Gastroenterology Insights

Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal malignancy. The identification and management of precursor lesions, particularly the increasingly common intraductal papillary mucinous neoplasms (IPMNs), pose a significant challenge, creating a profound clinical dilemma between intercepting pancreatic ductal adenocarcinoma and avoiding surgical overtreatment. This literature review aims to synthesize the latest evidence to facilitate a transition from purely morphology-based surveillance toward a biologically informed risk stratification paradigm. This approach could provide a personalized risk-stratification algorithm that optimizes therapeutic management and enables timely intervention for pancreatic cancer. By using PubMed, Embase, Scopus, and Web of Science, we analyzed and summarized key findings from recent literature (2020–2025), including cohort studies, mechanistic analyses, evidence-based guidelines, and systematic reviews on cyst fluid biomarkers (CEA panels, DNA/RNA sequencing), and emerging AI applications. Prospective and multicenter studies consistently report that NOD is independently associated with high-risk stigmata, cyst progression, and malignant transformation. Mechanistic research suggests a bidirectional interplay between the evolving neoplasia and pancreatic endocrine dysfunction. Updated guidelines underscore the need for more precise diagnostic algorithms. Recent work demonstrates that advanced cyst fluid markers—CEA panels, DNA/RNA sequencing, and multi-omic signatures—significantly improve diagnostic accuracy. Furthermore, explainable AI models show encouraging performance in predicting malignancy and assisting patient triage. Risk stratification in PCLs is shifting from morphology-based assessment toward integrated, multimodal approaches combining clinical, endocrine, imaging, molecular, and computational data. Recent evidence positions new-onset diabetes as a clinically accessible and biologically plausible marker of high-risk IPMNs. Similarly, molecular assays and AI-enhanced analytics provide an additional layer of diagnostic precision. The development of personalized risk prediction algorithms could improve early detection of malignancy while reducing unnecessary surgical resections.

Endoscopic ultrasound-guided gallbladder drainage (EUS-GBD) is an emerging intervention that provides a minimally invasive approach to drainage of the gallbladder, showing promising results in treating acute cholecystitis (AC) and malignant biliary obstruction (MBO). This review summarizes the current applications of EUS-GBD and compares its clinical effectiveness with traditional methods such as percutaneous transhepatic gallbladder drainage (PT-GBD) and endoscopic transpapillary gallbladder drainage (ET-GBD). Available evidence suggests that EUS-GBD may offer potential advantages in terms of success rates and complication profiles, particularly in patients who are not candidates for surgery or those at high surgical risk. The method is effective in reducing inflammation, alleviating symptoms from obstruction, and improving patient quality of life. This article also discusses the technical evolution of EUS-GBD, its indications, complications, and its comparative advantages over other drainage techniques. These observations suggest that EUS-GBD may represent a valuable addition to the therapeutic armamentarium for selected high-risk patients.

Background/Objectives: The global adoption of minimally invasive surgery has generated extensive video repositories, creating new opportunities for data-driven surgical education and quality assessment. Automated surgical phase recognition enables objective trainee evaluation, standardized competency assessment, and systematic procedural documentation. However, class imbalance in surgical workflows, where certain phases comprise 30–35% of frames while others represent only 5–10%, remains a significant challenge. This imbalance causes models to underperform on underrepresented yet clinically important phases. Methods: A retrospective analysis of laparoscopic cholecystectomy videos is performed with the implementation of a frame—based deep learning framework to develop and validate a surgical phase recognition pipeline based on ResNet-50 architecture with transfer learning. The model was designed to extract features from surgical video frames and classify them into seven distinct phases, without incorporating temporal context. We used the Cholec80 dataset and applied class balancing techniques to address inherent class imbalance. Results: The model achieved a mean balanced accuracy of 91.80% across five folds with consistent performance across all surgical phases. Per-phase F1-scores ranged from 0.89 to 0.95, demonstrating balanced classification without significant performance degradation on underrepresented phases. The confusion matrix revealed prediction errors primarily among adjacent or visually similar phases, reflecting the inherent ambiguity of surgical phase transitions. In practical terms, the model correctly identified the surgical phase in more than 9 out of 10 frames, enabling reliable automated segmentation of the operative workflow. Conclusions: This study demonstrates that artificial intelligence can reliably analyze surgical video data, achieving consistent and accurate phase recognition in laparoscopic cholecystectomy.