Evaluation by a Machine Learning System of Two Preparations for Small Bowel Capsule Endoscopy: The BUBS (Burst Unpleasant Bubbles with Simethicone) Study.

Background: Bubbles often mask the mucosa during capsule endoscopy (CE). Clinical scores assessing the cleanliness and the amount of bubbles in the small bowel (SB) are poorly reproducible unlike machine learning (ML) solutions. We aimed to measure the amount of bubbles with ML algorithms in SB CE recordings, and compare two polyethylene glycol (PEG)-based preparations, with and without simethicone, in patients with obscure gastro-intestinal bleeding (OGIB). Patients & Methods: All consecutive outpatients with OGIB from a tertiary care center received a PEG-based preparation, without or with simethicone, in two different periods. The primary outcome was a difference in the proportions (%) of frames with abundant bubbles (>10%) along the full-length video sequences between the two periods. SB CE recordings were analyzed by a validated computed algorithm based on a grey-level of co-occurrence matrix (GLCM), to assess the abundance of bubbles in each frame. Results: In total, 105 third generation SB CE recordings were analyzed (48 without simethicone and 57 with simethicone-added preparations). A significant association was shown between the use of a simethicone-added preparation and a lower abundance of bubbles along the SB (p = 0.04). A significantly lower proportion of "abundant in bubbles" frames was observed in the fourth quartile (30.5% vs. 20.6%, p = 0.02). There was no significant impact of the use of simethicone in terms of diagnostic yield, SB transit time and completion rate. Conclusion: An accurate and reproducible computed algorithm demonstrated significant decrease in the abundance of bubbles along SB CE recordings, with a marked effect in the last quartile, in patients for whom simethicone had been added in PEG-based preparations, compared to those without simethicone.

A multisystem-compatible deep learning-based algorithm for detection and characterization of angiectasias in small-bowel capsule endoscopy. A proof-of-concept study.

BACKGROUND AND AIMS: Current artificial intelligence (AI)-based solutions for capsule endoscopy (CE) interpretation are proprietary. We aimed to evaluate an AI solution trained on a specific CE system (Pillcam®, Medtronic) for the detection of angiectasias on images captured by a different proprietary system (MiroCam®, Intromedic). MATERIAL AND METHODS: An advanced AI solution (Axaro®, Augmented Endoscopy), previously trained on Pillcam® small bowell images, was evaluated on independent datasets with more than 1200 Pillcam® and MiroCam® still frames (equally distributed, with or without angiectasias). Images were reviewed by experts before and after AI interpretation. RESULTS: Sensitivity for the diagnosis of angiectasia was 97.4% with Pillcam® images and 96.1% with Mirocam® images, with specificity of 98.8% and 97.8%, respectively. Performances regarding the delineation of regions of interest and the characterization of angiectasias were similar in both groups (all above 95%). Processing time was significantly shorter with Mirocam® (20.7 ms) than with Pillcam® images (24.6 ms, p<0.0001), possibly related to technical differences between systems. CONCLUSION: This proof-of-concept study on still images paves the way for the development of resource-sparing, "universal" CE databases and AI solutions for CE interpretation.

Clinical feasibility of panintestinal (or panenteric) capsule endoscopy: a systematic review.

In recent years, panintestinal capsule endoscopy (PCE) with double-headed capsules has been used to perform complete, single-sitting exploration of both small bowel and colon in different clinical conditions. Double-headed capsules for colonic examination (CCE) have been exploited first in this setting, followed by newer generations of capsules (i.e. PillCam Crohn, PCC) specifically engineered for this purpose. The aim of this study was to evaluate the feasibility of PCE in the form of a systematic review. We performed a comprehensive literature search to identify papers in which CE was specifically used for a PCE of the gastrointestinal tract. Data on CE, bowel preparation regimen, rate of cleanliness and completeness, and data on transit times were analyzed. The primary outcome was to assess the feasibility of a whole-gut exploration with CE. Sixteen (n = 16) studies including 915 CE procedures with CCE1 (n = 134), CCE2 (n = 357) and PCC (n = 424) were included. 13/16 studies were performed in the setting of Crohn's disease. Cleanliness and completeness rates were acceptable in all studies, ranging from 63.9% and 68.6% to 100%, respectively. In conclusion, PCE is a feasible technique, although further structured studies are needed to explore its full potential.

Artificial intelligence in small bowel capsule endoscopy - current status, challenges and future promise.

Neural network-based solutions are under development to alleviate physicians from the tedious task of small-bowel capsule endoscopy reviewing. Computer-assisted detection is a critical step, aiming to reduce reading times while maintaining accuracy. Weakly supervised solutions have shown promising results; however, video-level evaluations are scarce, and no prospective studies have been conducted yet. Automated characterization (in terms of diagnosis and pertinence) by supervised machine learning solutions is the next step. It relies on large, thoroughly labeled databases, for which preliminary "ground truth" definitions by experts are of tremendous importance. Other developments are under ways, to assist physicians in localizing anatomical landmarks and findings in the small bowel, in measuring lesions, and in rating bowel cleanliness. It is still questioned whether artificial intelligence will enter the market with proprietary, built-in or plug-in software, or with a universal cloud-based service, and how it will be accepted by physicians and patients.

Experience with the use of a hemostatic powder in 152 patients undergoing urgent endoscopy for gastrointestinal bleeding.

BACKGROUND AND STUDY AIMS: In the recent years, topical hemostatic powders have been used for the management of upper gastrointestinal bleeding. The aim of this study was to report on the use of an hemostatic powder (Hemospray®), outside regular hours, by on-call endoscopists during urgent endoscopic procedures. MATERIAL AND METHODS: In this retrospective multicenter cohort study, consecutive patients having undergone an urgent endoscopy with the use of Hemospray® from November 2015 to December 2018 in the Paris and suburbs area were included. We collected clinical, biological and endoscopic variables. The outcomes such as the recurrence, repeat endoscopy and hemostatic treatment need, complications and survival were also collected. RESULTS: A total of 152 patients (mean 65 years old, 70.4% male) were included. Amongst the 31 endoscopists, 11 were "more experienced", and performed 48% of the endoscopies. The most common causes of bleeding were peptic ulcer (47.7%), malignancy (22.2%) and esophagitis (12.4%). Most bleedings originated from the upper GI tract (95.0%). Hemospray® was used as a salvage therapy in 60.8% of cases. Other hemostatic techniques were used in 52.9% of cases. Immediate bleeding cessation was noted in 79.0% of cases, recurrence in 39.9% of cases, and 26.4% of patients benefited from a repeat endoscopic hemostasis. 34 (23.0%) patients required a non-endoscopic treatment. At day 30, the survival rate was 71.6%. One complication was reported (perforation). CONCLUSIONS: Hemostatic powder application by on-call endoscopists outside regular hours is technically feasible, but comes with a high risk of rebleeding in severely ill patients.