Analysis of Regional Variations of the Interstitial Cells of Cajal in the Murine Distal Stomach Informed by Confocal Imaging and Machine Learning Methods

Artificial Intelligence Tools for Improving Manometric Diagnosis of Esophageal Dysmotility

PURPOSE OF REVIEW

Artificial intelligence (AI) is a broad term that pertains to a computer's ability to mimic and sometimes surpass human intelligence in interpretation of large datasets. The adoption of AI in gastrointestinal motility has been slower compared to other areas such as polyp detection and interpretation of histopathology.

RECENT FINDINGS

Within esophageal physiologic testing, AI can automate interpretation of image-based tests, especially high resolution manometry (HRM) and functional luminal imaging probe (FLIP) studies. Basic tasks such as identification of landmarks, determining adequacy of the HRM study and identification from achalasia from non-achalasia patterns are achieved with good accuracy. However, existing AI systems compare AI interpretation to expert analysis rather than to clinical outcome from management based on AI diagnosis. The use of AI methods is much less advanced within the field of ambulatory reflux monitoring, where challenges exist in assimilation of data from multiple impedance and pH channels. There remains potential for replication of the AI successes within esophageal physiologic testing to HRM of the anorectum, and to innovative and novel methods of evaluating gastric electrical activity and motor function. The use of AI has tremendous potential to improve detection of dysmotility within the esophagus using esophageal physiologic testing, as well as in other regions of the gastrointestinal tract. Eventually, integration of patient presentation, demographics and alternate test results to individual motility test interpretation will improve diagnostic precision and prognostication using AI tools.

Three-Dimensional Fractal Analysis of the Interstitial Cells of Cajal Networks of Gastrointestinal Tissue Specimens

Introduction

Several functional gastrointestinal disorders (FGIDs) have been associated with the degradation or remodeling of the network of interstitial cells of Cajal (ICC). Introducing fractal analysis to the field of gastroenterology as a promising data analytics approach to extract key structural characteristics that may provide insightful features for machine learning applications in disease diagnostics. Fractal geometry has advantages over several physically based parameters (or classical metrics) for analysis of intricate and complex microstructures that could be applied to ICC networks.

Methods

In this study, three fractal structural parameters: Fractal Dimension, Lacunarity, and Succolarity were employed to characterize scale-invariant complexity, heterogeneity, and anisotropy; respectively of three types of gastric ICC network structures from a flat-mount transgenic mouse stomach.

Results

The Fractal Dimension of ICC in the longitudinal muscle layer was found to be significantly lower than ICC in the myenteric plexus and circumferential muscle in the proximal, and distal antrum, respectively (both p < 0.0001). Conversely, the Lacunarity parameters for ICC-LM and ICC-CM were found to be significantly higher than ICC-MP in the proximal and in the distal antrum, respectively (both p < 0.0001). The Succolarity measures of ICC-LM network in the aboral direction were found to be consistently higher in the proximal than in the distal antrum (p < 0.05).

Conclusions

The fractal parameters presented here could go beyond the limitation of classical metrics to provide better understanding of the structural-functional relationship between ICC networks and the conduction of gastric bioelectrical slow waves.

Neural regulation of slow waves and phasic contractions in the distal stomach: a mathematical model

Objective.Neural regulation of gastric motility occurs partly through the regulation of gastric bioelectrical slow waves (SWs) and phasic contractions. The interaction of the tissues and organs involved in this regulatory process is complex. We sought to infer the relative importance of cellular mechanisms in inhibitory neural regulation of the stomach by enteric neurons and the interaction of inhibitory and excitatory electrical field stimulation.Approach.A novel mathematical model of gastric motility regulation by enteric neurons was developed and scenarios were simulated to determine the mechanisms through which enteric neural influence is exerted. This model was coupled to revised and extended electrophysiological models of gastric SWs and smooth muscle cells (SMCs).Main results.The mathematical model predicted that regulation of contractile apparatus sensitivity to intracellular calcium in the SMC was the major inhibition mechanism of active tension development, and that the effect on SW amplitude depended on the inhibition of non-specific cation currents more than the inhibition of calcium-activated chloride current (kiNSCC= 0.77 vs kiAno1= 0.33). The model predicted that the interaction between inhibitory and excitatory neural regulation, when applied with simultaneous and equal intensity, resulted in an inhibition of contraction amplitude almost equivalent to that of inhibitory stimulation (79% vs 77% decrease), while the effect on frequency was overall excitatory, though less than excitatory stimulation alone (66% vs 47% increase).Significance.The mathematical model predicts the effects of inhibitory and excitatory enteric neural stimulation on gastric motility function, as well as the effects when inhibitory and excitatory enteric neural stimulation interact. Incorporation of the model into organ-level simulations will provide insights regarding pathological mechanisms that underpin gastric functional disorders, and allow forin silicotesting of the effects of clinical neuromodulation protocols for the treatment of these disorders.

