Deep learning–based artificial intelligence model for identifying swallow types in esophageal high‐resolution manometry

Abstract

Background This study aimed to build and evaluate a deep learning, artificial intelligence (AI) model to automatically classify swallow types based on raw data from esophageal high‐resolution manometry (HRM). Methods HRM studies on patients with no history of esophageal surgery were collected including 1,741 studies with 26,115 swallows labeled by swallow type (normal, hypercontractile, weak‐fragmented, failed, and premature) by an expert interpreter per the Chicago Classification. The dataset was stratified and split into train/validation/test datasets for model development. Long short‐term memory (LSTM), a type of deep‐learning AI model, was trained and evaluated. The overall performance and detailed per‐swallow type performance were analyzed. The interpretations of the supine swallows in a single study were further used to generate an overall classification of peristalsis. Key Results The LSTM model for swallow type yielded accuracies from the train/validation/test datasets of 0.86/0.81/0.83. The model's interpretation for study‐level classification of peristalsis yielded accuracy of 0.88 in the test dataset. Among model misclassification, 535/698 (77%) swallows and 25/35 (71%) studies were to adjacent categories, for example, normal to weak or normal to ineffective, respectively. Conclusions and Inferences A deep‐learning AI model can automatically and accurately identify the Chicago Classification swallow types and peristalsis classification from raw HRM data. While future work to refine this model and incorporate overall manometric diagnoses are needed, this study demonstrates the role that AI will serve in the interpretation and classification of esophageal HRM studies.

Automated Chicago Classification for Esophageal Motility Disorder Diagnosis Using Machine Learning

“…There is limited research [ 3 , 4 , 5 , 19 , 20 , 21 , 22 , 23 , 24 ] that explored automated diagnosis of EMDs and pharingeal swallows utilizing AI-based methods or automation of the Chicago Classification system, Table 1 . In addition, the most relevant research in this sector is discussed below.…”

“…Another study performed by Kou et al [ 4 , 5 ] on automated detection of EMDs using raw multi-swallow pictures collected from esophageal HRM, showed good accuracy by using machine learning techniques and deep-learning models with a dataset of 1741 patients.…”

“…Unfortunately, although AI adoption is continuing its steady rise, the applications in esophageal manometry are limited. Only a few studies exist [ 3 , 4 , 5 ], and thus far, these applications are not used in a clinical setting.…”

“…In article [ 5 ] we can see a different approach because the authors used Long-Short-Term-Memory (LSTM) instead of CNN models to classify the swallow types automatically. The accuracy of 83% obtained with this model is lower than in the case of solutions using CNNs, which can be explained by the nature of the input dataset and the LSTM, which works well mostly with time series.…”

An artificial intelligence platform provides an accurate interpretation of esophageal motility from Functional Lumen Imaging Probe Panometry studies

“…The supervised, deep-learning model demonstrated herein utilized convolutional neural networks and careful parameter selection of the convolutional layers to inspect the FLIP studies in a manner somewhat similar to a clinician. While this study is the first to develop and test an AI model for FLIP Panometry motility interpretation, we have recently described using other AI approaches for HRM interpretation 9,18. Similar to using deep-learning algorithms to interpret the graphical depiction of esophageal motility provided by esophageal pressure topography, the models described interpreted esophageal motility from esophageal diameter topography (and pressure) from FLIP Panometry data.…”