AIMC Topic: Colonoscopy

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Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge.

Scientific reports
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-de...

A Framework for the Evaluation of Human Machine Interfaces of Robot-Assisted Colonoscopy.

IEEE transactions on bio-medical engineering
UNLABELLED: The Human Machine Interface (HMI) of intraluminal robots has a crucial impact on the clinician's performance. It increases or decreases the difficulty of the tasks, and is connected to the users' physical and mental stress.

Use of a Novel Artificial Intelligence System Leads to the Detection of Significantly Higher Number of Adenomas During Screening and Surveillance Colonoscopy: Results From a Large, Prospective, US Multicenter, Randomized Clinical Trial.

The American journal of gastroenterology
INTRODUCTION: Adenoma per colonoscopy (APC) has recently been proposed as a quality measure for colonoscopy. We evaluated the impact of a novel artificial intelligence (AI) system, compared with standard high-definition colonoscopy, for APC measureme...

Two-stage deep-learning-based colonoscopy polyp detection incorporating fisheye and reflection correction.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Colonoscopy is a useful method for the diagnosis and management of colorectal diseases. Many computer-aided systems have been developed to assist clinicians in detecting colorectal lesions by analyzing colonoscopy images. However,...

A novel artificial intelligence-assisted "vascular healing" diagnosis for prediction of future clinical relapse in patients with ulcerative colitis: a prospective cohort study (with video).

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Image-enhanced endoscopy has attracted attention as a method for detecting inflammation and predicting outcomes in patients with ulcerative colitis (UC); however, the procedure requires specialist endoscopists. Artificial intelli...

Density clustering-based automatic anatomical section recognition in colonoscopy video using deep learning.

Scientific reports
Recognizing anatomical sections during colonoscopy is crucial for diagnosing colonic diseases and generating accurate reports. While recent studies have endeavored to identify anatomical regions of the colon using deep learning, the deformable anatom...

Linked-color imaging with or without artificial intelligence for adenoma detection: a randomized trial.

Endoscopy
BACKGROUND: Adenoma detection rate (ADR) is an important indicator of colonoscopy quality and colorectal cancer incidence. Both linked-color imaging (LCI) with artificial intelligence (LCA) and LCI alone increase adenoma detection during colonoscopy,...

Deep learning system for true- and pseudo-invasion in colorectal polyps.

Scientific reports
Over 15 million colonoscopies were performed yearly in North America, during which biopsies were taken for pathological examination to identify abnormalities. Distinguishing between true- and pseudo-invasion in colon polyps is critical in treatment p...

Towards Semi-Autonomous Colon Screening Using an Electromagnetically Actuated Soft-Tethered Colonoscope Based on Visual Servo Control.

IEEE transactions on bio-medical engineering
OBJECTIVE: Conventional colonoscopy using a flexible colonoscope remains two major limitations, including patient discomfort and difficult manipulations for surgeons. Robotic colonoscopes have been developed to conduct colonoscopy in a patient-friend...

Enhancing artificial intelligence-doctor collaboration for computer-aided diagnosis in colonoscopy through improved digital literacy.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
Establishing appropriate trust and maintaining a balanced reliance on digital resources are vital for accurate optical diagnoses and effective integration of computer-aided diagnosis (CADx) in colonoscopy. Active learning using diverse polyp image da...