Functional and anatomical gastric regions and their relations to motility control

The common occurrence of gastric disorders, the accelerating emphasis on the role of the gut-brain axis, and development of realistic, predictive models of gastric function, all place emphasis on increasing understanding of the stomach and its control. However, the ways that regions of the stomach have been described anatomically, physiologically, and histologically do not align well. Mammalian single compartment stomachs can be considered as having four anatomical regions fundus, corpus, antrum, and pyloric sphincter. Functional regions are the proximal stomach, primarily concerned with adjusting gastric volume, the distal stomach, primarily involved in churning and propelling the content, and the pyloric sphincter that regulates passage of chyme into the duodenum. The proximal stomach extends from the dome of the fundus to a circumferential band where propulsive waves commence (slow waves of the pacemaker region), and the distal stomach consists of the pacemaker region and the more distal regions that are traversed by waves of excitation, that travel as far as the pyloric sphincter. Thus, the proximal stomach includes the fundus and different extents of the corpus, whereas the distal stomach consists of the remainder of the corpus and the antrum. The distributions of aglandular regions and of specialized glands, such as oxyntic glands, differ vastly between species and, across species, have little or no relation to anatomical or functional regions. It is hoped that this review helps to clarify nomenclature that defines gastric regions that will provide an improved basis for drawing conclusions for different investigations of the stomach.

Fractal nature of human gastrointestinal system: Exploring a new era

The morphological complexity of cells and tissues, whether normal or pathological, is characterized by two primary attributes: Irregularity and self-similarity across different scales. When an object exhibits self-similarity, its shape remains unchanged as the scales of measurement vary because any part of it resembles the whole. On the other hand, the size and geometric characteristics of an irregular object vary as the resolution increases, revealing more intricate details. Despite numerous attempts, a reliable and accurate method for quantifying the morphological features of gastrointestinal organs, tissues, cells, their dynamic changes, and pathological disorders has not yet been established. However, fractal geometry, which studies shapes and patterns that exhibit self-similarity, holds promise in providing a quantitative measure of the irregularly shaped morphologies and their underlying self-similar temporal behaviors. In this context, we explore the fractal nature of the gastrointestinal system and the potential of fractal geometry as a robust descriptor of its complex forms and functions. Additionally, we examine the practical applications of fractal geometry in clinical gastroenterology and hepatology practice.

Deciphering Stomach Myoelectrical Slow Wave Conduction Patterns via Confocal Imaging of Gastric Pacemaker Cells and Fractal Geometry

Interstitial Cells of Cajal (ICC) are specialized gastrointestinal (GI) pacemaker cells that generate and actively propagate slow waves of depolarization (SWs) of the muscularis propria. SWs regulate the motility of the GI tract necessary for digestion, absorption of nutrients, and elimination of waste. Within the gastric wall, there are three main inter-connected layers of ICC networks: longitudinal muscle ICC (ICC-LM), myenteric plexus ICC (ICC-MP) & circumferential muscle (ICC-CM). Fractal structural parameters such as Fractal Dimension (FD), Lacunarity and Succolarity, have many advantages over physically-based parameters when it comes to characterizing the complex architectures of ICC networks. The analysis of networks of ICC throughout the proximal and distal murine gastric antrum with the FD and Lacunarity metrics was previously performed. Although the application of Succolarity is relatively nascent compared to the FD and Lacunarity; nevertheless, numerous studies have demonstrated the capability of this fractal measure to extract information from images associated with flow by which neither the FD nor Lacunarity are capable of discerning. In this study, Succolarity analysis of ICC-MP and ICC-CM networks were performed with confocal images taken across the proximal and distal murine antrum. Our findings demonstrated the Succolarity of ICC-MP and ICC-CM varied with directions and antral regions. The Succolarity of ICC-MP did not vary considerably with direction, however, Succolarity was higher in the aboral direction with 0.2113 ±0.1589, and 0.0637 ±0.0822 in the proximal and distal antrum, respectively. The overall Succolarity of ICC-MP was significantly higher than that of ICC-CM in the proximal antrum ( 0.1580±0.1325 vs [Formula: see text]) and in the distal antrum ( 0.0449 ±0.0409 vs [Formula: see text]). Clinical Relevance-Modeling SWs conduction patterns via image analysis of detailed ICC networks help to facilitate an improved understanding of the mechanisms underpinning GI myoelectric activity and the diseases associated with its dysfunction.