AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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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...
Radiography, Thoracic Thoracic Diseases Humans Algorithms Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted
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Deep learning-based post hoc denoising for 3D volume-rendered cardiac CT in mitral valve prolapse.

The international journal of cardiovascular imaging Apr 23, 2025
We hypothesized that deep learning-based post hoc denoising could improve the quality of cardiac CT for the 3D volume-rendered (VR) imaging of mitral valve (MV) prolapse. We aimed to evaluate the quality of denoised 3D VR images for visualizing MV pr...
Radiographic Image Interpretation, Computer-Assisted Mitral Valve Mitral Valve Prolapse Middle Aged Deep Learning Imaging, Three-Dimensional Predictive Value of Tests Female Humans Reproducibility of Results Retrospective Studies Aged Tomography, X-Ray Computed Male
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Torg-Pavlov ratio qualification to diagnose developmental cervical spinal stenosis based on HRViT neural network.

BMC musculoskeletal disorders Apr 23, 2025
BACKGROUND: Developing computer-assisted methods to measure the Torg-Pavlov ratio (TPR), defined as the ratio of the sagittal diameter of the cervical spinal canal to the sagittal diameter of the corresponding vertebral body on lateral radiographs, c...
Radiographic Image Interpretation, Computer-Assisted Cervical Vertebrae Spinal Stenosis Spinal Canal Adult Humans Neural Networks, Computer Male Female Middle Aged Young Adult Deep Learning
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Plaque burden improves the detection of ischemic CAD over stenosis from coronary computed tomography angiography.

The international journal of cardiovascular imaging Apr 22, 2025
In symptomatic patients undergoing coronary CTA for suspected coronary artery disease (CAD), we assessed if quantification of plaque burden, in addition to luminal narrowing and clinical risk factors, offers incremental value for the identification o...
Artificial Intelligence Humans Retrospective Studies Vascular Calcification Female Computed Tomography Angiography Coronary Artery Disease Risk Factors Reproducibility of Results Myocardial Perfusion Imaging Coronary Vessels Aged Multidetector Computed Tomography Coronary Stenosis Severity of Illness Index Male Plaque, Atherosclerotic Predictive Value of Tests Coronary Angiography Middle Aged Radiographic Image Interpretation, Computer-Assisted
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Using deep learning for estimation of time-since-injury in pediatric accidental fractures.

Pediatric radiology Apr 22, 2025
BACKGROUND: Estimating time-since-injury of healing fractures is imprecise, encompassing excessively wide timeframes. Most injured children are evaluated at non-children's hospitals, yet pediatric radiologists can disagree with up to one in six skele...
Child, Preschool Child Fractures, Bone Radiographic Image Interpretation, Computer-Assisted Sensitivity and Specificity Male Infant Humans Deep Learning Accidental Injuries Retrospective Studies Female Infant, Newborn
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CAD-Unet: A capsule network-enhanced Unet architecture for accurate segmentation of COVID-19 lung infections from CT images.

Medical image analysis Apr 19, 2025
Since the outbreak of the COVID-19 pandemic in 2019, medical imaging has emerged as a primary modality for diagnosing COVID-19 pneumonia. In clinical settings, the segmentation of lung infections from computed tomography images enables rapid and accu...
SARS-CoV-2 Lung Deep Learning Radiographic Image Interpretation, Computer-Assisted Humans Neural Networks, Computer COVID-19 Tomography, X-Ray Computed
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World of Forms: Deformable geometric templates for one-shot surface meshing in coronary CT angiography.

Medical image analysis Apr 18, 2025
Deep learning-based medical image segmentation and surface mesh generation typically involve a sequential pipeline from image to segmentation to meshes, often requiring large training datasets while making limited use of prior geometric knowledge. Th...
Imaging, Three-Dimensional Coronary Angiography Humans Radiographic Image Interpretation, Computer-Assisted Neural Networks, Computer Computed Tomography Angiography Algorithms Deep Learning
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Deep learning-based prediction of enhanced CT scans for lymph node metastasis in esophageal squamous cell carcinoma.

Japanese journal of radiology Apr 11, 2025
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) poses a significant global health challenge with a particularly grim prognosis. Accurate prediction of lymph node metastasis (LNM) in ESCC is crucial for optimizing treatment strategies and improv...
Aged, 80 and over Adult Sensitivity and Specificity Male Retrospective Studies Tomography, X-Ray Computed Female Aged Humans Contrast Media Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Middle Aged Esophageal Neoplasms Esophageal Squamous Cell Carcinoma Deep Learning Lymph Nodes Lymphatic Metastasis
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A comparison of an integrated and image-only deep learning model for predicting the disappearance of indeterminate pulmonary nodules.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Apr 11, 2025
BACKGROUND: Indeterminate pulmonary nodules (IPNs) require follow-up CT to assess potential growth; however, benign nodules may disappear. Accurately predicting whether IPNs will resolve is a challenge for radiologists. Therefore, we aim to utilize d...
Tomography, X-Ray Computed Female Aged Sensitivity and Specificity Humans Retrospective Studies Multiple Pulmonary Nodules Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Male Middle Aged Deep Learning Lung Neoplasms
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Machine-Learning-Based Computed Tomography Radiomics Regression Model for Predicting Pulmonary Function.

Academic radiology Apr 10, 2025
RATIONALE AND OBJECTIVES: Chest computed tomography (CT) radiomics can be utilized for categorical predictions; however, models predicting pulmonary function indices directly are lacking. This study aimed to develop machine-learning-based regression ...
Humans Middle Aged Lung Regression Analysis Forced Expiratory Volume Aged Radiographic Image Interpretation, Computer-Assisted Adult Respiratory Function Tests Radiomics Vital Capacity Male Machine Learning Retrospective Studies Tomography, X-Ray Computed Female
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