AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
Clear Filters Showing 91 to 100 of 1260 articles
Effect of artificial intelligence-aided differentiation of adenomatous and non-adenomatous colorectal polyps at CT colonography on radiologists' therapy management.
European radiology
Jan 25, 2025
OBJECTIVES: Adenomatous colorectal polyps require endoscopic resection, as opposed to non-adenomatous hyperplastic colorectal polyps. This study aims to evaluate the effect of artificial intelligence (AI)-assisted differentiation of adenomatous and n...
Middle Aged
Diagnosis, Differential
Radiologists
Radiographic Image Interpretation, Computer-Assisted
Sensitivity and Specificity
Colorectal Neoplasms
Male
Colonic Polyps
Adenomatous Polyps
Colonography, Computed Tomographic
Humans
Artificial Intelligence
Retrospective Studies
Aged, 80 and over
Adult
Female
Aged
A deep learning pipeline for systematic and accurate vertebral fracture reporting in computed tomography.
Clinical radiology
Jan 25, 2025
AIM: Spine fractures are a frequent and relevant diagnosis, but systematic documentation is time-consuming and sometimes overlooked. A deep learning pipeline for opportunistic fracture detection in computed tomography (CT) spine images of varying fie...
AI-assisted radiologists vs. standard double reading for rib fracture detection on CT images: A real-world clinical study.
PloS one
Jan 24, 2025
To evaluate the diagnostic accuracy of artificial intelligence (AI) assisted radiologists and standard double-reading in real-world clinical settings for rib fractures (RFs) detection on CT images. This study included 243 consecutive chest trauma pat...
Interpretable COVID-19 chest X-ray detection based on handcrafted feature analysis and sequential neural network.
Computers in biology and medicine
Jan 22, 2025
Deep learning methods have significantly improved medical image analysis, particularly in detecting COVID-19 chest X-rays. Nonetheless, these methodologies frequently inhibit some drawbacks, such as limited interpretability, extensive computational r...
Utilizing deep learning for automatic segmentation of the cochleae in temporal bone computed tomography.
Acta radiologica (Stockholm, Sweden : 1987)
Jan 22, 2025
BackgroundSegmentation of the cochlea in temporal bone computed tomography (CT) is the basis for image-guided otologic surgery. Manual segmentation is time-consuming and laborious.PurposeTo assess the utility of deep learning analysis in automatic se...
Robot-Assisted CT-Guided Biopsy with an Artificial Intelligence-Based Needle-Path Generator: An Experimental Evaluation Using a Phantom Model.
Journal of vascular and interventional radiology : JVIR
Jan 21, 2025
PURPOSE: To investigate the feasibility of a robotic system with artificial intelligence-based lesion detection and path planning for computed tomography (CT)-guided biopsy compared with the conventional freehand technique.
CXR-LLaVA: a multimodal large language model for interpreting chest X-ray images.
European radiology
Jan 15, 2025
OBJECTIVE: This study aimed to develop an open-source multimodal large language model (CXR-LLaVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation...
Evaluation of a Deep Learning Denoising Algorithm for Dose Reduction in Whole-Body Photon-Counting CT Imaging: A Cadaveric Study.
Academic radiology
Jan 15, 2025
RATIONALE AND OBJECTIVES: Photon Counting CT (PCCT) offers advanced imaging capabilities with potential for substantial radiation dose reduction; however, achieving this without compromising image quality remains a challenge due to increased noise at...
Evaluating a clinically available artificial intelligence model for intracranial aneurysm detection: a multi-reader study and algorithmic audit.
Neuroradiology
Jan 15, 2025
PURPOSE: We aimed to validate a clinically available artificial intelligence (AI) model to assist general radiologists in the detection of intracranial aneurysm (IA) in a multi-reader multi-case (MRMC) study, and to explore its performance in routine...
Attention-guided erasing for enhanced transfer learning in breast abnormality classification.
International journal of computer assisted radiology and surgery
Jan 15, 2025
PURPOSE: Breast cancer remains one of the most prevalent cancers globally, necessitating effective early screening and diagnosis. This study investigates the effectiveness and generalizability of our recently proposed data augmentation technique, att...
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