arXiv subjects

Dustin A. Carlson

Publications and source records attributed to Dustin A. Carlson.

7 recordsLinked to original sources

arXiv2024

Neurological disorders leading to mechanical dysfunction of the esophagus: an emergent behavior of a neuromechanical dynamical system

An understanding how neurological disorders lead to mechanical dysfunction of the esophagus requires knowledge of the neural circuit of the enteric nervous system. Historically, this has been elusive. Here, we present an empirically guided neural circuit for the esophagus. It has a chain of unidirectionally coupled relaxation oscillators, receiving excitatory signals from stretch receptors along the esophagus. The resulting neuromechanical model reveals complex patterns and behaviors that emerge from interacting components in the system. A wide variety of clinically observed normal and abnormal esophageal responses to distension are successfully predicted. Specifically, repetitive antegrade contractions (RACs) are conclusively shown to emerge from the coupled neuromechanical dynamics in response to sustained volumetric distension. Normal RACs are shown to have a robust balance between excitatory and inhibitory neuronal populations, and the mechanical input through stretch receptors. When this balance is affected, contraction patterns akin to motility disorders are observed. For example, clinically observed repetitive retrograde contractions emerge due to a hyper stretch sensitive wall. Such neuromechanical insights could be crucial to eventually develop targeted pharmacological interventions.

q-bio.NC↗

arXiv2022

A mechanics--based perspective on the function of human sphincters during functional luminal imaging probe manometry

Functional luminal imaging probe (FLIP) is used to measure cross-sectional area (CSA) and pressure at sphincters. It consists of a catheter surrounded by a fluid filled cylindrical bag, closed on both ends. Plotting the pressure-CSA hysteresis of a sphincter during a contraction cycle, which is available through FLIP testing, offers information on its functionality, and can provide diagnostic insights. However, limited work has been done to explain the mechanics of these pressure-CSA loops. This work presents a consolidated picture of pressure-CSA loops of different sphincters. Clinical data reveal that although sphincters have a similar purpose (controlling the flow of liquids and solids by opening and closing), two different pressure-CSA loop patterns emerge: negative slope loop (NSL) and positive slope loop (PSL). We show that the loop type is the result of an interplay between (or lack thereof) two mechanical modes: (i) neurogenic mediated relaxation of the sphincter muscle and (ii) muscle contraction proximal to the sphincter which causes mechanical distention. We conclude that sphincters which only function through mechanism (i) exhibition NSL whereas sphincters which open as a result of both (i) and (ii) display a PSL. This work provides a fundamental mechanical understanding of human sphincters. This can be used to identify normal and abnormal phenotypes for the different sphincters and help in creating physiomarkers based on work calculation.

physics.med-ph↗

arXiv2022

A multi-stage machine learning model on diagnosis of esophageal manometry

High-resolution manometry (HRM) is the primary procedure used to diagnose esophageal motility disorders. Its interpretation and classification includes an initial evaluation of swallow-level outcomes and then derivation of a study-level diagnosis based on Chicago Classification (CC), using a tree-like algorithm. This diagnostic approach on motility disordered using HRM was mirrored using a multi-stage modeling framework developed using a combination of various machine learning approaches. Specifically, the framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. In the swallow-level stage, three models based on convolutional neural networks (CNNs) were developed to predict swallow type, swallow pressurization, and integrated relaxation pressure (IRP). At the study-level stage, model selection from families of the expert-knowledge-based rule models, xgboost models and artificial neural network(ANN) models were conducted, with the latter two model designed and augmented with motivation from the export knowledge. A simple model-agnostic strategy of model balancing motivated by Bayesian principles was utilized, which gave rise to model averaging weighted by precision scores. The averaged (blended) models and individual models were compared and evaluated, of which the best performance on test dataset is 0.81 in top-1 prediction, 0.92 in top-2 predictions. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data. Moreover, the proposed modeling framework could be easily extended to multi-modal tasks, such as diagnosis of esophageal patients based on clinical data from both HRM and functional luminal imaging probe panometry (FLIP).

cs.LG↗

arXiv2022

Pressure -- area loop based phenotypic classification and mechanics of esophagogastric junction physiology

