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.

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.