AIMC Topic: Radiography, Thoracic

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Integrating VAI-Assisted Quantified CXRs and Multimodal Data to Assess the Risk of Mortality.

Journal of imaging informatics in medicine Oct 24, 2024
To address the unmet need for a widely available examination for mortality prediction, this study developed a foundation visual artificial intelligence (VAI) to enhance mortality risk stratification using chest X-rays (CXRs). The VAI employed deep le...
Humans Artificial Intelligence Retrospective Studies Radiography, Thoracic Middle Aged Risk Assessment Deep Learning Proportional Hazards Models Female Aged Male
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Real-World evaluation of an AI triaging system for chest X-rays: A prospective clinical study.

European journal of radiology Oct 10, 2024
Chest X-rays (CXRs) are crucial for diagnosing and managing lung conditions. While CXR is a common and cost-effective diagnostic tool, interpreting the high volume of CXRs is challenging due to workforce limitations. Artificial intelligence (AI) offe...
Lung Diseases Radiography, Thoracic Young Adult Adult Humans Sensitivity and Specificity Male Middle Aged Adolescent Female Triage Reproducibility of Results Aged Aged, 80 and over Artificial Intelligence Software Prospective Studies
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Optimizing Catheter Verification: An Understandable AI Model for Efficient Assessment of Central Venous Catheter Placement in Chest Radiography.

Investigative radiology Oct 9, 2024
PURPOSE: Accurate detection of central venous catheter (CVC) misplacement is crucial for patient safety and effective treatment. Existing artificial intelligence (AI) often grapple with the limitations of label inaccuracies and output interpretations...
Sensitivity and Specificity Male Deep Learning Catheterization, Central Venous Central Venous Catheters Radiography, Thoracic Humans Artificial Intelligence Female
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Implementation of an AI Algorithm in Clinical Practice to Reduce Missed Incidental Pulmonary Embolisms on Chest CT and Its Impact on Short-Term Survival.

Investigative radiology Oct 9, 2024
OBJECTIVES: A substantial number of incidental pulmonary embolisms (iPEs) in computed tomography scans are missed by radiologists in their daily routine. This study analyzes the radiological reports of iPE cases before and after implementation of an ...
Artificial Intelligence Algorithms Retrospective Studies Tomography, X-Ray Computed Humans Pulmonary Embolism Radiography, Thoracic Incidental Findings Missed Diagnosis Survival Rate Sensitivity and Specificity Male Middle Aged Female Aged Aged, 80 and over
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Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens.

PloS one Oct 9, 2024
The field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment decisions. This rise has led to the development of Explainable AI (XAI) system to improve the transpare...
SARS-CoV-2 Male Deep Learning Pneumonia Coronavirus Infections Radiography, Thoracic Betacoronavirus COVID-19 Algorithms Humans Tomography, X-Ray Computed Artificial Intelligence Female Pandemics
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DeepCOVIDNet-CXR: deep learning strategies for identifying COVID-19 on enhanced chest X-rays.

Biomedizinische Technik. Biomedical engineering Oct 8, 2024
OBJECTIVES: COVID-19 is one of the recent major epidemics, which accelerates its mortality and prevalence worldwide. Most literature on chest X-ray-based COVID-19 analysis has focused on multi-case classification (COVID-19, pneumonia, and normal) by ...
Deep Learning Radiography, Thoracic COVID-19 Databases, Factual SARS-CoV-2 Lung Humans
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Deep Conformal Supervision: Leveraging Intermediate Features for Robust Uncertainty Quantification.

Journal of imaging informatics in medicine Oct 7, 2024
Trustworthiness is crucial for artificial intelligence (AI) models in clinical settings, and a fundamental aspect of trustworthy AI is uncertainty quantification (UQ). Conformal prediction as a robust uncertainty quantification (UQ) framework has bee...
Pneumonia Intracranial Hemorrhages Radiography, Thoracic Uncertainty Humans Artificial Intelligence Deep Learning
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Label refinement network from synthetic error augmentation for medical image segmentation.

Medical image analysis Sep 27, 2024
Deep convolutional neural networks for image segmentation do not learn the label structure explicitly and may produce segmentations with an incorrect structure, e.g., with disconnected cylindrical structures in the segmentation of tree-like structure...
Radiography, Thoracic Computed Tomography Angiography Brain Tomography, X-Ray Computed Imaging, Three-Dimensional Image Processing, Computer-Assisted Humans Neural Networks, Computer Algorithms
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Evaluation of the impact of artificial intelligence-assisted image interpretation on the diagnostic performance of clinicians in identifying pneumothoraces on plain chest X-ray: a multi-case multi-reader study.

Emergency medicine journal : EMJ Sep 25, 2024
BACKGROUND: Artificial intelligence (AI)-assisted image interpretation is a fast-developing area of clinical innovation. Most research to date has focused on the performance of AI-assisted algorithms in comparison with that of radiologists rather tha...
Humans Artificial Intelligence Algorithms Female Sensitivity and Specificity Pneumothorax Male Clinical Competence Radiography, Thoracic
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Radiation dose reduction in pediatric computed tomography (CT) using deep convolutional neural network denoising.

Clinical radiology Sep 21, 2024
AIM: We evaluated the quality of noncontrast chest computed tomography (CT) for pediatric patients at two dose levels with and without denoising using a deep convolutional neural network (CNN).
Tomography, X-Ray Computed Female Male Infant Humans Neural Networks, Computer Retrospective Studies Child Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Radiography, Thoracic Convolutional Neural Networks Adolescent Child, Preschool
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