Endoscopist's Judgment Is as Useful as Risk Scores for Predicting Outcome in Peptic Ulcer Bleeding: A Multicenter Study

BM-BronchoLC - A rich bronchoscopy dataset for anatomical landmarks and lung cancer lesion recognition

Flexible bronchoscopy has revolutionized respiratory disease diagnosis. It offers direct visualization and detection of airway abnormalities, including lung cancer lesions. Accurate identification of airway lesions during flexible bronchoscopy plays an important role in the lung cancer diagnosis. The application of artificial intelligence (AI) aims to support physicians in recognizing anatomical landmarks and lung cancer lesions within bronchoscopic imagery. This work described the development of BM-BronchoLC, a rich bronchoscopy dataset encompassing 106 lung cancer and 102 non-lung cancer patients. The dataset incorporates detailed localization and categorical annotations for both anatomical landmarks and lesions, meticulously conducted by senior doctors at Bach Mai Hospital, Vietnam. To assess the dataset's quality, we evaluate two prevalent AI backbone models, namely UNet++ and ESFPNet, on the image segmentation and classification tasks with single-task and multi-task learning paradigms. We present BM-BronchoLC as a reference dataset in developing AI models to assist diagnostic accuracy for anatomical landmarks and lung cancer lesions in bronchoscopy data.

Development and validation of a model to predict rebleeding within three days after endoscopic hemostasis for high-risk peptic ulcer bleeding

Background

Peptic ulcer bleeding remains a typical medical emergency with significant morbidity and mortality. Peptic ulcer rebleeding often occurs within three days after emergent endoscopic hemostasis. Our study aims to develop a nomogram to predict rebleeding within three days after emergent endoscopic hemostasis for high-risk peptic ulcer bleeding.

Methods

We retrospectively reviewed the data of 386 patients with bleeding ulcers and high-risk stigmata who underwent emergent endoscopic hemostasis between March 2014 and October 2018. The least absolute shrinkage and selection operator method was used to identify predictors. The model was displayed as a nomogram. Internal validation was carried out using bootstrapping. The model was evaluated using the calibration plot, decision-curve analyses, and clinical impact curve.

Results

Overall, 386 patients meeting the inclusion criteria were enrolled, with 48 patients developed rebleeding within three days after initial endoscopic hemostasis. Predictors contained in the nomogram included albumin, prothrombin time, shock, haematemesis/melena and Forrest classification. The model showed good discrimination and good calibration with a C-index of 0.854 (C-index: 0.830 via bootstrapping validation). Decision-curve analyses and clinical impact curve also demonstrated that it was clinically valuable.

Conclusion

This study presents a nomogram that incorporates clinical, laboratory, and endoscopic features, effectively predicting rebleeding within three days after emergent endoscopic hemostasis and identifying high-risk rebleeding patients with peptic ulcer bleeding.

Trial registration This clinical trial has been registered in the ClinicalTrials.gov (ID: NCT04895904) approved by the International Committee of Medical Journal Editors (ICMJE).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02145-9.

Current Status and Future Perspective of Artificial Intelligence in the Management of Peptic Ulcer Bleeding: A Review of Recent Literature

With the decreasing incidence of peptic ulcer bleeding (PUB) over the past two decades, the clinician experience of managing patients with PUB has also declined, especially for young endoscopists. A patient with PUB management requires collaborative care involving the emergency department, gastroenterologist, radiologist, and surgeon, from initial assessment to hospital discharge. The application of artificial intelligence (AI) methods has remarkably improved people's lives. In particular, AI systems have shown great potential in many areas of gastroenterology to increase human performance. Colonoscopy polyp detection or diagnosis by an AI system was recently introduced for commercial use to improve endoscopist performance. Although PUB is a longstanding health problem, these newly introduced AI technologies may soon impact endoscopists' clinical practice by improving the quality of care for these patients. To update the current status of AI application in PUB, we reviewed recent relevant literature and provided future perspectives that are required to integrate such AI tools into real-world practice.

Justification of Genetic Factors for Predicting the Risk of Acute Bleeding in Peptic Ulcer Disease

PAI genotyping for the G43A and 4G/5G polymorphisms was performed in 60 patients with peptic ulcer disease: 12 with an uncomplicated ulcer, 5 with perforation, the rest with ongoing bleeding. Fourteen patients had recurrent bleeding. The 5G/5G and G43A genotypes were not detected in patients with uncomplicated ulcers. All patients with ulcer perforation had the G43G genotype, 60% of patients had the 4G/4G genotype, and the rest of them had the 4G/5G and 5G/5G genotypes. The number of carriers of the 5G allele (86.05%) was higher in patients with bleeding than in ones with ulcer perforation (p=0.036) and ulcer without bleeding (p=0.021, χ2=5.32). The number of carriers of the 5G allele was higher in patients with recurrent bleeding (92.86%) than those without any relapses (82.76%) but there were no statistically significant differences (p=0.27, χ2=0.802). The G43G homozygous genotype was found in 94.12% of patients with peptic ulcer without bleeding, which was statistically significantly higher (p=0.02) than the ones with bleeding. The A allele was observed in 27.91% of patients with bleeding and 8.33% patients without any bleeding (p=0.05). The number of carriers of the A allele in patients with recurrent bleeding was statistically significantly higher than in ones without any bleeding (p=0.046). The 5G and A alleles in patients with a peptic ulcer can be used to predict the course of peptic ulcer disease and can be regarded as a predictor of the risk of bleeding relapse.