ABSTRACT
BACKGROUND AND AIMS: Small bowel (SB) capsule endoscopy (CE) is a first line procedure for exploring the SB. Endoscopic GastroIntestinal PlacemenT (EGIPT) of SB CE is sometimes necessary. While the experience of EGIPT is large in pediatric populations, we aimed to describe the safety, efficacy and outcomes of EGIPT of SB CE in adult patients. METHODS: The international CApsule endoscopy REsearch (iCARE) group set up a retrospective multicenter study. Patients over 18 year-old who underwent EGIPT of SB CE before May 2022 were included. Data were collected from medical records and capsule recordings. The primary endpoint was the technical success rate of the EGIPT procedures. RESULTS: 630 patients were included (mean age 62.5 years old, 55.9% female) from 39,565 patients (1.6%) issued from 29 centers. EGIPT technical success was achieved in 610 procedures (96.8%). Anesthesia (moderate/deep sedation or general anesthesia) and centers with intermediate or high procedure loads were independent factors of technical success. Severe adverse events occurred in three (0.5%) patients. When technically successful, EGIPT was associated with a high SB CE completion rate (84.4%) and with a substantial diagnostic yield (61.1%). Completion rate was significantly higher when the capsule was delivered in the SB compared to when delivered in the stomach. CONCLUSION: EGIPT of SB CE is highly feasible, safe and comes with high completion rate and diagnostic yield. When indicated, it should rather be performed under anesthesia and the capsule should be delivered in the duodenum rather than in the stomach, for better SB examination outcomes.
ABSTRACT
Background and Study Aims: Deep learning (DL) for video capsule endoscopy (VCE) is an emerging research field. It has shown high accuracy for the detection of Crohn's disease (CD) ulcers. Non-steroidal anti-inflammatory drugs (NSAIDS) are commonly used medications. In the small bowel, NSAIDs may cause a variety of gastrointestinal adverse events including NSAID-induced ulcers. These ulcers are the most important differential diagnosis for small bowel ulcers in patients evaluated for suspected CD. We evaluated a DL network that was trained using CD VCE ulcer images and evaluated its performance for NSAID ulcers. Patients and Methods: The network was trained using CD ulcers and normal mucosa from a large image bank created from VCE of diagnosed CD patients. NSAIDs-induced enteropathy images were extracted from the prospective Bifidobacterium breve (BIf95) trial dataset. All images were acquired from studies performed using PillCam SBIII. The area under the receiver operating curve (AUC) was used as a metric. We compared the network's AUC for detecting NSAID ulcers to that of detecting CD ulcers. Results: Overall, the CD training dataset included 17,640 CE images. The NSAIDs testing dataset included 1,605 CE images. The DL network exhibited an AUC of 0.97 (95% CI 0.97-0.98) for identifying images with NSAID mucosal ulcers. The diagnostic accuracy was similar to that obtained for CD related ulcers (AUC 0.94-0.99). Conclusions: A network trained on VCE CD ulcers similarly identified NSAID findings. As deep learning is transforming gastrointestinal endoscopy, this result should be taken into consideration in the future design and analysis of VCE deep learning applications.