Abstract: A catheter-based baro-hydrometer device measures or otherwise monitors esophageal function. The device includes a segmented balloon design, including a first and second balloon whose fill volumes can be independently controlled. In general, the device leverages secondary peristalsis related to balloon distention in order to study esophageal function. Pressure sensors coupled to the balloons record pressure data, which can be classified and processed to measure or otherwise monitor esophageal function.

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: A fluid mechanics-based analysis tool is implemented to compute pressure field data, fluid velocity data, and/or muscular work data in a lumen or other tubular organ or structure from planimetry and pressure data (e.g., measured using a balloon dilation or other planimetry catheter). In this way, flow data can be estimated, which are otherwise insensible from current planimetry catheter technologies due to economic and/or manufacturing limitations.

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.