Decision Graph Embedding for High-Resolution Manometry Diagnosis

Abstract

High-resolution manometry is an imaging modality which enables the categorization of esophageal motility disorders. Spatio-temporal pressure data along the esophagus is acquired using a tubular device and multiple test swallows are performed by the patient. Current approaches visualize these swallows as individual instances, despite the fact that aggregated metrics are relevant in the diagnostic process. Based on the current Chicago Classification, which serves as the gold standard in this area, we introduce a visualization supporting an efficient and correct diagnosis. To reach this goal, we propose a novel decision graph representing the Chicago Classification with workflow optimization in mind. Based on this graph, we are further able to prioritize the different metrics used during diagnosis and can exploit this prioritization in the actual data visualization. Thus, different disorders and their related parameters are directly represented and intuitively influence the appearance of our visualization. Within this paper, we introduce our novel visualization, justify the design decisions, and provide the results of a user study we performed with medical students as well as a domain expert. On top of the presented visualization, we further discuss how to derive a visual signature for individual patients that allows us for the first time to perform an intuitive comparison between subjects, in the form of small multiples.