AIMC Topic: Colonoscopy

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Novel deep learning-based computer-aided diagnosis system for predicting inflammatory activity in ulcerative colitis.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Endoscopy is increasingly performed for evaluating patients with ulcerative colitis (UC). However, its diagnostic accuracy is largely affected by the subjectivity of endoscopists' experience and scoring methods, and scoring of se...

Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation.

Journal of gastroenterology
BACKGROUND: Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the ...

Artificial Intelligence for Colonoscopy: Past, Present, and Future.

IEEE journal of biomedical and health informatics
During the past decades, many automated image analysis methods have been developed for colonoscopy. Real-time implementation of the most promising methods during colonoscopy has been tested in clinical trials, including several recent multi-center st...

Automated histological classification for digital pathology images of colonoscopy specimen via deep learning.

Scientific reports
Colonoscopy is an effective tool to detect colorectal lesions and needs the support of pathological diagnosis. This study aimed to develop and validate deep learning models that automatically classify digital pathology images of colon lesions obtaine...

Validation of a natural language processing algorithm to identify adenomas and measure adenoma detection rates across a health system: a population-level study.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Measuring adenoma detection rates (ADRs) at the population level is challenging because pathology reports are often reported in an unstructured format; further, there is significant variation in reporting methods across instituti...

Frame-by-Frame Analysis of a Commercially Available Artificial Intelligence Polyp Detection System in Full-Length Colonoscopies.

Digestion
INTRODUCTION: Computer-aided detection (CADe) helps increase colonoscopic polyp detection. However, little is known about other performance metrics like the number and duration of false-positive (FP) activations or how stable the detection of a polyp...

Warning from artificial intelligence against inaccurate polyp size estimation.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society

PRAPNet: A Parallel Residual Atrous Pyramid Network for Polyp Segmentation.

Sensors (Basel, Switzerland)
In a colonoscopy, accurate computer-aided polyp detection and segmentation can help endoscopists to remove abnormal tissue. This reduces the chance of polyps developing into cancer, which is of great importance. In this paper, we propose a neural net...

Development and evaluation of a deep learning model to improve the usability of polyp detection systems during interventions.

United European gastroenterology journal
BACKGROUND: The efficiency of artificial intelligence as computer-aided detection (CADe) systems for colorectal polyps has been demonstrated in several randomized trials. However, CADe systems generate many distracting detections, especially during i...