Future perspectives in esophageal manometry

Esophageal manometry has undergone significant advancements, transitioning from conventional line tracings to high-resolution manometry with topographic analysis. This evolution has improved the classification and diagnosis of esophageal motility disorders, as defined by the Chicago Classification. However, challenges remain in interpreting borderline cases, assessing esophagogastric junction outflow obstruction, and correlating manometric findings with clinical symptoms. Artificial intelligence (AI) has emerged as a promising tool for enhancing esophageal manometry by enabling automated data analysis, pattern recognition, and predictive modeling. Future perspectives include the integration of AI for automated analysis, refinement of pressure topography metrics, and incorporation of adjunctive testing such as functional luminal imaging probe technology. Additionally, novel catheter designs and ambulatory manometry may enhance diagnostic accuracy and patient comfort. Integrating manometry findings with biomechanical models and machine learning techniques may support the development of more personalized management strategies. This review explores current and emerging technologies and their potential impact on the future of esophageal manometry, aiming to improve diagnostic precision and therapeutic outcomes in esophageal motility disorders.