters Showing 491 to 500 of 591 articles
Phantom evaluation of feasibility and applicability of artificial intelligence based pulmonary nodule detection in chest radiographs.
Medicine
Nov 22, 2024
The aim of our study was to evaluate the specific performance of an artificial intelligence (AI) algorithm for lung nodule detection in chest radiography for a larger number of nodules of different sizes and densities using a standardized phantom app...
Care to Explain? AI Explanation Types Differentially Impact Chest Radiograph Diagnostic Performance and Physician Trust in AI.
Radiology
Nov 1, 2024
Background It is unclear whether artificial intelligence (AI) explanations help or hurt radiologists and other physicians in AI-assisted radiologic diagnostic decision-making. Purpose To test whether the type of AI explanation and the correctness and...
DEEP LEARNING-BASED FRAMEWORK TO DETERMINE THE DEGREE OF COVID-19 INFECTIONS FROM CHEST X-RAY.
Georgian medical news
Oct 1, 2024
The corona virus disease-19 (COVID-19) epidemic, the whole globe is suffering from a medical condition catastrophe that is unprecedented in scale. As the coronavirus spreads, scientists are worried about offering or helping in the supply of remedies ...
Deep Learning-Based Reconstruction Algorithm With Lung Enhancement Filter for Chest CT: Effect on Image Quality and Ground Glass Nodule Sharpness.
Korean journal of radiology
Sep 1, 2024
OBJECTIVE: To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone.
Unveiling Disease Progression in Chest Radiographs through AI.
Radiology. Artificial intelligence
Sep 1, 2024
Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning.
Radiology. Artificial intelligence
Sep 1, 2024
Purpose To develop a machine learning approach for classifying disease progression in chest radiographs using weak labels automatically derived from radiology reports. Materials and Methods In this retrospective study, a twin neural network was devel...
External Testing of a Deep Learning Model to Estimate Biologic Age Using Chest Radiographs.
Radiology. Artificial intelligence
Sep 1, 2024
Purpose To assess the prognostic value of a deep learning-based chest radiographic age (hereafter, CXR-Age) model in a large external test cohort of Asian individuals. Materials and Methods This single-center, retrospective study included chest radio...
Open Access Data and Deep Learning for Cardiac Device Identification on Standard DICOM and Smartphone-based Chest Radiographs.
Radiology. Artificial intelligence
Sep 1, 2024
Purpose To develop and evaluate a publicly available deep learning model for segmenting and classifying cardiac implantable electronic devices (CIEDs) on Digital Imaging and Communications in Medicine (DICOM) and smartphone-based chest radiographs. M...
Artificial intelligence software for analysing chest X-ray images to identify suspected lung cancer: an evidence synthesis early value assessment.
Health technology assessment (Winchester, England)
Aug 1, 2024
BACKGROUND: Lung cancer is one of the most common types of cancer in the United Kingdom. It is often diagnosed late. The 5-year survival rate for lung cancer is below 10%. Early diagnosis may improve survival. Software that has an artificial intellig...
Using AI to Identify Unremarkable Chest Radiographs for Automatic Reporting.
Radiology
Aug 1, 2024
Background Radiology practices have a high volume of unremarkable chest radiographs and artificial intelligence (AI) could possibly improve workflow by providing an automatic report. Purpose To estimate the proportion of unremarkable chest radiograph...
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