S1038 Artificial Intelligence in FLIP Panometry: Multicentric Validation for Esophageal Motility Pattern Recognition

Introduction: Since its development, functional lumen imaging probe (FLIP) panometry has been considered a tool for real-time physiological assessment of the esophagogastric junction opening and esophageal body contractile activity during an endoscopic procedure. Despite its promise and the recent development of the Dallas Consensus, real-time determination of the distensibility index and evaluation of the contractile pattern lacks standardization and remains complex. Artificial intelligence models have proven their potential benefit in the analysis of motility patterns in high-resolution esophageal manometry, however their role in FLIP panometry has not been explored. This pilot study aims to develop an artificial intelligence model for automatic classification of motility patterns during a FLIP panometry exam. Methods: This multicenter study included 80 exams from 4 centers from Spain and Portugal. The full dataset included exams performed with both the 8 cm and 16 cm probe. Several machine learning models were trained and evaluated for detection of FLIP panometry patterns. Each exam was classified according to the recently published Dallas Consensus, based on a consensus reached by 2 independent experts. The training, validation and testing dataset were divided in a 70%/15%/15% patient split design. Models’ performance was evaluated through their accuracy and area under the receiver-operating characteristic curve (AUC-ROC). Results: The GradientBoostingClassifier identified disorders of the esophagogastric junction opening with an accuracy of 79.5% and AUC-ROC of 0.912. For classification of the contractile response, The GradientBoostingClassifier model outperformed the remaining machine learning models with an accuracy of 86.0% and an AUC-ROC of 0.887. Conclusion: In this pilot study with integration of exams from different probes and demographic contexts, a machine learning model accurately identified disorders of the esophagogastric junction opening and contractile response during FLIP panometry exams according to Dallas Consensus. Artificial intelligence integration into FLIP panometry studies offers the opportunity to revolutionize the approach to this exam during an endoscopic procedure, by optimizing both exam accuracy, standardization and accessibility, transforming the management of patients with esophageal disorders.