Detection of multiple lesions of gastrointestinal tract for endoscopy using artificial intelligence model: a pilot study.

BACKGROUND: This study was aimed to develop a computer-aided diagnosis (CAD) system with deep-learning technique and to validate its efficiency on detecting the four categories of lesions such as polyps, advanced cancer, erosion/ulcer and varices at endoscopy. METHODS: A deep convolutional neural network (CNN) that consists of more than 50 layers were trained with a big dataset containing 327,121 white light images (WLI) of endoscopy from 117,005 cases collected from 2012 to 2017. Two CAD models were developed using images with or without annotation of the training dataset. The efficiency of the CAD system detecting the four categories of lesions was validated by another dataset containing consecutive cases from 2018 to 2019. RESULTS: A total of 1734 cases with 33,959 images were included in the validation datasets which containing lesions of polyps 1265, advanced cancer 500, erosion/ulcer 486, and varices 248. The CAD system developed in this study may detect polyps, advanced cancer, erosion/ulcer and varices as abnormality with the sensitivity of 88.3% and specificity of 90.3%, respectively, in 0.05 s. The training datasets with annotation may enhance either sensitivity or specificity about 20%, p = 0.000. The sensitivities and specificities for polyps, advanced cancer, erosion/ulcer and varices reached about 90%, respectively. The detect efficiency for the four categories of lesions reached to 89.7%. CONCLUSION: The CAD model for detection of multiple lesions in gastrointestinal lumen would be potentially developed into a double check along with real-time assessment and interpretation of the findings encountered by the endoscopists and may be a benefit to reduce the events of missing lesions.

The impacts of acid suppression on duodenal microbiota during the early phase of severe acute pancreatitis.

Duodenal dysbiosis may be potential infection risks in patients with severe acute pancreatitis (SAP). Acid-suppression drugs (ACDs) are widely used in SAP patients in Asian countries. However, the impact of ACDs on duodenal microbiota during the early phase of SAP is still unknown. This randomized controlled clinical trial evaluated the impacts of esomeprazole (Eso), one of ACDs on duodenal microbiota during the first week of SAP with duodenal aspirates culture and 16sRNA Illumina sequencing analysis. 66 patients were randomized as 1:1 ratio into Eso group (Eso 40 mg/day) and Eso-N group (no Eso). The occurrence of duodenal bacterial overgrowth (DBO) was significantly higher in Eso group (about 85%) than that in Eso-N group (about 42%). The duodenal microbiota of the SAP patients shifted away from that of the normal control. There were differences between the Eso-N and Eso groups including enriched abundances of the class Negativicutes, order Selenomonadales and genus Veillonella. Acid suppression significantly increased incidence of Candida oesophagitis (CE) by 8-folds but did not increase other infectious events. In conclusion, acid suppression greatly increased the occurrence of DBO, duodenal dysbiosis and CE during the first week of SAP. Restrictive use of acid-suppressing medications might be helpful to reduce CE and potential risk of pancreatic infection in SAP patients.Trial registration: Chictr.org, ChiCTR-IPR-16008301, Registered April 18 2016, http://www.chictr.org.cn/showproj.aspx?proj=14089 .