AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Applications and potential of machine, learning augmented chest X-ray interpretation in cardiology.

Minerva cardiology and angiology Nov 13, 2024
The chest X-ray (CXR) has a wide range of clinical indications in the field of cardiology, from the assessment of acute pathology to disease surveillance and screening. Despite many technological advancements, CXR interpretation error rates have rema...
Humans Machine Learning Clinical Decision-Making Deep Learning Radiographic Image Interpretation, Computer-Assisted Heart Diseases Radiography, Thoracic Cardiology
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Diagnostic performance of an artificial intelligence model for the detection of pneumothorax at chest X-ray.

Clinical imaging Nov 12, 2024
PURPOSE: Pneumothorax (PTX) is a common clinical urgency, its diagnosis is usually performed on chest radiography (CXR), and it presents a setting where artificial intelligence (AI) methods could find terrain in aiding radiologists in facing increasi...
Humans Artificial Intelligence Retrospective Studies Tomography, X-Ray Computed Female Reproducibility of Results Aged Aged, 80 and over Middle Aged Young Adult Radiographic Image Interpretation, Computer-Assisted Adult Radiography, Thoracic Sensitivity and Specificity Pneumothorax Male
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Deep learning segmentation-based bone removal from computed tomography of the brain improves subdural hematoma detection.

Journal of neuroradiology = Journal de neuroradiologie Nov 8, 2024
PURPOSE: Timely identification of intracranial blood products is clinically impactful, however the detection of subdural hematoma (SDH) on non-contrast CT scans of the head (NCCTH) is challenging given interference from the adjacent calvarium. This w...
Hematoma, Subdural Male Deep Learning Radiographic Image Interpretation, Computer-Assisted Skull Humans Algorithms Tomography, X-Ray Computed Female
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Assessing the need for coronary angiography in high-risk non-ST-elevation acute coronary syndrome patients using artificial intelligence and computed tomography.

The international journal of cardiovascular imaging Nov 8, 2024
PURPOSE: This study aimed to evaluate the efficacy of the Chat Generative Pre-trained Transformer (ChatGPT) in guiding the need for invasive coronary angiography (ICA) in high-risk non-ST-elevation (NSTE) acute coronary syndrome (ACS) patients based ...
Risk Assessment Non-ST Elevated Myocardial Infarction Predictive Value of Tests Double-Blind Method Risk Factors Aged Male Middle Aged Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Acute Coronary Syndrome Computed Tomography Angiography Humans Artificial Intelligence Decision Support Techniques Prospective Studies Clinical Decision-Making Female Reproducibility of Results
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Evaluating the Efficacy of Deep Learning Reconstruction in Reducing Radiation Dose for Computer-Aided Volumetry for Liver Tumor: A Phantom Study.

Journal of computer assisted tomography Nov 5, 2024
OBJECTIVE: The purpose of this study was to compare radiation dose reduction capability for accurate liver tumor measurements of a computer-aided volumetry (CAD v ) software for filtered back projection (FBP), hybrid-type iterative reconstruction (IR...
Humans Tomography, X-Ray Computed Deep Learning Liver Neoplasms Phantoms, Imaging Liver Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Signal-To-Noise Ratio
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Combination of Deep Learning Grad-CAM and Radiomics for Automatic Localization and Diagnosis of Architectural Distortion on DBT.

Academic radiology Nov 3, 2024
RATIONALE AND OBJECTIVES: Detection and diagnosis of architectural distortion (AD) on digital breast tomosynthesis (DBT) is challenging. This study applied artificial intelligence (AI) using deep learning (DL) algorithms to detect AD, followed by rad...
Mammography Radiographic Image Interpretation, Computer-Assisted Female Aged Adult Sensitivity and Specificity Humans Breast Neoplasms Algorithms Radiomics Retrospective Studies Middle Aged Deep Learning
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Applying Deep-Learning Algorithm Interpreting Kidney, Ureter, and Bladder (KUB) X-Rays to Detect Colon Cancer.

Journal of imaging informatics in medicine Oct 31, 2024
Early screening is crucial in reducing the mortality of colorectal cancer (CRC). Current screening methods, including fecal occult blood tests (FOBT) and colonoscopy, are primarily limited by low patient compliance and the invasive nature of the proc...
Retrospective Studies Female Aged Sensitivity and Specificity Humans Algorithms Early Detection of Cancer Radiographic Image Interpretation, Computer-Assisted Colonic Neoplasms Urinary Bladder Ureter Male Middle Aged Deep Learning Kidney
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Beyond strong labels: Weakly-supervised learning based on Gaussian pseudo labels for the segmentation of ellipse-like vascular structures in non-contrast CTs.

Medical image analysis Oct 30, 2024
Deep learning-based automated segmentation of vascular structures in preoperative CT angiography (CTA) images contributes to computer-assisted diagnosis and interventions. While CTA is the common standard, non-contrast CT imaging has the advantage of...
Supervised Machine Learning Aorta, Abdominal Computed Tomography Angiography Normal Distribution Humans Algorithms Deep Learning Radiographic Image Interpretation, Computer-Assisted
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Impact of deep Learning-enhanced contrast on diagnostic accuracy in stroke CT angiography.

European journal of radiology Oct 28, 2024
PURPOSE: To examine the impact of deep learning-augmented contrast enhancement on image quality and diagnostic accuracy of poorly contrasted CT angiography in patients with suspected stroke.
Aged Sensitivity and Specificity Male Middle Aged Deep Learning Humans Contrast Media Retrospective Studies Radiographic Image Interpretation, Computer-Assisted Female Stroke Reproducibility of Results Signal-To-Noise Ratio Computed Tomography Angiography Cerebral Angiography Radiographic Image Enhancement
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Evaluation of a deep learning-based software to automatically detect and quantify breast arterial calcifications on digital mammogram.

Diagnostic and interventional imaging Oct 26, 2024
PURPOSE: The purpose of this study was to evaluate an artificial intelligence (AI) software that automatically detects and quantifies breast arterial calcifications (BAC).
Radiographic Image Interpretation, Computer-Assisted Breast Vascular Calcification Breast Diseases Sensitivity and Specificity Middle Aged Deep Learning Mammography Female Aged Aged, 80 and over Adult Humans Software Retrospective Studies Tomography, X-Ray Computed
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