Artificial Intelligence Driven Diagnosis of Motility Patterns in High-Resolution Esophageal Manometry: A Multicentric...

Esophageal motility disorders (EMDs) are common in clinical practice, with a high symptomatic burden and significant impact on the patients' quality of life. High-resolution esophageal manometry (HREM) is the gold standard for the evaluation of functional esophageal disorders. The Chicago Classification offers a standardized approach to HREM. However, HREM remains a complex procedure, both in data analysis and in accessibility. This study aimed to develop and validate machine learning (ML) models to detect EMDs according to the Chicago Classification.

We retrospectively analyzed 618 HREM examinations from 3 centers (Spain and the United States) using 2 recording systems. Labels were assigned by expert consensus as either disorder present or absent for 2 categories: esophagogastric junction outflow disorders and peristalsis disorders. Several ML models were trained and evaluated. ML classifiers were developed using an 80/20 patient-level stratified split for training/validation and testing. Model selection was guided by internal evaluation through repeated 10-fold cross-validation. Model performance was assessed by accuracy and area under the receiver-operating characteristic curve (AUC-ROC).

The GradientBoostingClassifier model outperformed the remaining ML models with an accuracy of 0.942 ± 0.015 and an AUC-ROC of 0.921 ± 0.041 for identifying disorders of esophagogastric junction outflow. The xGBClassifier model detected disorders of peristalsis with an accuracy of 0.809 ± 0.029 and an AUC-ROC of 0.871 ± 0.027. Performance was consistent across repeated validations, demonstrating model robustness and generalization.

This multicenter, multidevice study demonstrates that ML models can accurately detect EMDs in HREM. Artificial intelligence-driven HREM may improve diagnosis by standardizing interpretation and reducing interobserver variability.