Abstract: Measurements of esophageal pressure and geometry are classified using a trained machine learning algorithm, such as a neural network or other classifier algorithm. Contractile response patterns can be identified in the esophageal pressure and geometry data, from which classified feature data can be generated. The classified feature data classify the esophageal pressure and geometry data as being indicative of an upper gastrointestinal disorder in the subject.

Abstract: Described here are systems and methods for four-dimensional manometry, which can include generating and displaying rendering data that simultaneously depict spacetime variations in impedance, pressure, and esophageal luminal morphology. From these data, bolus tracking and esophageal opening and velocity data can be measured and visualized without the need for additional imaging, thereby reducing a subject's exposure to otherwise necessary ionizing radiation.