Endoscopic prediction of submucosal invasion in Barrett's cancer with the use of artificial intelligence: a pilot study.

BACKGROUND: The accurate differentiation between T1a and T1b Barrett's-related cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an artificial intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett's cancer on white-light images. METHODS: Endoscopic images from three tertiary care centers in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) were evaluated using the AI system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett's cancer. RESULTS: The sensitivity, specificity, F1 score, and accuracy of the AI system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.74, and 0.71, respectively. There was no statistically significant difference between the performance of the AI system and that of experts, who showed sensitivity, specificity, F1, and accuracy of 0.63, 0.78, 0.67, and 0.70, respectively. CONCLUSION: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett's cancer. AI scored equally to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and real-life settings. Nevertheless, the correct prediction of submucosal invasion in Barrett's cancer remains challenging for both experts and AI.

Endoscopic Treatment of Zenker's Diverticulum: Comparable Treatment Outcomes in Treatment-Naïve and Pretreated Patients.

BACKGROUND AND AIMS: Flexible endoscopic treatment plays an important role in the treatment of Zenker's diverticulum (ZD). This study analyzes long-term symptom control and the rate of adverse events in treatment-naïve patients and patients with recurrence, using the stag beetle knife junior (sb knife jr). METHODS: From August 2013 to May 2019, 100 patients with symptomatic ZD were treated with flexible endoscopy using the sb knife jr. Before treatment, as well as 1 and 6 months afterwards, symptoms were obtained by a nine-point questionnaire, with symptoms weighted from 0 to 4. RESULTS: Overall, 126 interventions were performed. The median follow-up period was 41 months (range 7-74). For the three most frequent symptoms, regurgitation, dysphagia, and dry cough, a significant reduction of the mean score could be achieved, from 2.85/3.45/2.85 before the initial treatment to 0.56/1.09/0.98 6 months later. 17 patients were retreated because of recurrence. Out of these, 12 patients underwent a 2nd, 4 patients a 3rd, and 1 patient a 4th session, respectively. The mean dysphagia score for successfully treated patients could be reduced from initially 2.34 to 0.49/0.33/0.67 after the 1st/2nd/3rd session, the frequency of dysphagia from 3.45 to 0.92/1.00/1.33, and the score for regurgitations from 2.85 to 0.35/1.00/0.67. In first-line treatment, as well as in retreatment, no severe adverse event occurred. CONCLUSION: Patients with ZD can be treated safely and effectively with the sb knife jr. Retreatment leads to equal symptom relief as compared to a successful first-line treatment and is not associated with a higher rate of adverse events.

Impact of the COVID-19 outbreak on endoscopy training in a tertiary care centre in Germany.

OBJECTIVE: The COVID-19 crisis has impacted on all aspects of health care including medical education and training. We describe the disruption of endoscopy training in a tertiary care center in Germany. DESIGN/METHOD: The reorganization of a high-volume endoscopy unit during the German COVID-19 outbreak is described with special focus on endoscopy trainees. Changes in case volume of gastroenterology fellows were evaluated and compared to a year prior to the outbreak. RESULTS: Reallocation of resources led to the transfer of gastroenterology fellows to intensive care and infectious disease units. Case volume of fellows declined between January and April 2020 by up to 63%. When compared with data from the year prior to the outbreak, endoscopy performed by fellows reduced by up to 56%. Educational meetings and skill evaluation were cancelled indefinitely. CONCLUSION: The COVID-19 outbreak has had a negative impact on endoscopy training of gastroenterology fellows in a high-volume center in Germany. This must be taken into consideration when planning "return-strategies" after the pandemic.

A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology.

Background and aim The growing number of publications on the application of artificial intelligence (AI) in medicine underlines the enormous importance and potential of this emerging field of research. In gastrointestinal endoscopy, AI has been applied to all segments of the gastrointestinal tract most importantly in the detection and characterization of colorectal polyps. However, AI research has been published also in the stomach and esophagus for both neoplastic and non-neoplastic disorders. The various technical as well as medical aspects of AI, however, remain confusing especially for non-expert physicians. This physician-engineer co-authored review explains the basic technical aspects of AI and provides a comprehensive overview of recent publications on AI in gastrointestinal endoscopy. Finally, a basic insight is offered into understanding publications on AI in gastrointestinal endoscopy.