Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence

Discordance Rate and Risk Factor of Other Diagnostic Modalities for Small Bowel Tumors Detected by Device-Assisted Enteroscopy: A Korean Association for the Study of Intestinal Disease (KASID) Multicenter Study

Background/Aims

Despite advances in imaging and endoscopic technology, diagnostic modalities for small bowel tumors are simultaneously performed. We investigated the discrepancy rate between each modality and predictive factors of discrepancy in patients with definite small bowel tumors.

Methods

Data of patients with definite small bowel tumors who underwent both device-assisted enteroscopy (DAE) and computed tomography (CT) were retrieved from web-based enteroscopy registry database in Korea. Predictive risk factors associated with discrepancy were analyzed using logistic regression analysis.

Results

Among 998 patients, 210 (21.0%) were diagnosed with small bowel tumor using DAE, in 193 patients with definite small bowel tumor, DAE and CT were performed. Of these patients, 12 (6.2%) showed discrepancy between examinations. Among 49 patients who underwent DAE and video capsule endoscopy (VCE) examination, 13 (26.5%) showed discrepancy between examinations. No significant independent risk factors were associated with concordance between DAE and CT in multivariate logistic regression analysis among the patients. In a multivariate logistic regression analysis, red blood cell transfusion was negatively associated with concordance between DAE and VCE in patients with small bowel tumor (odds ratio, 0.163; 95% confidence interval, 0.026 to 1.004; p=0.050).

Conclusions

For small bowel tumors, the discrepancy rate between DAE and CT was 6.2%, and 26.5% between DAE and VCE. Despite developments in cross-sectional imaging (VCE and DAE modalities), discrepancies still exist. For small bowel bleeding that require significant transfusion while showing insignificant VCE findings, DAE should be considered as the next diagnostic approach, considering the possibility of missed small bowel tumor.

Current Approaches for Monitoring of Patients with Inflammatory Bowel Diseases: A Narrative Review

BACKGROUND

Patients with inflammatory bowel diseases (IBD) require proactive monitoring both during the active phase to evaluate therapeutic response and during the remission phase to evaluate relapse or colorectal cancer surveillance. However, monitoring may vary between patients with ulcerative colitis (UC) and Crohn's disease (CD), with distinct tools and intervals.

METHODS

This narrative review aims to focus on modern approaches to IBD monitoring, considering international guidelines and expert consensus.

RESULTS

The most recent European diagnostic guidelines advocate a combination of clinical, laboratory, endoscopic, and radiological parameters to evaluate the disease course of patients with IBD. Unfortunately, the conventional symptom-based therapeutic approach does not improve long-term outcomes and there is no single ideal biomarker available. Endoscopy plays a key role in evaluating response to therapy as well as monitoring disease activity. Recently, bedside intestinal ultrasound (IUS) has gained increasing interest and diffusion as it appears to offer several advantages including the monitoring of therapeutic response.

CONCLUSION

In light of growing clinical advances, we present a schematic evidence-based monitoring algorithm that can be easily applied in clinical practice which combines all major monitoring modalities, including noninvasive tools such as IUS and video-capsule endoscopy.

Artificial Intelligence-assisted Analysis of Pan-enteric Capsule Endoscopy in Patients with Suspected Crohn's Disease: A Study on Diagnostic Performance

BACKGROUND AND AIM

Pan-enteric capsule endoscopy [PCE] is a highly sensitive but time-consuming tool for detecting pathology. Artificial intelligence [AI] algorithms might offer a possibility to assist in the review and reduce the analysis time of PCE. This study examines the agreement between PCE assessments aided by AI technology and standard evaluations, in patients suspected of Crohn's disease [CD].

METHOD

PCEs from a prospective, blinded, multicentre study, including patients suspected of CD, were processed by the deep learning solution AXARO® [Augmented Endoscopy, Paris, France]. Based on the image output, two observers classified the patient's PCE as normal or suggestive of CD, ulcerative colitis, or cancer. The primary outcome was per-patient sensitivities and specificities for detecting CD and inflammatory bowel disease [IBD]. Complete reading of PCE served as the reference standard.

RESULTS

A total of 131 patients' PCEs were analysed, with a median recording time of 303 min. The AXARO® framework reduced output to a median of 470 images [2.1%] per patient, and the pooled median review time was 3.2 min per patient. For detecting CD, the observers had a sensitivity of 96% and 92% and a specificity of 93% and 90%, respectively. For the detection of IBD, both observers had a sensitivity of 97% and had a specificity of 91% and 90%, respectively. The negative predictive value was 95% for CD and 97% for IBD.

CONCLUSIONS

Using the AXARO® framework reduced the initial review time substantially while maintaining high diagnostic accuracy-suggesting its use as a rapid tool to rule out IBD in PCEs of patients suspected of Crohn's disease.