AIMC Topic: Adenomatous Polyps

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A novel approach to overcome black box of AI for optical diagnosis in colonoscopy.

Scientific reports Jul 1, 2025
Accurate real-time optical diagnosis that distinguishes neoplastic from non-neoplastic colorectal lesions during colonoscopy can lower the costs of pathological assessments, prevent unnecessary polypectomies, and help avoid adverse events. Using a mu...
Humans Artificial Intelligence Female Deep Learning Colonic Polyps Adenomatous Polyps Narrow Band Imaging Sensitivity and Specificity Colorectal Neoplasms Male Colonoscopy
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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...
Colonography, Computed Tomographic Radiologists Radiographic Image Interpretation, Computer-Assisted Colonic Polyps Adenomatous Polyps Aged Aged, 80 and over Colorectal Neoplasms Adult Humans Male Artificial Intelligence Middle Aged Sensitivity and Specificity Retrospective Studies Diagnosis, Differential Female
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A Boundary-Enhanced Decouple Fusion Segmentation Network for Diagnosis of Adenomatous Polyps.

Journal of imaging informatics in medicine Jul 22, 2024
Adenomatous polyps, a common premalignant lesion, are often classified into villous adenoma (VA) and tubular adenoma (TA). VA has a higher risk of malignancy, whereas TA typically grows slowly and has a lower likelihood of cancerous transformation. A...
Humans Colonoscopy Deep Learning Image Interpretation, Computer-Assisted Adenomatous Polyps
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The value of CT radiomics combined with deep transfer learning in predicting the nature of gallbladder polypoid lesions.

Acta radiologica (Stockholm, Sweden : 1987) Apr 16, 2024
BACKGROUND: Computed tomography (CT) radiomics combined with deep transfer learning was used to identify cholesterol and adenomatous gallbladder polyps that have not been well evaluated before surgery.
Middle Aged Deep Learning Contrast Media Predictive Value of Tests Adult Radiomics Male Adenomatous Polyps Gallbladder Cholesterol Gallbladder Diseases Gallbladder Neoplasms Polyps Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Female Aged
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Artificial intelligence-based endoscopic diagnosis of colorectal polyps using residual networks.

PloS one Jun 22, 2021
Convolutional neural networks (CNNs) are widely used for artificial intelligence (AI)-based image classification. Residual network (ResNet) is a new technology that facilitates the accuracy of image classification by CNN-based AI. In this study, we d...
Colonic Polyps Adenomatous Polyps Artificial Intelligence Sensitivity and Specificity Predictive Value of Tests Endoscopy, Gastrointestinal Humans Neural Networks, Computer
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Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study.

Gastroenterology Jun 17, 2020
BACKGROUND AND AIMS: Up to 30% of adenomas might be missed during screening colonoscopy-these could be polyps that appear on-screen but are not recognized by endoscopists or polyps that are in locations that do not appear on the screen at all. Comput...
Missed Diagnosis Humans Prospective Studies Female Deep Learning China Adolescent Predictive Value of Tests Male Colonic Neoplasms Middle Aged Reproducibility of Results Colonic Polyps Colonoscopy Aged Image Interpretation, Computer-Assisted Diagnosis, Computer-Assisted Young Adult Adenomatous Polyps Adult
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Polyp detection algorithm can detect small polyps: Ex vivo reading test compared with endoscopists.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society May 28, 2020
BACKGROUND AND STUDY AIMS: Small polyps are occasionally missed during colonoscopy. This study was conducted to validate the diagnostic performance of a polyp-detection algorithm to alert endoscopists to unrecognized lesions.
Algorithms Colonoscopy Deep Learning Colonic Polyps Adenomatous Polyps Humans
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Improved Accuracy in Optical Diagnosis of Colorectal Polyps Using Convolutional Neural Networks with Visual Explanations.

Gastroenterology Feb 29, 2020
BACKGROUND & AIMS: Narrow-band imaging (NBI) can be used to determine whether colorectal polyps are adenomatous or hyperplastic. We investigated whether an artificial intelligence (AI) system can increase the accuracy of characterizations of polyps b...
Seoul Adenomatous Polyps Narrow Band Imaging Observer Variation Hyperplasia Humans Visual Perception Reproducibility of Results Colorectal Neoplasms Predictive Value of Tests Colonoscopy Workflow Diagnosis, Computer-Assisted Colonic Polyps Deep Learning Image Interpretation, Computer-Assisted Clinical Competence
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An overview of deep learning algorithms and water exchange in colonoscopy in improving adenoma detection.

Expert review of gastroenterology & hepatology Nov 30, 2019
: Among the Gastrointestinal (GI) Endoscopy Editorial Board top 10 topics in advances in endoscopy in 2018, water exchange colonoscopy and artificial intelligence were both considered important advances. Artificial intelligence holds the potential to...
Adenomatous Polyps Humans Reproducibility of Results Colonic Neoplasms Colonic Polyps Colonoscopy Diagnosis, Computer-Assisted Image Interpretation, Computer-Assisted Deep Learning Water Predictive Value of Tests
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Challenges Facing the Detection of Colonic Polyps: What Can Deep Learning Do?

Medicina (Kaunas, Lithuania) Aug 12, 2019
Colorectal cancer (CRC) is one of the most common causes of cancer mortality in the world. The incidence is related to increases with age and western dietary habits. Early detection through screening by colonoscopy has been proven to effectively redu...
Adenomatous Polyps Humans Colonoscopy Deep Learning Neural Networks, Computer Early Detection of Cancer Colorectal Neoplasms Adenoma
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