Detecting and clustering swallow events in esophageal long-term high-resolution manometry
CoRR(2024)
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
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.
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