AIMC Topic: Radiography, Thoracic

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Automated classification of chest X-rays: a deep learning approach with attention mechanisms.

BMC medical imaging Mar 4, 2025
BACKGROUND: Pulmonary diseases such as COVID-19 and pneumonia, are life-threatening conditions, that require prompt and accurate diagnosis for effective treatment. Chest X-ray (CXR) has become the most common alternative method for detecting pulmonar...
Pneumonia, Viral Humans COVID-19 SARS-CoV-2 Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Lung Deep Learning
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Metastatic Lung Lesion Changes in Follow-up Chest CT: The Advantage of Deep Learning Simultaneous Analysis of Prior and Current Scans With SimU-Net.

Journal of thoracic imaging Mar 1, 2025
PURPOSE: Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for ...
Lung Neoplasms Female Follow-Up Studies Middle Aged Radiographic Image Interpretation, Computer-Assisted Aged Lung Humans Radiography, Thoracic Male Deep Learning Tomography, X-Ray Computed
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Multi-axis transformer based U-Net with class balanced ensemble model for lung disease classification using X-ray images.

Journal of X-ray science and technology Feb 26, 2025
Chest X-rays are an essential diagnostic tool for identifying chest disorders because of its high sensitivity in detecting pathological anomalies in the lungs. Classification models based on conventional Convolutional Neural Networks (CNNs) are adve...
Lung Deep Learning Image Processing, Computer-Assisted Radiography, Thoracic Radiographic Image Interpretation, Computer-Assisted Lung Diseases Humans Neural Networks, Computer Algorithms
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Missing-modality enabled multi-modal fusion architecture for medical data.

Journal of biomedical informatics Feb 21, 2025
BACKGROUND: Fusion of multi-modal data can improve the performance of deep learning models. However, missing modalities are common in medical data due to patient specificity, which is detrimental to the performance of multi-modal models in applicatio...
Humans Radiography, Thoracic Algorithms Deep Learning Medical Informatics
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Development and clinical evaluation of an AI-assisted respiratory state classification system for chest X-rays: A BMI-Specific approach.

Computers in biology and medicine Feb 16, 2025
PURPOSE: In this study, we aimed to develop and clinically evaluate an artificial intelligence (AI)-assisted support system for determining inhalation and exhalation states on chest X-ray images, focusing on the specific challenge of respiratory stat...
Female Bayes Theorem Aged Radiography, Thoracic Humans Adult Body Mass Index Artificial Intelligence Male Obesity Middle Aged
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Bias Amplification to Facilitate the Systematic Evaluation of Bias Mitigation Methods.

IEEE journal of biomedical and health informatics Feb 10, 2025
The future of artificial intelligence (AI) safety is expected to include bias mitigation methods from development to application. The complexity and integration of these methods could grow in conjunction with advances in AI and human-AI interactions....
Humans Artificial Intelligence Male Bias Radiography, Thoracic COVID-19 Female SARS-CoV-2
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Aligning large language models with radiologists by reinforcement learning from AI feedback for chest CT reports.

European journal of radiology Feb 6, 2025
BACKGROUND: Large language models (LLMs) often struggle to fully capture the nuanced preferences and clinical judgement of radiologists in medical report summarization even when fine-tuned on massive medical reports. This could lead to the generated ...
Reinforcement, Psychology Radiologists Radiography, Thoracic Large Language Models Tomography, X-Ray Computed Female Aged Feedback Male Middle Aged Artificial Intelligence Machine Learning Retrospective Studies Humans
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Facing Differences of Similarity: Intra- and Inter-Correlation Unsupervised Learning for Chest X-Ray Anomaly Detection.

IEEE transactions on medical imaging Feb 4, 2025
Anomaly detection can significantly aid doctors in interpreting chest X-rays. The commonly used strategy involves utilizing the pre-trained network to extract features from normal data to establish feature representations. However, when a pre-trained...
Humans Unsupervised Machine Learning Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Algorithms Databases, Factual Lung
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The use of artificial intelligence to aid the diagnosis of lung cancer - A retrospective-cohort study.

Radiography (London, England : 1995) Jan 30, 2025
INTRODUCTION: AI software in the form of deep learning-based automatic detection (DLAD) algorithms for chest X-ray (CXR) interpretation have shown success in early detection of lung cancer (LC), however, there remains uncertainty related to clinical ...
Algorithms Retrospective Studies Tomography, X-Ray Computed Humans Sensitivity and Specificity Artificial Intelligence Male Early Detection of Cancer Deep Learning Female Radiographic Image Interpretation, Computer-Assisted Lung Neoplasms Aged Radiography, Thoracic Aged, 80 and over
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Clinical Validation of an AI System for Pneumoconiosis Detection Using Chest X-rays.

Journal of occupational and environmental medicine Jan 28, 2025
OBJECTIVE: The aims of the study were to develop and evaluate "eTóraxLaboral," an intelligent platform for detecting signs of pneumoconiosis in chest radiographs and to assess its predictive capacity.
Radiography, Thoracic Sensitivity and Specificity Male Female ROC Curve Humans Middle Aged Aged Pneumoconiosis Artificial Intelligence Adult Predictive Value of Tests Retrospective Studies
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