How to Cope with Big Data in Functional Analysis of the Esophagus

Abstract

<b><i>Introduction:</i></b> Esophageal motility disorders have a severe impact on patients’ quality of life. While high-resolution manometry (HRM) is the gold standard in the diagnosis of esophageal motility disorders, intermittently occurring muscular deficiencies often remain undiscovered if they do not lead to an intense level of discomfort or cause suffering in patients. Ambulatory long-term HRM allows us to study the circadian (dys)function of the esophagus in a unique way. With the prolonged examination period of 24 h, however, there is an immense increase in data which requires personnel and time for evaluation not available in clinical routine. Artificial intelligence (AI) might contribute here by performing an autonomous analysis. <b><i>Methods:</i></b> On the basis of 40 previously performed and manually tagged long-term HRM in patients with suspected temporary esophageal motility disorders, we implemented a supervised machine learning algorithm for automated swallow detection and classification. <b><i>Results:</i></b> For a set of 24 h of long-term HRM by means of this algorithm, the evaluation time could be reduced from 3 days to a core evaluation time of 11 min for automated swallow detection and clustering plus an additional 10–20 min of evaluation time, depending on the complexity and diversity of motility disorders in the examined patient. In 12.5% of patients with suggested esophageal motility disorders, AI-enabled long-term HRM was able to reveal new and relevant findings for subsequent therapy. <b><i>Conclusion:</i></b> This new approach paves the way to the clinical use of long-term HRM in patients with temporary esophageal motility disorders and might serve as an ideal and clinically relevant application of AI.