A physiological model for the investigation of esophageal motility in healthy and pathologic conditions

A fuzzy-based framework for diagnosing esophageal motility disorder using high-resolution manometry

In recent years, the high-resolution manometry (HRM) technique has been increasingly used to study esophageal and colonic pressurization and has become a standard routine for discovering mobility disorders. In addition to evolving guidelines for the interpretation of HRM like Chicago standard, some complexities, such as the dependency of normative reference values on the recording device and other external variables, still remain for medical professions. In this study, a decision support framework is developed to aid the diagnosis of esophageal mobility disorders based on HRM data. To abstract HRM data, Spearman correlation is employed to model the spatio-temporal dependencies of pressure values of HRM components and convolutional graph neural networks are then utilized to embed relation graphs to the features vector. In the decision-making stage, a novel Expert per Class Fuzzy Classifier (EPC-FC) is presented that employs the ensemble structure and contains expertized sub-classifiers for recognizing a specific disorder. Training sub-classifiers using the negative correlation learning method makes the EPC-FC highly generalizable. Meanwhile, separating the sub-classifiers of each class gives flexibility and interpretability to the structure. The suggested framework is evaluated on a dataset of 67 patients in 5 different classes recorded in Shariati Hospital. The average accuracy of 78.03% for a single swallow and 92.54% for subject-level is achieved for distinguishing mobility disorders. Moreover, compared with the other studies, the presented framework has an outstanding performance considering that it imposes no limits on the type of classes or HRM data. On the other hand, the EPC-FC outperforms other comparative classifiers such as SVM and AdaBoost not only in HRM diagnosis but also on other benchmark classification problems.

Deep learning for tracing esophageal motility function over time

BACKGROUND AND OBJECTIVE

Esophageal high-resolution manometry (HRM) is widely performed to evaluate the representation of manometric features in patients for diagnosing normal esophageal motility and motility disorders. Clinicians commonly assess esophageal motility function using a scheme termed the Chicago classification, which is difficult, time-consuming and inefficient with large amounts of data.

METHODS

Deep learning is a promising approach for diagnosing disorders and has various attractive advantages. In this study, we effectively trace esophageal motility function with HRM by using a deep learning computational model, namely, EMD-DL, which leverages three-dimensional convolution (Conv3D) and bidirectional convolutional long-short-term-memory (BiConvLSTM) models. More specifically, to fully exploit wet swallowing information, we establish an efficient swallowing representation method by localizing manometric features and swallowing box regressions from HRM. Then, EMD-DL learns how to identify major motility disorders, minor motility disorders and normal motility. To the best of our knowledge, this is the first attempt to use Conv3D and BiConvLSTM to predict esophageal motility function over esophageal HRM.

RESULTS

Test experiments on HRM datasets demonstrated that the overall accuracy of the proposed EMD-DL model is 91.32% with 90.5% sensitivity and 95.87% specificity. By leveraging information across swallowing motor cycles, our model can rapidly recognize esophageal motility function better than a gastroenterologist and lays the foundation for accurately diagnosing esophageal motility disorders in real time.

CONCLUSIONS

This approach opens new avenues for detecting and identifying esophageal motility function, thereby facilitating more efficient computer-aided diagnosis in clinical practice.

Utility of a new automated diagnostic program in high-resolution esophageal manometry

BACKGROUND

A new automated diagnostic program for high-resolution esophageal manometry (HREM) has been developed. This diagnostic program could detect locations of landmarks and could make final diagnoses automatically. However, the accuracy of the program is not known. The aim of this study was to evaluate the accuracy of the automated diagnostic program for HREM.

METHODS

A total of 445 studies were enrolled. An HREM system (Starlet®) was used, and esophageal motility was diagnosed using the Chicago classification v3.0. First, the locations of the upper esophageal sphincter, transition zone, lower esophageal sphincter, esophago-gastric junction, crural diaphragm and stomach were determined, and each swallow was checked manually. Then, the parameters of the Chicago classification were calculated using an analytic program of the Starlet, and diagnoses were made by three experts. Second, all study raw data were analyzed again by the automated diagnostic program. Diagnoses made by the program were compared to those made by experts to evaluate the accuracy of the diagnoses.

RESULTS

The new diagnostic program could identify the landmarks of each swallow, calculate the parameters and make a final diagnosis within 10 s. The diagnoses made by the automated diagnostic program were not matched to those made by experts in only 10 studies, and the overall accuracy of the new automated diagnostic program thus reached 97.8% (435/445).

CONCLUSIONS

The new automated diagnostic program for HREM is clinically useful in terms of high diagnostic accuracy and time-saving.

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

High-resolution manometry (HRM) is the primary method for diagnosing esophageal motility disorders and its interpretation and classification are based on variables (features) from data of each swallow. Modeling and learning the semantics directly from raw swallow data could not only help automate the feature extraction, but also alleviate the bias from pre-defined features. With more than 32-thousand raw swallow data, a generative model using the approach of variational auto-encoder (VAE) was developed, which, to our knowledge, is the first deep-learning-based unsupervised model on raw esophageal manometry data. The VAE model was reformulated to include different types of loss motivated by domain knowledge and tuned with different hyper-parameters. Training of the VAE model was found sensitive on the learning rate and hence the evidence lower bound objective (ELBO) was further scaled by the data dimension. Case studies showed that the dimensionality of latent space have a big impact on the learned semantics. In particular, cases with 4-dimensional latent variables were found to encode various physiologically meaningful contraction patterns, including strength, propagation pattern as well as sphincter relaxation. Cases with so-called hybrid L2 loss seemed to better capture the coherence of contraction/relaxation transition. Discriminating capability was further evaluated using simple linear discriminative analysis (LDA) on predicting swallow type and swallow pressurization, which yields clustering patterns consistent with clinical impression. The current work on modeling and understanding swallow-level data will guide the development of study-level models for automatic diagnosis as the next stage.

A Procedure for the Automatic Analysis of High-Resolution Manometry Data to Support the Clinical Diagnosis of Esophageal Motility Disorders

OBJECTIVE

Degenerative phenomena may affect esophageal motility as a relevant social-health problem. The diagnosis of such disorders is usually performed by the analysis of data from high-resolution manometry (HRM). Inter- and intraobserver variability frequently affects the diagnosis, with potential interpretative and thus therapeutic errors, with unnecessary or worse treatments. This may be avoided with automatic procedures that minimize human intervention in data processing.

METHODS

In order to support the traditional diagnostic process, an automatic procedure was defined considering a specific physiomechanical model that is able to objectively interpret data from HRM. A training set (N = 226) of healthy volunteers and pathological subjects was collected in order to define the model parameters distributions of the different groups of subjects, providing a preliminary database. A statistical algorithm was defined for an objective identification of the patient's healthy or pathological condition by comparing patient parameters with the database.

RESULTS

A collection of HRMs including subjects of the training set has been built. Statistical relationships between parameters and pathologies have been established leading to a preliminary database. An automatic diagnosis procedure has been developed to compare model parameters of a specific patient with the database. The procedure was able to match the correct diagnosis up to 86% of the analyzed subjects.

CONCLUSION

The success rate of the automatic procedure addresses the suitability of the developed algorithms to provide a valid support to the clinicians for the diagnostic activity.

SIGNIFICANCE

The objectivity of developed tools increases the reliability of data interpretation and, consequently, patient acceptance.