Nasim Parsa

Barrett’s esophagus is the only known precursor for esophageal adenocarcinoma. The stepwise progression of nondysplastic Barrett’s to low-grade dysplasia, high-grade dysplasia, intramucosal carcinoma, and invasive cancer provides an opportunity for endoscopic surveillance, with the goal to detect and effectively treat early neoplastic changes endoscopically, in order to avoid surgery and chemoradiation. Multiple limitations in the current endoscopic surveillance practice have led to missed early neoplasia and therefore reduced the effectiveness of the current surveillance practice. Artificial intelligence (AI)-based clinical decision support systems have been developed to provide additional assistance to physicians performing diagnostic and therapeutic gastrointestinal endoscopy. In this article, we review the current endoscopic surveillance practice for Barrett’s esophagus, its limitations, the potential role of AI to improve these limitations, and our suggested framework to integrate AI into Barrett’s clinical practice.

Background Cricopharyngeal bars (CPBs) are a unique etiology of oropharyngeal dysphagia. Symptomatic patients are managed with endoscopic dilation or surgical myotomy. Cricopharyngeal peroral endoscopic myotomy (CP-POEM) is an emerging technique for the management of dysphagia due to CPBs. This study evaluated technical success, clinical success, adverse events, and long-term recurrence following CP-POEM. Methods Consecutive patients who underwent POEM for management of CPBs between May 2015 and December 2020 at four tertiary care centers were included. Primary outcome was clinical success (defined as improvement of dysphagia score to ≤ 1). Secondary outcomes were technical success, rate and severity of adverse events, procedure duration, and symptom recurrence. Results 27 patients (mean age 69 years; 10 female) underwent CP-POEM during the study period. The most common presenting symptoms at the time of index procedure were dysphagia (26; 96.3 %) and regurgitation (20; 74.1 %). Clinical and technical success were achieved in all patients. Mild/moderate adverse events occurred in two patients (7.4 %). CP-POEM significantly reduced the median dysphagia score. Conclusions CP-POEM was a safe and effective treatment for symptomatic CPBs. Although symptom recurrence was low, long-term outcome data are needed. CP-POEM should be considered as a management option for symptomatic CPBs at centers with POEM expertise. ^* Co-first authors. Received: 19 March 2021 Accepted after revision: 23 July 2021 Publication Date: 28 October 2021 (online) © 2021. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany

Colonoscopy remains the gold standard exam for colorectal cancer screening due to its ability to detect and resect pre-cancerous lesions in the colon. However, its performance is greatly operator dependent. Studies have shown that up to one-quarter of colorectal polyps can be missed on a single colonoscopy, leading to high rates of interval colorectal cancer. In addition, the American Society for Gastrointestinal Endoscopy has proposed the “resect-and-discard” and “diagnose-and-leave” strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the performance of optical biopsy has been suboptimal in community practice. With recent improvements in machine-learning techniques, artificial intelligence–assisted computer-aided detection and diagnosis have been increasingly utilized by endoscopists. The application of computer-aided design on real-time colonoscopy has been shown to increase the adenoma detection rate while decreasing the withdrawal time and improve endoscopists’ optical biopsy accuracy, while reducing the time to make the diagnosis. These are promising steps toward standardization and improvement of colonoscopy quality, and implementation of “resect-and-discard” and “diagnose-and-leave” strategies. Yet, issues such as real-world applications and regulatory approval need to be addressed before artificial intelligence models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of artificial intelligence for detection and characterization of colorectal polyps and review the limitation of existing artificial intelligence technologies and future directions for this field.