AIMC Topic: Radiography, Thoracic

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Intelligent diagnosis model for chest X-ray images diseases based on convolutional neural network.

BMC medical imaging Jul 2, 2025
To address misdiagnosis caused by feature coupling in multi-label medical image classification, this study introduces a chest X-ray pathology reasoning method. It combines hierarchical attention convolutional networks with a multi-label decoupling lo...
Humans Neural Networks, Computer Radiographic Image Interpretation, Computer-Assisted Lung Diseases Radiography, Thoracic Algorithms Lung Convolutional Neural Networks
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Gradual poisoning of a chest x-ray convolutional neural network with an adversarial attack and AI explainability methods.

Scientific reports Jul 1, 2025
Given artificial intelligence's transformative effects, studying safety is important to ensure it is implemented in a beneficial way. Convolutional neural networks are used in radiology research for prediction but can be corrupted through adversarial...
Radiography, Thoracic Artificial Intelligence Humans Convolutional Neural Networks Neural Networks, Computer Pneumonia
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Artificial Intelligence Iterative Reconstruction for Dose Reduction in Pediatric Chest CT: A Clinical Assessment via Below 3 Years Patients With Congenital Heart Disease.

Journal of thoracic imaging Jul 1, 2025
PURPOSE: To assess the performance of a newly introduced deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in reducing the dose of pediatric chest CT by using the image data of below 3-y...
Humans Artificial Intelligence Prospective Studies Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Radiography, Thoracic Heart Defects, Congenital Tomography, X-Ray Computed Infant Female Lung Male Child, Preschool
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Improved swin transformer-based thorax disease classification with optimal feature selection using chest X-ray.

PloS one Jun 25, 2025
Thoracic diseases, including pneumonia, tuberculosis, lung cancer, and others, pose significant health risks and require timely and accurate diagnosis to ensure proper treatment. Thus, in this research, a model for thorax disease classification using...
Radiographic Image Interpretation, Computer-Assisted Humans Radiography, Thoracic Algorithms Thoracic Diseases Deep Learning
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Deep Learning-enhanced Opportunistic Osteoporosis Screening in Ultralow-Voltage (80 kV) Chest CT: A Preliminary Study.

Academic radiology May 2, 2025
RATIONALE AND OBJECTIVES: To explore the feasibility of deep learning (DL)-enhanced, fully automated bone mineral density (BMD) measurement using the ultralow-voltage 80 kV chest CT scans performed for lung cancer screening.
Radiographic Image Interpretation, Computer-Assisted Feasibility Studies Radiography, Thoracic Aged Aged, 80 and over Male Middle Aged Deep Learning Lung Neoplasms Osteoporosis Tomography, X-Ray Computed Female Humans Bone Density Retrospective Studies
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Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm.

Journal of thoracic imaging May 1, 2025
PURPOSE: To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR).
Radiation Dosage Radiography, Thoracic Multiple Pulmonary Nodules Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Aged, 80 and over Adult Sensitivity and Specificity Tomography, X-Ray Computed Cross-Sectional Studies Lung Female Male Humans Deep Learning Aged Middle Aged Algorithms Lung Neoplasms Prospective Studies
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Deep learning based dual stage model for accurate nasogastric tube positioning in chest radiographs.

Scientific reports Apr 25, 2025
Accurate placement of nasogastric tubes (NGTs) is crucial for ensuring patient safety and effective treatment. Traditional methods relying on manual inspection are susceptible to human error, highlighting the need for innovative solutions. This study...
Intubation, Gastrointestinal Humans Deep Learning Radiography, Thoracic
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Reduction of radiation exposure in chest radiography using deep learning-based noise reduction processing: A phantom and retrospective clinical study.

Radiography (London, England : 1995) Apr 24, 2025
INTRODUCTION: Intelligent noise reduction (INR), a deep learning-based noise reduction developed by Canon, is used in planar radiography to improve image quality and reduce patient exposure dose. This study aimed to evaluate the reduction of patient ...
Retrospective Studies Female Aged Adult Male Middle Aged Humans Radiography, Thoracic Phantoms, Imaging Radiation Exposure Deep Learning Radiographic Image Interpretation, Computer-Assisted Radiation Dosage
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Reconstruction-based approach for chest X-ray image segmentation and enhanced multi-label chest disease classification.

Artificial intelligence in medicine Apr 23, 2025
U-Net is a commonly used model for medical image segmentation. However, when applied to chest X-ray images that show pathologies, it often fails to include these critical pathological areas in the generated masks. To address this limitation, in our s...
Humans Algorithms Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Thoracic Diseases
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Tuberculosis detection using few shot learning.

Scientific reports Apr 16, 2025
Tuberculosis (TB), a contagious disease, significantly affects lungs functioning. Amongst multiple detection methodologies, Chest X-ray analysis is considered the most effective methodology. Traditional Deep Learning methodologies have shown good res...
Tuberculosis Humans Tuberculosis, Pulmonary Deep Learning Radiography, Thoracic
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