Estimation of mechanical work done to open the esophagogastric junction using functional lumen imaging probe panometry

Enhancing Chicago Classification diagnoses with functional lumen imaging probe-mechanics (FLIP-MECH)

BACKGROUND

Esophageal motility disorders can be diagnosed by either high-resolution manometry (HRM) or the functional lumen imaging probe (FLIP) but there is no systematic approach to synergize the measurements of these modalities or to improve the diagnostic metrics that have been developed to analyze them. This work aimed to devise a formal approach to bridge the gap between diagnoses inferred from HRM and FLIP measurements using deep learning and mechanics.

METHODS

The "mechanical health" of the esophagus was analyzed in 740 subjects including a spectrum of motility disorder patients and normal subjects. The mechanical health was quantified through a set of parameters including wall stiffness, active relaxation, and contraction pattern. These parameters were used by a variational autoencoder to generate a parameter space called virtual disease landscape (VDL). Finally, probabilities were assigned to each point (subject) on the VDL through linear discriminant analysis (LDA), which in turn was used to compare with FLIP and HRM diagnoses.

RESULTS

Subjects clustered into different regions of the VDL with their location relative to each other (and normal) defined by the type and severity of dysfunction. The two major categories that separated best on the VDL were subjects with normal esophagogastric junction (EGJ) opening and those with EGJ obstruction. Both HRM and FLIP diagnoses correlated well within these two groups.

CONCLUSION

Mechanics-based parameters effectively estimated esophageal health using FLIP measurements to position subjects in a 3-D VDL that segregated subjects in good alignment with motility diagnoses gleaned from HRM and FLIP studies.

Assessing mechanical function of peristalsis with functional lumen imaging probe panometry: Contraction power and displaced volume

BACKGROUND AND AIMS

The distal contractile integral (DCI) quantifies the contractile vigor of primary peristalsis on high-resolution manometry (HRM), whereas no such metric exists for secondary peristalsis on functional lumen imaging probe (FLIP) panometry. This study aimed to evaluate novel FLIP metrics of contraction power and displaced volume in asymptomatic controls and a patient cohort.

METHODS

Thirty-five asymptomatic controls and adult patients (with normal esophagogastric junction outflow/opening and without spasm) who completed HRM and FLIP panometry were included. The patient group also completed timed barium esophagram (TBE). Contraction power (estimate of esophageal work over time) and displaced volume (estimate of contraction-associated fluid flow) were computed from FLIP. HRM was analyzed per Chicago Classification v4.0.

KEY RESULTS

In controls, median (5th-95th percentile) contraction power was 27 mW (10-44) and displaced volume was 43 mL (17-66). 95 patients were included: 72% with normal motility on HRM, 17% with ineffective esophageal motility (IEM), and 12% with absent contractility. Among patients, DCI was significantly correlated with both contraction power (rho = 0.499) and displaced volume (rho = 0.342); p values < 0.001. Both contraction power and displaced volume were greater in patients with normal motility versus IEM or absent contractility, complete versus incomplete bolus transit, and normal versus abnormal retention on TBE; p values < 0.02.

CONCLUSIONS

FLIP panometry metrics of contraction power and displaced volume appeared to effectively quantify peristaltic vigor. These novel metrics may enhance evaluation of esophageal motility with FLIP panometry and provide a reliable surrogate to DCI on HRM.

MRI-MECH: mechanics-informed MRI to estimate esophageal health

Dynamic magnetic resonance imaging (MRI) is a popular medical imaging technique that generates image sequences of the flow of a contrast material inside tissues and organs. However, its application to imaging bolus movement through the esophagus has only been demonstrated in few feasibility studies and is relatively unexplored. In this work, we present a computational framework called mechanics-informed MRI (MRI-MECH) that enhances that capability, thereby increasing the applicability of dynamic MRI for diagnosing esophageal disorders. Pineapple juice was used as the swallowed contrast material for the dynamic MRI, and the MRI image sequence was used as input to the MRI-MECH. The MRI-MECH modeled the esophagus as a flexible one-dimensional tube, and the elastic tube walls followed a linear tube law. Flow through the esophagus was governed by one-dimensional mass and momentum conservation equations. These equations were solved using a physics-informed neural network. The physics-informed neural network minimized the difference between the measurements from the MRI and model predictions and ensured that the physics of the fluid flow problem was always followed. MRI-MECH calculated the fluid velocity and pressure during esophageal transit and estimated the mechanical health of the esophagus by calculating wall stiffness and active relaxation. Additionally, MRI-MECH predicted missing information about the lower esophageal sphincter during the emptying process, demonstrating its applicability to scenarios with missing data or poor image resolution. In addition to potentially improving clinical decisions based on quantitative estimates of the mechanical health of the esophagus, MRI-MECH can also be adapted for application to other medical imaging modalities to enhance their functionality.

A mechanics-based perspective on the pressure-cross-sectional area loop within the esophageal body

Introduction: Plotting the pressure-cross-sectional area (P-CSA) hysteresis loops within the esophagus during a contraction cycle can provide mechanistic insights into esophageal motor function. Pressure and cross-sectional area during secondary peristalsis can be obtained from the functional lumen imaging probe (FLIP). The pressure-cross-sectional area plots at a location within the esophageal body (but away from the sphincter) reveal a horizontal loop shape. The horizontal loop shape has phases that appear similar to those in cardiovascular analyses, whichinclude isometric and isotonic contractions followed by isometric and isotonic relaxations. The aim of this study is to explain the various phases of the pressurecross-sectional area hysteresis loops within the esophageal body. Materials and Methods: We simulate flow inside a FLIP device placed inside the esophagus lumen. We focus on three scenarios: long functional lumen imaging probe bag placed insidethe esophagus but not passing through the lower esophageal sphincter, long functional lumen imaging probe bag that crosses the lower esophageal sphincter, and a short functional lumen imaging probe bag placed in the esophagus body that does not pass through the lower esophageal sphincter. Results and Discussion: Horizontal P-CSA area loop pattern is robust and is reproduced in all three cases with only small differences. The results indicate that the horizontal loop pattern is primarily a product of mechanical conditions rather than any inherently different function of the muscle itself. Thus, the distinct phases of the loop can be explained solely based on mechanics.

Virtual disease landscape using mechanics-informed machine learning: Application to esophageal disorders

Esophageal disorders are related to the mechanical properties and function of the esophageal wall. Therefore, to understand the underlying fundamental mechanisms behind various esophageal disorders, it is crucial to map mechanical behavior of the esophageal wall in terms of mechanics-based parameters corresponding to altered bolus transit and increased intrabolus pressure. We present a hybrid framework that combines fluid mechanics and machine learning to identify the underlying physics of various esophageal disorders (motility disorders, eosinophilic esophagitis, reflux disease, scleroderma esophagus) and maps them onto a parameter space which we call the virtual disease landscape (VDL). A one-dimensional inverse model processes the output from an esophageal diagnostic device called the functional lumen imaging probe (FLIP) to estimate the mechanical "health" of the esophagus by predicting a set of mechanics-based parameters such as esophageal wall stiffness, muscle contraction pattern and active relaxation of esophageal wall. The mechanics-based parameters were then used to train a neural network that consists of a variational autoencoder that generated a latent space and a side network that predicted mechanical work metrics for estimating esophagogastric junction motility. The latent vectors along with a set of discrete mechanics-based parameters define the VDL and formed clusters corresponding to specific esophageal disorders. The VDL not only distinguishes among disorders but also displayed disease progression over time. Finally, we demonstrated the clinical applicability of this framework for estimating the effectiveness of a treatment and tracking patients' condition after a treatment.