Impact of second-generation transoral incisionless fundoplication on atypical GERD symptoms: a systematic review and meta-analysis.

BACKGROUND AND AIMS: Transoral incisionless fundoplication (TIF) using the EsophyX device (EndoGastric Solutions, Inc, Redmond, Wash, USA) is a minimally invasive endoscopic fundoplication technique. Our study aimed to assess the efficacy of TIF for atypical GERD symptoms in patients with chronic or refractory GERD. METHODS: A systematic search of 4 major databases was performed. All original studies assessing atypical GERD using a validated symptom questionnaire (the reflux symptom index [RSI]) were included. The RSI score was assessed before and after TIF at a 6- and 12-month follow-up. Data on technical success rate, adverse events, proton pump inhibitor (PPI) use, and patient satisfaction were also collected. Only TIF procedures currently in practice using the EsophyX device (ie, TIF 2.0) and TIF with concomitant hiatal hernia repair were included in the review. RESULTS: Ten studies (564 patients) were included. At the 6- and 12- month follow-up, there was a mean reduction of 15.72 (95% confidence interval, 12.15-19.29) and 14.73 (95% confidence interval, 11.74-17.72) points, respectively, in the RSI score post-TIF, with a technical success rate of 99.5% and a pooled adverse event rate of 1%. At both time intervals, more than two-thirds of the patients were satisfied with their health condition and roughly three-fourths of the patients were off daily PPIs. CONCLUSIONS: Our study shows that TIF using the EsophyX device is safe and effective in reducing atypical GERD symptoms at 6 and 12 months of follow-up. It improves patient-centered outcomes and can be a minimally invasive therapeutic option for patients suffering from atypical GERD symptoms on chronic medical therapy.

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.

Artificial intelligence in gastrointestinal endoscopy: The future is almost here.

Artificial intelligence (AI) enables machines to provide unparalleled value in a myriad of industries and applications. In recent years, researchers have harnessed artificial intelligence to analyze large-volume, unstructured medical data and perform clinical tasks, such as the identification of diabetic retinopathy or the diagnosis of cutaneous malignancies. Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning, are beginning to emerge in gastrointestinal endoscopy. The most promising of these efforts have been in computer-aided detection and computer-aided diagnosis of colorectal polyps, with recent systems demonstrating high sensitivity and accuracy even when compared to expert human endoscopists. AI has also been utilized to identify gastrointestinal bleeding, to detect areas of inflammation, and even to diagnose certain gastrointestinal infections. Future work in the field should concentrate on creating seamless integration of AI systems with current endoscopy platforms and electronic medical records, developing training modules to teach clinicians how to use AI tools, and determining the best means for regulation and approval of new AI technology.