AIMC Topic: Colonoscopy

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Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial).

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
BACKGROUND & AIMS: Artificial intelligence-based computer-aided polyp detection (CADe) systems are intended to address the issue of missed polyps during colonoscopy. The effect of CADe during screening and surveillance colonoscopy has not previously ...

Colorectal Polyp Image Detection and Classification through Grayscale Images and Deep Learning.

Sensors (Basel, Switzerland)
Colonoscopy screening and colonoscopic polypectomy can decrease the incidence and mortality rate of colorectal cancer (CRC). The adenoma detection rate and accuracy of diagnosis of colorectal polyp which vary in different experienced endoscopists hav...

Evaluation of novel LCI CAD EYE system for real time detection of colon polyps.

PloS one
BACKGROUND: Linked color imaging (LCI) has been shown to be effective in multiple randomized controlled trials for enhanced colorectal polyp detection. Recently, artificial intelligence (AI) with deep learning through convolutional neural networks ha...

Automatic Polyp Segmentation in Colonoscopy Images Using a Modified Deep Convolutional Encoder-Decoder Architecture.

Sensors (Basel, Switzerland)
Colorectal cancer has become the third most commonly diagnosed form of cancer, and has the second highest fatality rate of cancers worldwide. Currently, optical colonoscopy is the preferred tool of choice for the diagnosis of polyps and to avert colo...

Artificial intelligence and polyp detection in colonoscopy: Use of a single neural network to achieve rapid polyp localization for clinical use.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Artificial intelligence has been extensively studied to assist clinicians in polyp detection, but such systems usually require expansive processing power, making them prohibitively expensive and hindering wide adaption. The curren...

Computer-Aided Colon Polyp Detection on High Resolution Colonoscopy Using Transfer Learning Techniques.

Sensors (Basel, Switzerland)
Colonoscopies reduce the incidence of colorectal cancer through early recognition and resecting of the colon polyps. However, the colon polyp miss detection rate is as high as 26% in conventional colonoscopy. The search for methods to decrease the po...

Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial.

Journal of gastroenterology
BACKGROUND: We have developed the computer-aided detection (CADe) system using an original deep learning algorithm based on a convolutional neural network for assisting endoscopists in detecting colorectal lesions during colonoscopy. The aim of this ...