AIMC Topic: Colonoscopy

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Artificial Intelligence System to Determine Risk of T1 Colorectal Cancer Metastasis to Lymph Node.

Gastroenterology
BACKGROUND & AIMS: In accordance with guidelines, most patients with T1 colorectal cancers (CRC) undergo surgical resection with lymph node dissection, despite the low incidence (∼10%) of metastasis to lymph nodes. To reduce unnecessary surgical rese...

Application of optical character recognition with natural language processing for large-scale quality metric data extraction in colonoscopy reports.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Colonoscopy is commonly performed for colorectal cancer screening in the United States. Reports are often generated in a non-standardized format and are not always integrated into electronic health records. Thus, this information...

Application of Deep Learning for Early Screening of Colorectal Precancerous Lesions under White Light Endoscopy.

Computational and mathematical methods in medicine
METHODS: We collected and sorted out the white light endoscopic images of some patients undergoing colonoscopy. The convolutional neural network model is used to detect whether the image contains lesions: CRC, colorectal adenoma (CRA), and colorectal...

Deep learning to find colorectal polyps in colonoscopy: A systematic literature review.

Artificial intelligence in medicine
Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and removal of colorectal lesions with potential to evolve into cancer...

A Transparent and Adaptable Method to Extract Colonoscopy and Pathology Data Using Natural Language Processing.

Journal of medical systems
Key variables recorded as text in colonoscopy and pathology reports have been extracted using natural language processing (NLP) tools that were not easily adaptable to new settings. We aimed to develop a reliable NLP tool with broad adaptability. Dur...

A Stacked Generalization U-shape network based on zoom strategy and its application in biomedical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The deep neural network model can learn complex non-linear relationships in the data and has superior flexibility and adaptability. A downside of this flexibility is that they are sensitive to initial conditions, both in ter...

An automated detection system for colonoscopy images using a dual encoder-decoder model.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Conventional computer-aided detection systems (CADs) for colonoscopic images utilize shape, texture, or temporal information to detect polyps, so they have limited sensitivity and specificity. This study proposes a method to extract possible polyp fe...

Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study.

Gastroenterology
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...

Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer.

Nature communications
Colonoscopy is commonly used to screen for colorectal cancer (CRC). We develop a deep learning model called CRCNet for optical diagnosis of CRC by training on 464,105 images from 12,179 patients and test its performance on 2263 patients from three in...

Regulatory considerations for artificial intelligence technologies in GI endoscopy.

Gastrointestinal endoscopy
Artificial intelligence (AI) technologies in clinical medicine have become the subject of intensive investigative efforts and popular attention. In domains ranging from pathology to radiology, AI has demonstrated the potential to improve clinical per...