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 Deep Learning Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Humans COVID-19 SARS-CoV-2 Lung
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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 ...
Humans Tomography, X-Ray Computed Middle Aged Lung Deep Learning Female Aged Male Lung Neoplasms Follow-Up Studies Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic
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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...
Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted Lung Diseases Algorithms Radiography, Thoracic Humans Neural Networks, Computer Lung Deep Learning
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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...
Deep Learning Medical Informatics Radiography, Thoracic Humans Algorithms
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Applications of Artificial Intelligence in Acute Thoracic Imaging.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes Feb 19, 2025
The applications of artificial intelligence (AI) in radiology are rapidly advancing with AI algorithms being used in a wide range of disease pathologies and clinical settings. Acute thoracic pathologies including rib fractures, pneumothoraces, and ac...
Acute Disease Radiography, Thoracic Thoracic Diseases Humans Artificial Intelligence
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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...
Humans Artificial Intelligence Female Middle Aged Body Mass Index Bayes Theorem Obesity Radiography, Thoracic Aged Adult Male
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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....
COVID-19 Female SARS-CoV-2 Male Humans Bias Artificial Intelligence Radiography, Thoracic
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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 ...
Tomography, X-Ray Computed Female Aged Feedback Male Reinforcement, Psychology Artificial Intelligence Radiologists Middle Aged Machine Learning Radiography, Thoracic Retrospective Studies Humans Large Language Models
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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...
Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Databases, Factual Lung Unsupervised Machine Learning Humans Algorithms
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Normative values for lung, bronchial sizes, and bronchus-artery ratios in chest CT scans: from infancy into young adulthood.

European radiology Feb 1, 2025
OBJECTIVE: To estimate the developmental trends of quantitative parameters obtained from chest computed tomography (CT) and to provide normative values on dimensions of bronchi and arteries, as well as bronchus-artery (BA) ratios from preschool age t...
Infant Lung Adolescent Humans Infant, Newborn Radiography, Thoracic Child, Preschool Tomography, X-Ray Computed Young Adult Bronchi Female Child Male Pulmonary Artery Reference Values
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