Artificial intelligence in capsule endoscopy (ArtIC)
Research type
Research Study
Full title
Role of the artificial intelligence in capsule endoscopy (ArtIC) for the identification of small bowel lesions in patients with small intestinal bleeding
IRAS ID
297101
Contact name
Mark McAlindon
Contact email
Sponsor organisation
Sheffield Teaching Hospitals NHS FT
Clinicaltrials.gov Identifier
Duration of Study in the UK
0 years, 11 months, 30 days
Research summary
Research Summary
Capsule endoscopy (CE) is a swallowable pill camera, safe, patient friendly and performed for the evaluation of the small bowel which is difficult to reach using conventional endoscopy. The most common indication for CE is suspected small bowel bleeding. The capsule acquires images of the gastrointestinal tract during transit and transmits them to a data recorder which the patient wears on a belt.
This raw data is converted to a video format on a computer which the clinician can view. Meta-analysis shows that CE has a diagnostic yield of about 60% for this indication, and angiodysplasias are the most relevant findings, accounting for 50%. It is now an accepted first line small bowel examination in suspected bleeding if conventional upper and lower endoscopy do not reveal the cause. The main drawback of CE is the time and concentration needed for the clinician to view the video and produce the report, a process which may take 30-60 minutes, per case. In order to reduce reading time, several softwares have been developed which recognise and remove sequentially identical (or near identical) images. These have been shown to significantly reduce reading time without impacting the miss rate in diffuse mucosal disease (such as Crohn’s disease). This may not be the same with isolated lesions since several studies report an unacceptable miss rate.
New advances such as artificial intelligence are being investigated. Deep convolutional neural networks (CNNs) are algorithms developed by the computer analysis of thousands of pathological images to establish an extensive and complex range of recognition features used to identify images containing potential abnormalities in clinical practice.
The aim of the study is to determine the role of AI-assisted CE reading in clinical practice.
Summary of Results
Artificial intelligence (AI) software was compared to expert human readers of small bowel capsule endoscopy videos. AI-assisted reading proved superior to human reading, identifying more lesions causing anaemia, and with a 9-fold quicker reading time.
REC name
London - Chelsea Research Ethics Committee
REC reference
21/PR/0711
Date of REC Opinion
8 Jun 2021
REC opinion
Further Information Favourable Opinion