AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Development and validation of computer-aided detection for colorectal neoplasms using deep learning incorporated with computed tomography colonography.

BMC gastroenterology Mar 8, 2025
OBJECTIVES: Computed tomography (CT) colonography is increasingly recognized as a valuable modality for diagnosing colorectal lesions, however, the interpretation workload remains challenging for physicians. Deep learning-based artificial intelligenc...
Diagnosis, Computer-Assisted Deep Learning Early Detection of Cancer Radiographic Image Interpretation, Computer-Assisted Colorectal Neoplasms Humans Male Neural Networks, Computer Middle Aged Female Aged Sensitivity and Specificity Colonography, Computed Tomographic
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CZT-based photon-counting-detector CT with deep-learning reconstruction: image quality and diagnostic confidence for lung tumor assessment.

Japanese journal of radiology Mar 7, 2025
PURPOSE: This is a preliminary analysis of one of the secondary endpoints in the prospective study cohort. The aim of this study is to assess the image quality and diagnostic confidence for lung cancer of CT images generated by using cadmium-zinc-tel...
Tomography, X-Ray Computed Tellurium Female Radiographic Image Interpretation, Computer-Assisted Aged Cadmium Male Humans Lung Photons Prospective Studies Deep Learning Zinc Lung Neoplasms
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A Preoperative CT-based Multiparameter Deep Learning and Radiomic Model with Extracellular Volume Parameter Images Can Predict the Tumor Budding Grade in Rectal Cancer Patients.

Academic radiology Mar 6, 2025
RATIONALE AND OBJECTIVES: To investigate a computed tomography (CT)-based multiparameter deep learning-radiomic model (DLRM) for predicting the preoperative tumor budding (TB) grade in patients with rectal cancer.
Radiomics Male Middle Aged Preoperative Care Deep Learning Tomography, X-Ray Computed Humans Neoplasm Grading Female Aged Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Adult Rectal Neoplasms
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Learning Consistent Semantic Representation for Chest X-ray via Anatomical Localization in Self-Supervised Pre-Training.

IEEE journal of biomedical and health informatics Mar 6, 2025
Despite the similar global structures in Chest X-ray (CXR) images, the same anatomy exhibits varying appearances across images, including differences in local textures, shapes, colors, etc. Learning consistent representations for anatomical semantics...
Thorax Humans Radiographic Image Interpretation, Computer-Assisted Algorithms Supervised Machine Learning Semantics Radiography, Thoracic
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OMS-CNN: Optimized Multi-Scale CNN for Lung Nodule Detection Based on Faster R-CNN.

IEEE journal of biomedical and health informatics Mar 6, 2025
The global increase in lung cancer cases, often marked by pulmonary nodules, underscores the critical importance of timely detection to mitigate cancer progression and reduce morbidity and mortality. The Faster R-CNN approach is a two-stage, high-pre...
Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Algorithms Tomography, X-Ray Computed Lung Humans Lung Neoplasms Neural Networks, Computer
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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...
Humans COVID-19 SARS-CoV-2 Lung Deep Learning Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Pneumonia, Viral
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Deep Learning-Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time.

AJNR. American journal of neuroradiology Mar 4, 2025
BACKGROUND AND PURPOSE: ASPECTS is a long-standing and well-documented selection criterion for acute ischemic stroke treatment; however, the interpretation of ASPECTS is a challenging and time-consuming task for physicians with notable interobserver ...
Radiographic Image Interpretation, Computer-Assisted Observer Variation Computed Tomography Angiography Retrospective Studies Reproducibility of Results Deep Learning Ischemic Stroke Humans Algorithms
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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 ...
Radiography, Thoracic Male Middle Aged Lung Deep Learning Humans Lung Neoplasms Tomography, X-Ray Computed Follow-Up Studies Female Radiographic Image Interpretation, Computer-Assisted Aged
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Artificial intelligence quantification and experienced reader computed tomography analysis for differentiating normal from minimally and mildly diseased coronary arteries: an early real-world compatibility study.

The international journal of cardiovascular imaging Feb 28, 2025
Differentiating normal from minimally and mildly diseased coronary arteries on coronary computed tomographic angiography (CCTA) is crucial, impacting treatment decisions due to the extremely low coronary artery event risk associated with the former. ...
Middle Aged Severity of Illness Index Predictive Value of Tests Coronary Angiography Reproducibility of Results Aged Male Coronary Vessels Plaque, Atherosclerotic Vascular Calcification Asymptomatic Diseases Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Observer Variation Humans Artificial Intelligence Retrospective Studies Female Computed Tomography Angiography
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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 Diseases Radiography, Thoracic Lung Humans Deep Learning Neural Networks, Computer Image Processing, Computer-Assisted Algorithms Radiographic Image Interpretation, Computer-Assisted
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