AIMC Topic: Colonic Polyps
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A clinical pilot trial of an artificial intelligence-driven smart phone application of bowel preparation for colonoscopy: a randomized clinical trial.
Scandinavian journal of gastroenterology
Dec 22, 2024
BACKGROUND: High-quality bowel preparation is paramount for a successful colonoscopy. This study aimed to explore the effect of artificial intelligence-driven smartphone software on the quality of bowel preparation.
CE-Net: Cascade attention and context-aware cross-level fusion network via edge learning guidance for polyp segmentation.
Computers in biology and medicine
Dec 8, 2024
Colorectal polyps are one of the most direct causes of colorectal cancer. Polypectomy can effectively block the process of colorectal cancer, but accurate polyp segmentation methods are required as an auxiliary means. However, there are several chall...
Identification of biomarkers for the diagnosis in colorectal polyps and metabolic dysfunction-associated steatohepatitis (MASH) by bioinformatics analysis and machine learning.
Scientific reports
Nov 27, 2024
Colorectal polyps are precursors of colorectal cancer. Metabolic dysfunction associated steatohepatitis (MASH) is one of metabolic dysfunction associated fatty liver disease (MAFLD) phenotypic manifestations. Much evidence has suggested an associatio...
Creating a standardized tool for the evaluation and comparison of artificial intelligence-based computer-aided detection programs in colonoscopy: a modified Delphi approach.
Gastrointestinal endoscopy
Nov 26, 2024
BACKGROUND AND AIMS: Multiple computer-aided detection (CADe) software programs have now achieved regulatory approval in the United States, Europe, and Asia and are being used in routine clinical practice to support colorectal cancer screening. There...
Role of Artificial Intelligence for Colon Polyp Detection and Diagnosis and Colon Cancer.
Gastrointestinal endoscopy clinics of North America
Nov 26, 2024
The broad use of artificial intelligence (AI) and its various applications have already shown significant impact in medicine and in everyday life. In gastroenterology, the most studied AI tools at present are computer-aided detection (CADe) and compu...
MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with Application in Colonic Polyp Image Segmentation.
Sensors (Basel, Switzerland)
Nov 23, 2024
Accurate polyp image segmentation is of great significance, because it can help in the detection of polyps. Convolutional neural network (CNN) is a common automatic segmentation method, but its main disadvantage is the long training time. Transformer...
In-context learning enables multimodal large language models to classify cancer pathology images.
Nature communications
Nov 21, 2024
Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this process is computationally and technically demanding. In language processin...
In vivo evaluation of complex polyps with endoscopic optical coherence tomography and deep learning during routine colonoscopy: a feasibility study.
Scientific reports
Nov 14, 2024
Standard-of-care (SoC) imaging for assessing colorectal polyps during colonoscopy, based on white-light colonoscopy (WLC) and narrow-band imaging (NBI), does not have sufficient accuracy to assess the invasion depth of complex polyps non-invasively d...
The use of artificial intelligence in colonoscopic evaluations.
Current opinion in gastroenterology
Oct 25, 2024
PURPOSE OF REVIEW: This review aims to highlight the transformative impact of artificial intelligence in the field of gastrointestinal endoscopy, particularly in the detection and characterization of colorectal polyps.
Colonoscopy polyp classification via enhanced scattering wavelet Convolutional Neural Network.
PloS one
Oct 11, 2024
Among the most common cancers, colorectal cancer (CRC) has a high death rate. The best way to screen for colorectal cancer (CRC) is with a colonoscopy, which has been shown to lower the risk of the disease. As a result, Computer-aided polyp classific...
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