Machine learning for the prediction of post-ERCP pancreatitis risk: A proof-of-concept study

Current status and issues for prediction and prevention of postendoscopic retrograde cholangiopancreatography pancreatitis

Acute pancreatitis, which sometimes results in mortality, is a significant complication of endoscopic retrograde cholangiopancreatography (ERCP). Many studies have been conducted to predict and prevent post-ERCP pancreatitis (PEP), and meta-analyses have been reported that summarized these studies. However, many issues remain unresolved. Many risk factors for PEP have been reported, and it is rare for patients undergoing ERCP to have only one risk factor. The use of artificial intelligence may be important for analyzing complex and diverse risk factors. It is desirable to develop an alternative test for pancreatic enzymes that can predict the onset of PEP within 1 h after ERCP. The effectiveness of low-dose nonsteroidal anti-inflammatory drugs (NSAIDs) are controversial. Nitrate and tacrolimus are considered medications that have additional effects on NSAIDs and may be used for the prevention of PEP. Pancreatic stent placement with deliberate placement of the guidewire into the pancreatic duct may be more effective in preventing PEP. A comparison between transpancreatic sphincterotomy with deliberate guidewire placement into the pancreatic duct and needle-knife precut sphincterotomy is necessary. Early precutting is thought to be effective for the prevention of PEP, and the effectiveness of primary precut has been reported. However, the optimal timing of precut for the prevention of PEP has not been sufficiently discussed. Further research on prediction and prevention must be conducted to eliminate the mortality caused by PEP.

Unlocking the Potential of AI in EUS and ERCP: A Narrative Review for Pancreaticobiliary Disease

Artificial Intelligence (AI) is transforming pancreaticobiliary endoscopy by enhancing diagnostic accuracy, procedural efficiency, and clinical outcomes. This narrative review explores AI's applications in endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP), emphasizing its potential to address diagnostic and therapeutic challenges in pancreaticobiliary diseases. In EUS, AI improves pancreatic mass differentiation, malignancy prediction, and landmark recognition, demonstrating high diagnostic accuracy and outperforming traditional guidelines. In ERCP, AI facilitates precise biliary stricture identification, optimizes procedural techniques, and supports decision-making through real-time data integration, improving ampulla recognition and predicting cannulation difficulty. Additionally, predictive analytics help mitigate complications like post-ERCP pancreatitis. The future of AI in pancreaticobiliary endoscopy lies in multimodal data fusion, integrating imaging, genomic, and molecular data to enable personalized medicine. However, challenges such as data quality, external validation, clinician training, and ethical concerns-like data privacy and algorithmic bias-must be addressed to ensure safe implementation. By overcoming these challenges, AI has the potential to redefine pancreaticobiliary healthcare, improving diagnostic accuracy, therapeutic outcomes, and personalized care.

A new preprocedural predictive risk model for post-endoscopic retrograde cholangiopancreatography pancreatitis: The SuPER model

Background

Post-endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) is a severe and deadly adverse event following ERCP. The ideal method for predicting PEP risk before ERCP has yet to be identified. We aimed to establish a simple PEP risk score model (SuPER model: Support for PEP Reduction) that can be applied before ERCP.

Methods

This multicenter study enrolled 2074 patients who underwent ERCP. Among them, 1037 patients each were randomly assigned to the development and validation cohorts. In the development cohort, the risk score model for predicting PEP was established via logistic regression analysis. In the validation cohort, the performance of the model was assessed.

Results

In the development cohort, five PEP risk factors that could be identified before ERCP were extracted and assigned weights according to their respective regression coefficients: -2 points for pancreatic calcification, 1 point for female sex, and 2 points for intraductal papillary mucinous neoplasm, a native papilla of Vater, or the pancreatic duct procedures (treated as 'planned pancreatic duct procedures' for calculating the score before ERCP). The PEP occurrence rate was 0% among low-risk patients (≤0 points), 5.5% among moderate-risk patients (1-3 points), and 20.2% among high-risk patients (4-7 points). In the validation cohort, the C statistic of the risk score model was 0.71 (95% CI 0.64-0.78), which was considered acceptable. The PEP risk classification (low, moderate, and high) was a significant predictive factor for PEP that was independent of intraprocedural PEP risk factors (precut sphincterotomy and inadvertent pancreatic duct cannulation) (OR 4.2, 95% CI 2.8-6.3; p<0.01).

Conclusions

The PEP risk score allows an estimation of the risk of PEP prior to ERCP, regardless of whether the patient has undergone pancreatic duct procedures. This simple risk model, consisting of only five items, may aid in predicting and explaining the risk of PEP before ERCP and in preventing PEP by allowing selection of the appropriate expert endoscopist and useful PEP prophylaxes.

Funding

No external funding was received for this work.

Advancements in biliopancreatic endoscopy - A comprehensive review of artificial intelligence in EUS and ERCP

The development and implementation of artificial intelligence (AI), particularly deep learning (DL) models, has generated significant interest across various fields of gastroenterology. While research in luminal endoscopy has seen rapid translation to clinical practice with approved AI devices, its potential extends far beyond, offering promising benefits for biliopancreatic endoscopy like optical characterization of strictures during cholangioscopy or detection and classification of pancreatic lesions during diagnostic endoscopic ultrasound (EUS). This narrative review provides an up-to-date of the latest literature and available studies in this field. Serving as a comprehensive guide to the current landscape of AI in biliopancreatic endoscopy, emphasizing technological advancements, main applications, ethical considerations, and future directions for research and clinical implementation.

