S3275 AI-Driven Endoscopic Ultrasound – A Case Series of Applications in Multiple Clinical Settings

Introduction: Endoscopic ultrasound (EUS) can be performed in a wide range of clinical scenarios, being particularly fundamental for the assessment of pancreatic cystic and solid lesions, as well as for the evaluation of chronic pancreatitis. Additionally, EUS plays a key role in lymph node assessment for staging of gastrointestinal and lung cancers. Thanks to its proximity to target structures and the ability to obtain tissue samples, EUS can directly inform patient management. However, its diagnostic accuracy remains suboptimal and is affected by high interobserver variability in lesion interpretation and sampling. This case series illustrates how a previously published artificial intelligence (AI)-based tool can be integrated into EUS in 3 distinct clinical scenarios. Importantly, model predictions and AI outputs did not influence clinical decision-making during the procedures. Case Description/Methods: In case 1, A 64-year-old patient with jaundice and weight loss underwent EUS, which revealed a solid-cystic mass in the head of the pancreas. The AI-based cystic component analysis predicted a probability over 90% of a mucinous cystic lesion. Evaluation of the solid component yielded a probability over 85% of adenocarcinoma. Targeted sampling confirmed the presence of adenocarcinoma arising from a intraductal papillary mucinous neoplasm (IPMN) in the head of pancreas. In case 2, during evaluation of a 50-year-old patient with chronic pancreatitis and normal tumor markers, EUS identified an 18 mm peripancreatic lymph node. AI-based lymph node analysis predicted a benign/reactive profile, which was later confirmed by histology and clinical follow-up. In case 3, a 65-year-old woman with fatigue and weight loss underwent EUS due to suspicious celiac lymph nodes identified on computed tomography imaging. During the EUS, AI analysis predicted malignancy in 1 of the lymph nodes. Given the endoscopic appearance, the node was biopsied, and histology confirmed metastatic lung cancer. Discussion: This multicenter case series highlights the potential of AI integration into endoscopic ultrasound clinical practice, offering real-time support in diagnostic evaluation and lesion sampling. Applications include the assessment of pancreatic focal lesions, evaluation of patients with chronic pancreatitis, and lymph node characterization. Although AI-assisted endoscopic ultrasound is not intended to replace histopathological diagnosis, it may play a pivotal role in clinical decision-making, transforming the management of patients undergoing this procedure.