Detecting and clustering swallow events in esophageal long-term high-resolution manometry

TL;DR

This paper introduces a deep learning method for detecting and clustering swallowing events in long-term esophageal HRM data, improving diagnostic accuracy and efficiency in clinical settings.

Contribution

It presents a novel deep learning pipeline for automated swallow detection and clustering in long-term HRM data, enhancing clinical analysis and diagnosis.

Findings

Detects over 94% of swallow events

Enables reliable clustering for clinical validation

Improves feasibility of long-term HRM in practice

Abstract

High-resolution manometry (HRM) is the gold standard in diagnosing esophageal motility disorders. As HRM is typically conducted under short-term laboratory settings, intermittently occurring disorders are likely to be missed. Therefore, long-term (up to 24h) HRM (LTHRM) is used to gain detailed insights into the swallowing behavior. However, analyzing the extensive data from LTHRM is challenging and time consuming as medical experts have to analyze the data manually, which is slow and prone to errors. To address this challenge, we propose a Deep Learning based swallowing detection method to accurately identify swallowing events and secondary non-deglutitive-induced esophageal motility disorders in LTHRM data. We then proceed with clustering the identified swallows into distinct classes, which are analyzed by highly experienced clinicians to validate the different swallowing patterns. We evaluate our computational pipeline on a total of 25 LTHRMs, which were meticulously annotated by medical experts. By detecting more than 94% of all relevant swallow events and providing all relevant clusters for a more reliable diagnostic process among experienced clinicians, we are able to demonstrate the effectiveness as well as positive clinical impact of our approach to make LTHRM feasible in clinical care.