Automated Detection and Clustering of Swallowing Events in Long-Term High-Resolution Esophageal Manometry

Core Concepts

A Deep Learning-based approach for accurately detecting and clustering swallowing events in long-term high-resolution esophageal manometry data to expedite diagnosis of esophageal motility disorders.

Abstract

This paper presents a computational pipeline for automated detection and clustering of swallowing events in long-term high-resolution esophageal manometry (LTHRM) data. LTHRM is used to gain detailed insights into the swallowing behavior, but analyzing the extensive data is challenging and time-consuming for medical experts.
The proposed approach consists of two main steps:

Swallow Detection:

A Deep Learning-based method using Convolutional Neural Networks (CNNs) is developed to accurately identify swallowing events in LTHRM data.
The CNN-based approach outperforms both a non-ML baseline and a commercial LTHRM evaluation tool, achieving an average Recall score of 94% for swallow detection.

Swallow Clustering:

The detected swallows are categorized into distinct classes using a robust clustering approach.
This reduces the manual evaluation time for clinicians by presenting only a few representative images of each swallow cluster for further diagnosis.

The method is evaluated on a dataset of 25 LTHRM recordings that were meticulously annotated by medical experts. The resulting clusters are analyzed by experienced clinicians to validate their clinical value.
The proposed computational pipeline demonstrates the effectiveness and positive clinical impact of automating the analysis of LTHRM data, making it feasible for use in clinical care and expediting the diagnostic process for esophageal motility disorders.

Stats

LTHRM data collected from 25 patients with suspected intermittently occurring esophageal motility disorders
Over 25,000 manually labeled swallow events in the dataset

Quotes

"To address these limitations, we have extended HRM monitoring to a long-term setting (up to 24 hours) in a previous work [6]. However, this expansion presents new challenges, as the increased data volume necessitates sophisticated methods for analysis and interpretation."
"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."

Key Insights Distilled From

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

by Alexander Ge... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01126.pdf

Deeper Inquiries

How could the proposed approach be extended to detect and classify other esophageal motility disorders beyond swallowing events

The proposed approach can be extended to detect and classify other esophageal motility disorders beyond swallowing events by incorporating additional features and training the Deep Learning models on a more diverse dataset. For example, features related to peristalsis patterns, sphincter function, and pressure variations in the esophagus can be included to capture a broader range of motility disorders. By collecting data from patients with various esophageal conditions such as achalasia, esophageal spasms, and reflux disorders, the models can be trained to recognize distinct patterns associated with each disorder. This would involve annotating the data with the specific motility disorder present in each case to enable the models to learn the unique characteristics of each condition. Additionally, leveraging transfer learning techniques by fine-tuning pre-trained models on esophageal motility data can help improve the classification performance for different disorders.

What are the potential limitations or challenges in applying this Deep Learning-based method to real-world clinical settings with diverse patient populations and data quality

Applying this Deep Learning-based method to real-world clinical settings with diverse patient populations and data quality may face several limitations and challenges. One potential limitation is the generalizability of the models across different patient demographics, as variations in age, gender, and underlying health conditions can impact the esophageal motility patterns. Ensuring the robustness and reliability of the models across diverse populations would require a large and representative dataset for training and validation. Moreover, the quality of the input data, including noise, artifacts, and missing values, can affect the performance of the models. Preprocessing steps to handle data quality issues and artifacts would be crucial to ensure accurate detection and classification of motility disorders. Additionally, the interpretability of the Deep Learning models in clinical settings is essential for gaining trust from healthcare professionals. Providing explanations for the model predictions and incorporating clinician feedback into the model development process can help address this challenge.

How could the insights gained from the identified swallow event clusters be further leveraged to develop personalized treatment strategies for patients with esophageal motility disorders

The insights gained from the identified swallow event clusters can be leveraged to develop personalized treatment strategies for patients with esophageal motility disorders by tailoring interventions based on the specific patterns and characteristics of each patient's condition. By categorizing swallow events into distinct clusters, clinicians can identify commonalities and differences in motility patterns among patients. This information can guide the selection of appropriate treatment options, such as dietary modifications, lifestyle changes, medication, or surgical interventions. Furthermore, by analyzing the response of patients with similar swallow event clusters to different treatment modalities, personalized treatment plans can be optimized for better outcomes. Integrating patient-specific data, such as symptom severity, comorbidities, and treatment history, with the swallow event clusters can enable clinicians to make informed decisions and adjust treatment strategies based on individual patient needs and responses. This personalized approach can lead to more effective and tailored care for patients with esophageal motility disorders.