An interpretable artificial intelligence system for detecting risk factors of gastroesophageal variceal bleeding

The Role of Artificial Intelligence and Big Data for Gastrointestinal Disease

Artificial intelligence (AI) is a rapidly evolving presence in all fields and industries, with the ability to both improve quality and reduce the burden of human effort. Gastroenterology is a field with a focus on diagnostic techniques and procedures, and AI and big data have established and growing roles to play. Alongside these opportunities are challenges, which will evolve in parallel.

Endoscopic ultrasound-guided therapies in the treatment of gastric varices: An in-depth examination of associated adverse events

The Baveno VII consensus, released in 2023, recommends that the endoscopic treatment of choice for managing bleeding gastric varices (GV) is endoscopic ultrasound (EUS)-guided treatment, specifically EUS-guided cyanoacrylate (CYA) glue injection. This approach has been endorsed due to its efficacy in controlling bleeding while reducing rebleeding rates, compared to other endoscopic techniques. Despite its efficacy, CYA injection for GV has been linked to rare but serious adverse events, such as glue embolization leading to pulmonary embolism, infection/bacteremia, splenic infarction, intra-procedural and post-procedural complications. The technique for endoscopic obliteration of GV was subsequently refined using EUS guidance, aiming to more accurately direct the injection into the varix, reduce the volume of injected glue, target the perforating vein when possible, and confirm obliteration of GV via Doppler examination.

Development and internal validation of prediction model for rebleeding within one year after endoscopic treatment of cirrhotic varices: consideration from organ-based CT radiomics signature

BACKGROUND

Rebleeding after endoscopic treatment for esophagogastric varices (EGVs) in cirrhotic patients remains a significant clinical challenge, with high mortality rates and limited predictive tools. Current methods, relying on clinical indicators, often lack precision and fail to provide personalized risk assessments. This study aims to develop and validate a novel, non-invasive prediction model based on CT radiomics to predict rebleeding risk within one year of treatment, integrating radiomic features from key organs and clinical data.

METHODS

123 patients were enrolled and divided into rebleeding (n = 44) and non-bleeding group (n = 79) within 1 year after endoscopic treatment of EGVs. The liver, spleen, and the lower part of the esophagus were segmented and the extracted radiomics features were selected to construct liver/spleen/esophagus radiomics signatures based on logistic regression. Clinic-radiomics combined models and multi-organ combined radiomics models were constructed based on independent model scores using logistic regression. The model performance was evaluated by ROC analysis, calibration and decision curves. The continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices were analyzed.

RESULTS

The clinical-liver combined model had the highest AUC of 0.931 (95% CI: 0.887-0.974), which was followed by the liver-based model with AUC of 0.891 (95% CI: 0.835-0.74). The decision curves also showed that the clinical-liver combined model afforded a greater net benefit compared to other models within the threshold probability of 0.45 to 0.80. Significant improvements in discrimination (IDI, P < 0.05) and reclassification (NRI, P < 0.05) were obtained for clinical-liver combined model compared with the independent ones.

CONCLUSION

The independent and combined liver-based CT radiomics models performed well in predicting rebleeding within 1 year after endoscopic treatment of EGVs.

ENDOANGEL improves detection of missed colorectal adenomas in second colonoscopy: A retrospective study

The ENDOANGEL (EN) computer-assisted detection technique has emerged as a promising tool for enhancing the detection rate of colorectal adenomas during colonoscopies. However, its efficacy in identifying missed adenomas during subsequent colonoscopies remains unclear. Thus, we herein aimed to compare the adenoma miss rate (AMR) between EN-assisted and standard colonoscopies. Data from patients who underwent a second colonoscopy (EN-assisted or standard) within 6 months between September 2022 and May 2023 were analyzed. The EN-assisted group exhibited a significantly higher AMR (24.3% vs 11.9%, P = .005) than the standard group. After adjusting for potential confounders, multivariable analysis revealed that the EN-assisted group had a better ability to detect missed adenomas than the standard group (odds ratio = 2.89; 95% confidence interval = 1.14-7.80, P = .029). These findings suggest that EN-assisted colonoscopy represents a valuable advancement in improving AMR compared with standard colonoscopy. The integration of EN-assisted colonoscopy into routine clinical practice may offer significant benefits to patients requiring hospital resection of lesions following adenoma detection during their first colonoscopy.

