NCT04821349

Role of AI in CE for the Identification of SB Lesions in Patients With Small Intestinal Bleeding.

Role of the Artificial Intelligence in Capsule Endoscopy for the Identification of Small Bowel Lesions in Patients With Small Intestinal Bleeding ArtIC Study: Artificial Intelligence Capsule Endoscopy Study

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 137 (actual)
• Sponsor: Fondazione Poliambulanza Istituto Ospedaliero · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

Capsule Endoscopy (CE) is a safe, patient friendly and easy procedure performed for the evaluation of gastrointestinal tract unable to be explored via conventional endoscopy. The most common indication to perform SBCE is represented by Suspected Small Bowel Bleeding (SSBB). According to the widest meta-analysis available in literature, SBCE shows a diagnostic yield in SSBB of about 60%, and angiodysplasias are the most relevant findings, accounting for 50% of patients undergoing SBCE for SSBB. Accordingly, it represents the first line examination in SSBB investigation for determining the source of bleeding, if primary endoscopy results negative. Despite its high clinical feasibility, the evaluation of CE-video-captures is one of the main drawbacks since it is time consuming and requests the reader to concentrate to not miss any lesion. In order to reduce reading time, several software have been developed with the aim to cut similar images and select relevant images. For example, automated fast reading software have demonstrated to significantly reduce reading time without impacting the miss rate in pathological conditions affecting diffusely the mucosa (as IBD lesions do). Not the same assumption can be taken for isolated lesions since several studies reported an unacceptable miss rate for such a detection modality. New advancements such as artificial intelligence made their appearance in recent years. Deep convolutional neural networks (CNNs) have demonstrated to recognize specific images among a large variety up to exceed human performance in visual tasks. A Deep Learning model has been recently validated in the field of Small Bowel CE by Ding et al. According to their data collected on 5000 patients, the CNN-based auxiliary model identify abnormalities with 99.88% sensitivity in the per patient analysis and 99.90% sensitivity in the per-lesion analysis. With this perspective, it is believable that AI applied to SBCE can significantly shorten the reading time and support physicians to detect available lesions without losing significant lesions, further improving the diagnostic yield of the procedure.

Detailed description

This is a multicenter, multinational, blinded prospective trial, involving a consecutive series of patients recruited by 12 European centers based on the indication of OGIB: * after negative upper and lower endoscopy * France: after negative pregnancy test * Hb cut-off male: \<13, female: \<12 Capsule endoscopy will be performed in each site according to local rules and requirements, and the study protocol will concern only the post-procedure analysis on reading modalities for each patient, as specified below.1. Regimen of preparation AI depends on the possibility of the software to "see" images. An inadequate cleansing level precludes a proper visualization and the impact of technology. In order to have homogenous results, a standard regimen of preparation is advisable. The regimen includes a split dose of PEG-based solution (PlenVu, Moviprep) as recommended by ESGE guideline - technical report. Dose 1 will be administered at 7 pm of the day before and dose 2 in the morning of the procedure in order to be completed at least 1 hour before capsule ingestion. On the day before patients can have breakfast and a light meal. After lunch they should be fasting. Two and 4 hours after capsule ingestion patient are allowed to drink clear liquids and a light meal, respectively. 2\. Standard reading Each center will review the images collected according to the "normal reading" as recommended by ESGE guidelines. To compare reading time correctly, readers must read the video without including annotation of images. All lesions should be considered independently of their relevance. Findings are not marked on every image if they appear repeatedly on every consecutive image, but if they appear repeatedly with normal images in between. Annotations should be done after the reading, each lesion shall be labelled if it belongs to P1/2 category according to the Saurin Classification. Definition of P1, P2: red spots on the intestinal mucosa or small or isolated erosions or angioectasia, ulcers, tumors or varices or any other bleeding abnormality. 3\. AI-assisted reading Each center will receive from another center anonymized patient videos that have been uploaded on an encrypted USB key. Indeed, expert gastroenterologists from each center will proceed with AI-assisted video reading without knowing which center the video comes from nor the results of the normal reading. Reading conditions are the same as for standard reading. 4\. Consensus reading Results from both normal and AI-assisted reading will be compared. In the case of significant disagreement between the results from conventional capsule endoscopy reading and AI-assisted reading (e.g. missed lesions), a consensus review for each center will be done. Any lesion shall be considered.

Conditions

• Iron Deficiency Anemia
• Obscure Gastrointestinal Bleeding

Interventions

Type	Name	Description
DEVICE	Capsule endoscopy	A consecutive series of patients recruited by 12 European centers based on the indication of OGIB will undergo capsule endoscopy examination. Capsule endoscopy will be performed in each site according to local rules and requirements, and the study protocol will concern only the post-procedure analysis on reading modalities for each patient.

Timeline

• Start date: 2021-02-16
• Primary completion: 2022-05-05
• Completion: 2022-10-01
• First posted: 2021-03-29
• Last updated: 2024-02-21

Locations

1 site across 1 country: Italy