Multistep validation of a post-ERCP pancreatitis prediction system integrating multimodal data: a multicenter study

BACKGROUND AND AIMS

The impact of various categories of information on the prediction of post-ERCP pancreatitis (PEP) remains uncertain. We comprehensively investigated the risk factors associated with PEP by constructing and validating a model incorporating multimodal data through multiple steps.

METHODS

Cases (n = 1916) of ERCP were retrospectively collected from multiple centers for model construction. Through literature research, 49 electronic health record (EHR) features and 1 image feature related to PEP were identified. The EHR features were categorized into baseline, diagnosis, technique, and prevention strategies, covering pre-ERCP, intra-ERCP, and peri-ERCP phases. We first incrementally constructed models 1 to 4 incorporating these 4 feature categories and then added the image feature into models 1 to 4 and developed models 5 to 8. All models underwent testing and comparison using both internal and external test sets. Once the optimal model was selected, we conducted comparisons among multiple machine learning algorithms.

RESULTS

Compared with model 2 that incorporated baseline and diagnosis features, adding technique and prevention strategies (model 4) greatly improved the sensitivity (63.89% vs 83.33%, P < .05) and specificity (75.00% vs 85.92%, P < .001). A similar tendency was observed in the internal and external tests. In model 4, the top 3 features ranked by weight were previous pancreatitis, nonsteroidal anti-inflammatory drug use, and difficult cannulation. The image-based feature has the highest weight in models 5 to 8. Finally, model 8 used a random forest algorithm and showed the best performance.

CONCLUSIONS

We first developed a multimodal prediction model for identifying PEP with a clinical-acceptable performance. The image and technique features are crucial for PEP prediction.

Artificial intelligence in pancreaticobiliary endoscopy: Current applications and future directions

Pancreaticobiliary endoscopy is an essential tool for diagnosing and treating pancreaticobiliary diseases. However, it does not fully meet clinical needs, which presents challenges such as significant difficulty in operation and risks of missed diagnosis or misdiagnosis. In recent years, artificial intelligence (AI) has enhanced the diagnostic and treatment efficiency and quality of pancreaticobiliary endoscopy. Diagnosis and differential diagnosis based on endoscopic ultrasound (EUS) images, pathology of EUS-guided fine-needle aspiration or biopsy, need for endoscopic retrograde cholangiopancreatography (ERCP) and assessment of operational difficulty, postoperative complications and prediction of patient prognosis, and real-time procedure guidance. This review provides an overview of AI applications in pancreaticobiliary endoscopy and proposes future development directions in aspects such as data quality and algorithmic interpretability, aiming to provide new insights for the integration of AI technology with pancreaticobiliary endoscopy.

Current Gallstone Treatment Methods, State of the Art

This study aims to provide valuable references for clinicians in selecting appropriate surgical methods for biliary tract stones based on patient conditions. In this paper, the advantages and disadvantages of various minimally invasive cholelithiasis surgical techniques are systematically summarized and innovative surgical approaches and intelligent stone removal technologies are introduced. The goal is to evaluate and predict future research priorities and development trends in the field of gallstone surgery. In recent years, the incidence of gallstone-related diseases, including cholecystolithiasis and choledocholithiasis, has significantly increased. This surge in cases has prompted the development of several innovative methods for gallstone extraction, with minimally invasive procedures gaining the most popularity. Among these techniques, PTCS, ERCP, and LCBDE have garnered considerable attention, leading to new surgical techniques; however, it must be acknowledged that each surgical method has its unique indications and potential complications. The primary challenge for clinicians is selecting a surgical approach that minimizes patient trauma while reducing the incidence of complications such as pancreatitis and gallbladder cancer and preventing the recurrence of gallstones. The integration of artificial intelligence with stone extraction surgeries offers new opportunities to address this issue. Regarding the need for preoperative preparation for PTCS surgery, we recommend a combined approach of PTBD and PTOBF. For ERCP-based stone extraction, we recommend a small incision of the Oddi sphincter followed by 30 s of balloon dilation as the optimal procedure. If conditions permit, a biliary stent can be placed post-extraction. For the surgical approach of LCBDE, we recommend the transduodenal (TD) approach. Artificial intelligence is involved throughout the entire process of gallstone detection, treatment, and prognosis, and more AI-integrated medical technologies are expected to be applied in the future.

Predictive models in EUS/ERCP

Predictive models (PMs) in endoscopic retrograde cholangiopancreatography (ERCP) and endoscopic ultrasound (EUS) have the potential to improve patient outcomes, enhance diagnostic accuracy, and guide therapeutic interventions. This review aims to summarize the current state of predictive models in ERCP and EUS and their clinical implications. To be considered useful in clinical practice a PM should be accurate, easy to perform, and may consider objective variables. PMs in ERCP estimate correct indication, probability of success, and the risk of developing adverse events. These models incorporate patient-related factors and technical aspects of the procedure. In the field of EUS, these models utilize clinical and imaging data to predict the likelihood of malignancy, presence of specific lesions, or risk of complications related to therapeutic interventions. Further research, validation, and refinement are necessary to maximize the utility and impact of these models in routine clinical practice.