Artificial intelligence in pancreaticobiliary endoscopy

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.

Impact of Artificial Intelligence on Pancreaticobiliary Endoscopy

Pancreaticobiliary diseases can lead to significant morbidity and their diagnoses rely on imaging and endoscopy which are dependent on operator expertise. Artificial intelligence (AI) has seen a rapid uptake in the field of luminal endoscopy, such as polyp detection during colonoscopy. However, its use for pancreaticobiliary endoscopic modalities such as endoscopic ultrasound (EUS) and cholangioscopy remains scarce, with only few studies available. In this review, we delve into the current evidence, benefits, limitations, and future scope of AI technologies in pancreaticobiliary endoscopy.

Artificial intelligence using advanced imaging techniques and cholangiocarcinoma: Recent advances and future direction

While cholangiocarcinoma represents only about 3% of all gastrointestinal tumors, it has a dismal survival rate, usually because it is diagnosed at a late stage. The utilization of Artificial Intelligence (AI) in medicine in general, and in gastroenterology has made gigantic steps. However, the application of AI for biliary disease, in particular for cholangiocarcinoma, has been sub-optimal. The use of AI in combination with clinical data, cross-sectional imaging (computed tomography, magnetic resonance imaging) and endoscopy (endoscopic ultrasound and cholangioscopy) has the potential to significantly improve early diagnosis and the choice of optimal therapeutic options, leading to a transformation in the prognosis of this feared disease. In this review we summarize the current knowledge on the use of AI for the diagnosis and management of cholangiocarcinoma and point to future directions in the field.

EASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis

BACKGROUND

Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed.

METHODS

The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit-learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross-validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross-validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP).

RESULTS

The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy-to-use web application in the Streamlit Python-based framework (http://easy-app.org/).

CONCLUSIONS

The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model.

The evolving role of EUS-guided tissue acquisition

The introduction of endoscopic ultrasound-guided fine-needle aspiration into clinical practice was a pivotal moment for diagnostic gastrointestinal endoscopy. It facilitates the ease of tissue acquisition from previously inaccessible sites. The performance characteristics of cytological diagnosis are excellent. However, there remain areas of inadequacies. These include procedural inefficiencies such as the need for rapid on-site cytological evaluation or macroscopic on-site evaluation, the crucial role of histology for diagnosis in specific conditions, the issue of sampling errors and the need for repeat procedures, and the shift towards personalized medicine, which requires histology, immunohistochemical studies, and molecular analysis. The original Trucut biopsy needle had been cumbersome to use, but the recent introduction of newer-generation biopsy needles has transformed the landscape, such that there is now a greater focus on tissue acquisition for histological assessment. Concomitant technological advances of endoscopic ultrasound processors enabled higher-resolution imaging, and facilitated image enhancement using contrast harmonic endoscopic ultrasound and endoscopic ultrasound elastography. These techniques can be used as an adjunct to guide tissue acquisition in challenging situations. There is ongoing research on the use of artificial intelligence to complement diagnostic endoscopic ultrasound and the early data are promising. Artificial intelligence may be especially important to guide clinical decision-making if biopsy results are nondiagnostic.