The Prediction and Treatment of Bleeding Esophageal Varices in the Artificial Intelligence Era: A Review

Esophageal varices (EVs), a significant complication of cirrhosis, present a considerable challenge in clinical practice due to their high risk of bleeding and associated morbidity and mortality. This manuscript explores the transformative role of artificial intelligence (AI) in the management of EV, particularly in enhancing diagnostic accuracy and predicting bleeding risks. It underscores the potential of AI in offering noninvasive, efficient alternatives to traditional diagnostic methods such as esophagogastroduodenoscopy (EGD). The complexity of EV management is highlighted, necessitating a multidisciplinary approach that includes pharmacological therapy, endoscopic interventions, and, in some cases, surgical options tailored to individual patient profiles. Additionally, the paper emphasizes the importance of integrating AI into medical education and practice, preparing healthcare professionals for the evolving landscape of medical technology. It projects a future where AI significantly influences the management of gastrointestinal bleeding, improving clinical decision-making, patient outcomes, and overall healthcare efficiency. The study advocates for a patient-centered approach in healthcare, balancing the incorporation of innovative technologies with ethical principles and the diverse needs of patients to optimize treatment efficacy and enhance healthcare accessibility.

Applications of artificial intelligence in gastroscopy: a narrative review

Gastroscopy, a critical tool for the diagnosis of upper gastrointestinal diseases, has recently incorporated artificial intelligence (AI) technology to alleviate the challenges involved in endoscopic diagnosis of some lesions, thereby enhancing diagnostic accuracy. This narrative review covers the current status of research concerning various applications of AI technology to gastroscopy, then discusses future research directions. By providing this review, we hope to promote the integration of gastroscopy and AI technology, with long-term clinical applications that can assist patients.

ENDOANGEL versus water exchange for the detection of colorectal adenomas

Background

Recently, the ENDOANGEL (EN) system, a computer-assisted detection technique, and water exchange (WE) assisted colonoscopy have both been shown to increase the colorectal adenoma detection rate (ADR).

Objectives

The aim of this study was to compare the ADR between EN- and WE-assisted colonoscopy.

Design

This was a retrospective study.

Methods

Data from patients who underwent either EN- or WE-assisted colonoscopy between October 2021 and August 2022 were analysed consecutively. The primary outcome measure was the ADR.

Results

The ADR was found to be similar between the EN and WE groups, with 80 out of 199 (40.2%) patients in the EN group compared to 78 out of 174 (44.8%) patients in the WE group [1.21; 95% confidence interval (CI), 0.80-1.83]. In the analysis using stabilized inverse probability treatment weighting after adjustment for confounding factors, both colonoscopy methods had similar performance in terms of ADR (1.41; 95% CI, 0.88-2.27).

Conclusion

EN was found to be comparable to WE in terms of ADR during colonoscopy, and both methods may be effectively used in clinical practice.

Feasibility of computed tomography portal venography in the preoperative evaluation of emergent TIPS creation for cirrhotic patients with acute variceal bleeding

OBJECTIVES

To evaluate the feasibility of computed tomography portal venography (CTPV) in the preoperative evaluation of emergent transjugular intrahepatic portosystemic shunt (TIPS) creation for cirrhotic patients with acute variceal bleeding (AVB).

METHODS

One hundred and forty-eightcirrhotic patients with AVB undergoing emergent TIPS creation from January 2016 to December 2022 in our institution were enrolled in the retrospective study. The primary outcome was the consistency between CTPV and endoscopy in the classification and grading of gastroesophageal varices (GEVs). The second outcome was extraluminal CTPV findings. The consistency of CTPV and endoscopy in the classification and grading of GEVs was determined by Kappa values.

RESULTS

Emergent TIPS creation was technically successful in all patients. Forty-five patients underwent preoperative endoscopy. The results of CTPV diagnosis of GEVs classification were that 112, 28, and 8 patients were classified as gastroesophageal varices type 1 (GOV1), GOV2, and isolated gastric varices type 1 (IGV1), respectively. In diagnosing the classification and grading of GEVs, CTPV showed substantial agreement with preoperative endoscopy, with Kappa values of 0.823 and 0.625, respectively. CTPV provided the afferent and afferent vessels of GEVs for emergent TIPS creation.

CONCLUSION

CTPV is feasible and effective to act as an alternative preoperative evaluation method to endoscopy for cirrhotic patients with AVB undergoing emergent TIPS creation.