Enhancing the diagnostic yield of esophageal manometry using distension-contraction plots of peristalsis and artificial intelligence

Abstract

Our findings underscore the superiority of distension features over contraction metrics in diagnosing esophageal dysfunctions. By leveraging state-of-the-art machine learning techniques, our study highlights the diagnostic potential of distension-contraction plots of peristalsis. Implementation of these plots could significantly enhance the accuracy of identifying patients with esophageal motor disorders, advocating for their adoption as the standard in clinical practice.