AIMC Topic: Colonography, Computed Tomographic

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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...
Humans Neural Networks, Computer Deep Learning Colorectal Neoplasms Early Detection of Cancer Female Male Radiographic Image Interpretation, Computer-Assisted Aged Middle Aged Colonography, Computed Tomographic Sensitivity and Specificity Diagnosis, Computer-Assisted
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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...
Adult Sensitivity and Specificity Colorectal Neoplasms Male Female Humans Aged Artificial Intelligence Aged, 80 and over Middle Aged Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Diagnosis, Differential Colonic Polyps Radiologists Adenomatous Polyps Colonography, Computed Tomographic
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Synthesized colonoscopy dataset from high-fidelity virtual colon with abnormal simulation.

Computers in biology and medicine Jan 17, 2025
With the advent of the deep learning-based colonoscopy system, the need for a vast amount of high-quality colonoscopy image datasets for training is crucial. However, the generalization ability of deep learning models is challenged by the limited ava...
Imaging, Three-Dimensional Colonic Polyps Colonography, Computed Tomographic Humans Databases, Factual Colon Colonoscopy Deep Learning
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G-SET-DCL: a guided sequential episodic training with dual contrastive learning approach for colon segmentation.

International journal of computer assisted radiology and surgery Jan 9, 2025
PURPOSE: This article introduces a novel deep learning approach to substantially improve the accuracy of colon segmentation even with limited data annotation, which enhances the overall effectiveness of the CT colonography pipeline in clinical settin...
Humans Algorithms Reproducibility of Results Colon Colonic Polyps Colonography, Computed Tomographic Deep Learning Imaging, Three-Dimensional Radiographic Image Interpretation, Computer-Assisted
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Depth estimation from monocular endoscopy using simulation and image transfer approach.

Computers in biology and medicine Aug 22, 2024
Obtaining accurate distance or depth information in endoscopy is crucial for the effective utilization of navigation systems. However, due to space constraints, incorporating depth cameras into endoscopic systems is often impractical. Our goal is to ...
Image Processing, Computer-Assisted Colonography, Computed Tomographic Humans Computer Simulation Colon Deep Learning
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Morphometric analysis and tortuosity typing of the large intestine segments on computed tomography colonography with artificial intelligence.

Colombia medica (Cali, Colombia) Jun 30, 2024
BACKGROUND: Morphological properties such as length and tortuosity of the large intestine segments play important roles, especially in interventional procedures like colonoscopy.
Imaging, Three-Dimensional Colon, Transverse Colonography, Computed Tomographic Female Aged Adult Humans Aged, 80 and over Male Artificial Intelligence Middle Aged Algorithms Colon Retrospective Studies
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Deep Learning-Based Reconstruction Improves the Image Quality of Low-Dose CT Colonography.

Academic radiology Jan 29, 2024
RATIONALE AND OBJECTIVES: To evaluate the image quality of low-dose CT colonography (CTC) using deep learning-based reconstruction (DLR) compared to iterative reconstruction (IR).
Female Humans Radiation Dosage Middle Aged Adult Signal-To-Noise Ratio Deep Learning Male Colonography, Computed Tomographic Body Mass Index Radiographic Image Interpretation, Computer-Assisted
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Usefulness of the artificial intelligence-mediated virtual chromoendoscopy in peroral cholangioscopy.

Endoscopy Aug 21, 2023
Endoscopy, Gastrointestinal Colonography, Computed Tomographic Indigo Carmine Humans Artificial Intelligence Laparoscopy
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Deep learning in CT colonography: differentiating premalignant from benign colorectal polyps.

European radiology Jan 26, 2022
OBJECTIVES: To investigate the differentiation of premalignant from benign colorectal polyps detected by CT colonography using deep learning.
Retrospective Studies Aged Sensitivity and Specificity Deep Learning Colorectal Neoplasms Colonic Polyps Humans Precancerous Conditions Male Colonography, Computed Tomographic Middle Aged Colonoscopy
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Machine Learning-based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-of-Concept Study.

Radiology Feb 23, 2021
Background CT colonography does not enable definite differentiation between benign and premalignant colorectal polyps. Purpose To perform machine learning-based differentiation of benign and premalignant colorectal polyps detected with CT colonograph...
Proof of Concept Study Colonography, Computed Tomographic Colonic Neoplasms Female Colonic Polyps Humans Mass Screening Aged Image Interpretation, Computer-Assisted Male Machine Learning Precancerous Conditions Contrast Media Middle Aged Prospective Studies Diagnosis, Differential
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