Artificial intelligence automates and augments baseline impedance measurements from pH-impedance studies in gastroesophageal reflux disease

Automated software-derived supine baseline impedance is highly correlated with manual nocturnal baseline impedance for the diagnosis of GERD

BACKGROUND

Mean nocturnal baseline impedance (MNBI) can improve diagnostic accuracy for gastroesophageal reflux disease (GERD), but must be manually calculated and is not routinely reported. We aimed to determine how automated software-derived mean supine baseline impedance (MSBI), a potential novel GERD metric, is related to MNBI.

METHODS

Consecutively obtained pH-impedance studies were assessed. Manually extracted MNBI was compared to MSBI using paired t-test and Spearman's correlations.

KEY RESULTS

The correlation between MNBI and MSBI was very high (ρ = 0.966, p < 0.01).

CONCLUSIONS & INFERENCES

The ease of acquisition and correlation with MNBI warrant the routine clinical use and reporting of MSBI with pH-impedance studies.

Mucosal impedance as a diagnostic tool for gastroesophageal reflux disease: an update for clinicians

Mucosal impedance is a marker of esophageal mucosal integrity and a novel technique for assessing esophageal function and pathology. This article highlights its development and clinical application for gastroesophageal reflux disease (GERD), Barrett's esophagus, and eosinophilic esophagitis. A narrative review of key publications describing the development and use of mucosal impedance in clinical practice was conducted. A low mean nocturnal baseline impedance (MNBI) has been shown to be an independent predictor of response to anti-reflux therapy. MNBI predicts medication-responsive heartburn better than distal esophageal acid exposure time. Patients with equivocal evidence of GERD using conventional methods, with a low MNBI, had an improvement in symptoms following the initiation of PPI therapy compared to those with a normal MNBI. A similar trend was seen in a post fundoplication cohort. Strong clinical utility for the use of mucosal impedance in assessing eosinophilic esophagitis has been repeatedly demonstrated; however, there is minimal direction for application in Barrett's esophagus. The authors conclude that mucosal impedance has potential clinical utility for the assessment and diagnosis of GERD, particularly when conventional investigations have yielded equivocal results.

Utility of the Post-Reflux Swallow-Induced Peristaltic Wave Index and Mean Nocturnal Baseline Impedance for the Diagnosis of Gastroesophageal Reflux Disease Phenotypes in Children

(1) Objectives: Assessment of novel impedance parameters such as the post-reflux swallow-induced peristaltic wave (PSPW) index and mean nocturnal baseline impedance (MNBI) have been proposed to enhance the accuracy of gastroesophageal reflux disease (GERD) diagnosis. We aimed to evaluate the clinical value of MNBI and the PSPW index in discerning different phenotypes of GERD in children. (2) Methods: We conducted a prospective, observational study that included 49 children aged 5-18 years, referred for MII-pH monitoring due to negative endoscopy and persisting gastroesophageal reflux symptoms despite acid-suppressant treatment. The PSPW index and MNBI were assessed along with conventional metrics. (3) Results: Using a receiver operating characteristic (ROC) curve analysis, MNBI (AUC 0.864) and the PSPW index (AUC 0.83) had very good performance in differentiating between non-erosive reflux disease (NERD) and functional phenotypes. The PSPW index (AUC 0.87) discriminated better between functional heartburn (FH) and reflux hypersensitivity (RH) compared to the MNBI (AUC 0.712). A PSPW cut-off value of 65% provided a sensitivity of 76.9% and a specificity of 90% in distinguishing FH and RH. The PSPW index (AUC 0.87) proved to have better performance than the MNBI (AUC 0.802) in differentiating between FH and non-FH patients. MNBI diagnosed FH with a sensitivity of 84% and a specificity of 80.6% at a cut-off value of 2563 Ω. (4) Conclusions: The PSPW index and MNBI are useful to distinguish between GERD phenotypes in pediatric patients.

Role of Artificial Intelligence in the Diagnosis of Gastroesophageal Reflux Disease

Gastroesophageal reflux disease (GERD) is a disorder that usually presents with heartburn. GERD is diagnosed clinically, but most patients are misdiagnosed due to atypical presentations. The increased use of artificial intelligence (AI) in healthcare has provided multiple ways of diagnosing and treating patients accurately. In this review, multiple studies in which AI models were used to diagnose GERD are discussed. According to the studies, using AI models helped to diagnose GERD in patients accurately. AI, although considered one of the most potent emerging aspects of medicine with its accuracy in patient diagnosis, presents limitations of its own, which explains why healthcare providers may hesitate to use AI in patient care. The challenges and limitations should be addressed before AI is fully incorporated into the healthcare system.

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.

Challenges involved in the application of artificial intelligence in gastroenterology: The race is on!

