Virtual Histology in Everyday Gastrointestinal Endoscopy

Confocal Laser Endomicroscopy in the Diagnosis of Biliary and Pancreatic Disorders: A Systematic Analysis

BACKGROUND/AIMS

Endoscopic visualization of the microscopic anatomy can facilitate the real-time diagnosis of pancreatobiliary disorders and provide guidance for treatment. This study aimed to review the technique, image classification, and diagnostic performance of confocal laser endomicroscopy (CLE).

METHODS

We conducted a systematic review of CLE in pancreatic and biliary ducts of humans, and have provided a narrative of the technique, image classification, diagnostic performance, ongoing research, and limitations.

RESULTS

Probe-based CLE differentiates malignant from benign biliary strictures (sensitivity, ≥89%; specificity, ≥61%). Needlebased CLE differentiates mucinous from non-mucinous pancreatic cysts (sensitivity, 59%; specificity, ≥94%) and identifies dysplasia. Pancreatitis may develop in 2-7% of pancreatic cyst cases. Needle-based CLE has potential applications in adenocarcinoma, neuroendocrine tumors, and pancreatitis (chronic or autoimmune). Costs, catheter lifespan, endoscopist training, and interobserver variability are challenges for routine utilization.

CONCLUSION

CLE reveals microscopic pancreatobiliary system anatomy with adequate specificity and sensitivity. Reducing costs and simplifying image interpretation will promote utilization by advanced endoscopists.

Physician sentiment toward artificial intelligence (AI) in colonoscopic practice: a survey of US gastroenterologists

Background and study aims  Early studies have shown that artificial intelligence (AI) has the potential to augment the performance of gastroenterologists during endoscopy. Our aim was to determine how gastroenterologists view the potential role of AI in gastrointestinal endoscopy. Methods  In this cross-sectional study, an online survey was sent to US gastroenterologists. The survey included questions about physician level of training, experience, and practice characteristics and physician perception of AI. Descriptive statistics were used to summarize sentiment about AI. Univariate and multivariate analyses were used to assess whether background information about physicians correlated to their sentiment. Results  Surveys were emailed to 330 gastroenterologists nationwide. Between December 2018 and January 2019, 124 physicians (38 %) completed the survey. Eighty-six percent of physicians reported interest in AI-assisted colonoscopy; 84.7 % agreed that computer-assisted polyp detection (CADe) would improve their endoscopic performance. Of the respondents, 57.2 % felt comfortable using computer-aided diagnosis (CADx) to support a "diagnose and leave" strategy for hyperplastic polyps. Multivariate analysis showed that post-fellowship experience of fewer than 15 years was the most important factor in determining whether physicians were likely to believe that CADe would lead to more removed polyps (odds ratio = 5.09; P  = .01). The most common concerns about implementation of AI were cost (75.2 %), operator dependence (62.8 %), and increased procedural time (60.3 %). Conclusions  Gastroenterologists have strong interest in the application of AI to colonoscopy, particularly with regard to CADe for polyp detection. The primary concerns were its cost, potential to increase procedural time, and potential to develop operator dependence. Future developments in AI should prioritize mitigation of these concerns.