Evaluation of a single-use gastroscope in patients presenting with suspected upper gastrointestinal hemorrhage: a pilot feasibility study (One-Scope I).

BACKGROUND : Outbreaks of multidrug-resistant bacteria due to contaminated duodenoscopes and infection risks during the COVID-19 pandemic have driven the development of single-use endoscopes. The first single-use gastroscope is now available in Europe. Besides waste disposal and cost issues, the infection risk and performance remain unclear. We aimed to evaluate a single-use gastroscope in patients with signs of upper gastrointestinal bleeding. METHODS : 20 consecutive patients presenting with clinical signs of upper gastrointestinal bleeding between October and November 2022 were included in this case series. The primary aim was technical success, defined as access to the descending duodenum and adequate assessment of the upper gastrointestinal tract for the presence of a bleeding site. RESULTS : The primary aim was achieved in 19/20 patients (95 %). The bleeding site was identified in 18 patients. A therapeutic intervention was performed in six patients (two cap-mounted clips, one standard hemostatic clip, two variceal band ligations, one hemostatic powder, two adrenaline injections); technical and clinical success were achieved in all six patients. Two crossovers to a standard gastroscope occurred. CONCLUSIONS : Use of single-use gastroscopes may be feasible for patients presenting for urgent endoscopic evaluation and treatment of upper gastrointestinal bleeding.

Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm.

BACKGROUND AND AIMS: Celiac disease with its endoscopic manifestation of villous atrophy (VA) is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of VA at routine EGD may improve diagnostic performance. METHODS: A dataset of 858 endoscopic images of 182 patients with VA and 846 images from 323 patients with normal duodenal mucosa was collected and used to train a ResNet18 deep learning model to detect VA. An external dataset was used to test the algorithm, in addition to 6 fellows and 4 board-certified gastroenterologists. Fellows could consult the AI algorithm's result during the test. From their consultation distribution, a stratification of test images into "easy" and "difficult" was performed and used for classified performance measurement. RESULTS: External validation of the AI algorithm yielded values of 90%, 76%, and 84% for sensitivity, specificity, and accuracy, respectively. Fellows scored corresponding values of 63%, 72%, and 67% and experts scored 72%, 69%, and 71%, respectively. AI consultation significantly improved all trainee performance statistics. Although fellows and experts showed significantly lower performance for difficult images, the performance of the AI algorithm was stable. CONCLUSIONS: In this study, an AI algorithm outperformed endoscopy fellows and experts in the detection of VA on endoscopic still images. AI decision support significantly improved the performance of nonexpert endoscopists. The stable performance on difficult images suggests a further positive add-on effect in challenging cases.