Esophageal virtual disease landscape using mechanics-informed machine learning

  title={Esophageal virtual disease landscape using mechanics-informed machine learning},
  author={Sourav Halder and Junichi Yamasaki and Shashank Acharya and Wenjun Kou and Guy Elisha and Dustin A. Carlson and Peter J. Kahrilas and John E. Pandolfino and Neelesh A. Patankar},
The pathogenesis of esophageal disorders is related to the esophageal wall mechanics. Therefore, to understand the underlying fundamental mechanisms behind various esophageal disorders, it is crucial to map the esophageal wall mechanics-based parameters onto physiological and pathophysiological conditions corresponding to altered bolus transit and supraphysiologic IBP. In this work, we present a hybrid framework that combines fluid mechanics and machine learning to identify the underlying… 



A deep-learning-based unsupervised model on esophageal manometry using variational autoencoder

Mechanics informed fluoroscopy of esophageal transport.

FluoroMech uses a convolutional neural network to perform segmentation of image sequences generated from the fluoroscopy, and the segmented images become input to a one-dimensional model that predicts the flow rate and pressure distribution in fluid transported through the esophagus.

Mechanical properties of the esophagus in eosinophilic esophagitis.

Esophageal distensibility, defined by the change in the narrowest measurable CSA within the distal esophagus vs intraluminal pressure was significantly reduced in EoE patients compared with controls.

Evaluation of Esophageal Motility Utilizing the Functional Lumen Imaging Probe

FLIP topography provides an alternative and complementary method to HRM for evaluation of non-obstructive dysphagia and may indicate otherwise undetected abnormalities of esophageal function, thus FLIP provides a well-tolerated method for esphageal motility assessment at the time of upper endoscopy.

Evaluation of esophageal motor function in clinical practice

  • C. GyawaliA. Bredenoord M. Vaezi
  • Medicine
    Neurogastroenterology and motility : the official journal of the European Gastrointestinal Motility Society
  • 2013
The clinical value of HRM extends to the pediatric population, and complements preoperative evaluation prior to foregut surgery, while emerging techniques such as 3‐D HRM and impedance planimetry show promise in the assessment of esophageal sphincter function and esophagal biomechanics.

Artificial Intelligence-Assisted Gastroenterology—Promises and Pitfalls

The recent developments in healthcare-based AI and machine learning are overviewed and promises and pitfalls for its application to gastroenterology are described.

Overview of Deep Learning in Gastrointestinal Endoscopy

The effects of artificial intelligence on gastroenterology is described with a special focus on automatic diagnosis, based on endoscopic findings, and it is essential that endoscopists focus on this novel technology.

Directional, regional, and layer variations of mechanical properties of esophageal tissue and its interpretation using a structure-based constitutive model.

In this study, the uniaxial tensile tests were conducted along two mutually orthogonal directions of porcine esophageal tissue to identify the directional, layer, and regional comparisons of the mechanical properties in terms of the associated material parameters.

The Functional Lumen Imaging Probe Detects Esophageal Contractility Not Observed With Manometry in Patients With Achalasia.

The presence and patterns of contractility detected with FLIP topography may represent variations in pathophysiology, such as mechanisms of panesophageal pressurization in patients with type II achalasia.