The esophagogastric junction (EGJ) is located at the distal end of the esophagus and acts as a valve allowing swallowed materials to enter the stomach and preventing acid reflux. Irregular weakening or stiffening of the EGJ muscles result in changes to its opening and closing patterns which can progress into esophageal disorders. Therefore, understanding the physics behind the opening and closing cycle of the EGJ provides a mechanistic insight into its function and can help identify the underlying conditions that cause its degradation. Using clinical FLIP data, we plotted the pressure-area hysteresis at the EGJ location and distinguished two major loop types, a pressure dominant loop (PDL) and a tone dominant loop (TDL). In this study, we aimed to identify the key characteristics that define each loop type and find what causes the inversion from one loop to another. To do so, the clinical observations were reproduced using 1D simulations of flow inside a FLIP device located in the esophagus, and the work done by the EGJ wall over time was calculated. This work was decomposed into active and passive components, which revealed the competing mechanisms that dictate the loop type. These mechanisms are esophagus stiffness, fluid viscosity, and the EGJ relaxation pattern. In PDL, the leading source of energy in the cycle is coming from the fluid pressure increase from the peristaltic contraction wave, and in TDL the leading source of energy in the cycle is coming from the contraction and relaxation of the EGJ tone.

physics.bio-ph↗

arXiv2021

Peristaltic regimes in esophageal transport

A FLIP device gives cross-sectional area along the length of the esophagus and one pressure measurement, both as a function of time. Deducing mechanical properties of the esophagus including wall material properties, contraction strength, and wall relaxation from these data is a challenging inverse problem. Knowing mechanical properties can change how clinical decisions are made because of its potential for in-vivo mechanistic insights. To obtain such information, we conducted a parametric study to identify peristaltic regimes by using a 1D model of peristaltic flow through an elastic tube closed on both ends and also applied it to interpret clinical data. The results gave insightful information about the effect of tube stiffness, fluid/bolus density and contraction strength on the resulting esophagus shape through quantitive representations of the peristaltic regimes. Our analysis also revealed the mechanics of the opening of the contraction area as a function of bolus flow resistance. Lastly, we concluded that peristaltic driven flow displays three modes of peristaltic geometries, but all physiologically relevant flows fall into two peristaltic regimes characterized by a tight contraction.

physics.flu-dyn↗

arXiv2021

Esophageal virtual disease landscape using mechanics-informed machine learning

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 physics of the various esophageal disorders 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 endoscopic functional lumen imaging probe (EndoFLIP) 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 walls. The mechanics-based parameters were then used to train a neural network that consists of a variational autoencoder (VAE) that generates a latent space and a side network that predicts mechanical work metrics for estimating esophagogastric junction motility. The latent vectors along with a set of discrete mechanics-based parameters define the VDL and form clusters corresponding to the various esophageal disorders. The VDL not only distinguishes different disorders but can also be used to predict disease progression in time. Finally, we also demonstrate the clinical applicability of this framework for estimating the effectiveness of a treatment and track patient condition after a treatment.

cs.LG↗

arXiv2021

Pumping Patterns and Work Done during Peristalsis in Finite-length Elastic Tubes

Balloon dilation catheters are often used to quantify the physiological state of peristaltic activity in tubular organs and comment on their ability to propel fluid which is important for healthy human function. To fully understand this system's behavior, we analyzed the effect of a solitary peristaltic wave on a fluid-filled elastic tube with closed ends. A reduced order model that predicts the resulting tube wall deformations, flow velocities and pressure variations is presented. This simplified model is compared with detailed fluid-structure 3D immersed boundary simulations of peristaltic pumping in tube walls made of hyperelastic material. The major dynamics observed in the 3D simulations were also displayed by our 1D model under laminar flow conditions. Using the 1D model, several pumping regimes were investigated and presented in the form of a regime map that summarizes the system's response for a range of physiological conditions. Finally, the amount of work done during a peristaltic event in this configuration was defined and quantified. The variation of elastic energy and work done during pumping was found to have a unique signature for each regime. An extension of the 1D model is applied to enhance patient data collected by the device and find the work done for a typical esophageal peristaltic wave. This detailed characterization of the system's behavior aids in better interpreting the clinical data obtained from dilation catheters. Additionally, the pumping capacity of the esophagus can be quantified for comparative studies between disease groups.

physics.flu-dyn↗