Gastroenterology is a particularly data-rich field, generating vast repositories of data that are a fruitful ground for artificial intelligence (AI) and machine learning (ML) applications. In this opinion review, we initially elaborate on the current status of the application of AI/ML-based software in gastroenterology. Currently, AI/ML-based models have been developed in the following applications: Models integrated into the clinical setting following real-time patient data flagging patients at high risk for developing a gastrointestinal disease, models employing non-invasive parameters that provide accurate diagnoses aiming to either replace, minimize, or refine the indications of endoscopy, models utilizing genomic data to diagnose various gastrointestinal diseases, computer-aided diagnosis systems facilitating the interpretation of endoscopy images, models to facilitate treatment allocation and predict the response to treatment, and finally, models in prognosis predicting complications, recurrence following treatment, and overall survival. Then, we elaborate on several challenges and how they may negatively impact the widespread application of AI in healthcare and gastroenterology. Specifically, we elaborate on concerns regarding accuracy, cost-effectiveness, cybersecurity, interpretability, oversight, and liability. While AI is unlikely to replace physicians, it will transform the skillset demanded by future physicians to practice. Thus, physicians are expected to engage with AI to avoid becoming obsolete.

Evaluation of the Potential Utility of an Artificial Intelligence Chatbot in Gastroesophageal Reflux Disease Management

INTRODUCTION

Artificial intelligence chatbots could serve as an information resource for patients and a tool for clinicians. Their ability to respond appropriately to questions regarding gastroesophageal reflux disease is unknown.

METHODS

Twenty-three prompts regarding gastroesophageal reflux disease management were submitted to ChatGPT, and responses were rated by 3 gastroenterologists and 8 patients.

RESULTS

ChatGPT provided largely appropriate responses (91.3%), although with some inappropriateness (8.7%) and inconsistency. Most responses (78.3%) contained at least some specific guidance. Patients considered this a useful tool (100%).

DISCUSSION

ChatGPT's performance demonstrates the potential for this technology in health care, although also its limitations in its current state.

Encouraging appropriate use of proton pump inhibitors: existing initiatives and proposals for the future

INTRODUCTION

Proton pump inhibitors (PPIs) have revolutionized the management of acid-related disorders, representing today the mainstay treatment of these conditions. However, despite their large range of indications and usefulness, the remarkable expansion of their use in the last two decades cannot be explained by the increasing prevalence of acid-related diseases only. An inappropriate prescription for clinical conditions in which the pathogenetic role of acid has not been documented has been described, with the natural consequence of increasing the costs and the potential risk of iatrogenic harm due to adverse events and complications recently emerged.

AREAS COVERED

In this review, we summarize current indications of PPIs administration, potential adverse events associated with their chronic utilization, and misuse of PPIs. Moreover, we describe existing and possible initiatives for improving the use of PPIs, and some proposals for the future.

EXPERT OPINION

PPI deprescribing is the preferred and most effective approach to reduce the use of PPIs, rather than adopting sharp discontinuation, probably due to fewer withdrawal symptoms. Nonetheless, large knowledge gaps still exist in clinical practice regarding the optimal approach of PPI deprescribing in various clinical scenarios. Further prospective well-designed international studies are eagerly warranted to improve our perspectives on controlling global PPI inappropriate use.

Towards a better diagnosis of gastro esophageal reflux disease

INTRODUCTION

GERD is a common disorder and is characterized by the presence of typical or atypical symptoms. In GERD patients, the presence of mucosal alterations in endoscopy is detected in up to 30% of individuals. The clinical presentation of GERD patients may be complex and their management is challenging, due to the heterogeneous clinical presentation. The present review has been performed searching all relevant articles in this field, over the past years, using PubMed database.

AREAS COVERED

The diagnosis and management of GERD have been significantly improved in the last years due to the increasing availability of reflux monitoring techniques and the implementation of new procedures in the therapeutic armamentarium. Beside traditional impedance-pH variables, new metrics have been developed, increasing the diagnostic yield of reflux monitoring and better predicting the treatment response. Traditional pharmacological treatments include acid-suppressive-therapy and/or anti-acid. On the other hand, surgical treatment and, more recently, endoscopic procedures represent a promising field in the therapeutic approach.

EXPERT OPINION

Diagnosis and treatment of GERD still represent a challenging area. However, we believe that an accurate upfront evaluation is, nowadays, necessary in addressing patients with GERD to a more accurate diagnosis as well as to the best treatment options.

Application of Artificial Intelligence in Measuring Novel pH-Impedance Metrics for Optimal Diagnosis of GERD

Novel metrics extracted from pH-impedance monitoring can augment the diagnosis of gastroesophageal reflux disease (GERD). Artificial intelligence (AI) is being widely used to improve the diagnostic capabilities of various diseases. In this review, we update the current literature regarding applications of artificial intelligence in measuring novel pH-impedance metrics. AI demonstrates high performance in the measurement of impedance metrics, including numbers of reflux episodes and post-reflux swallow-induced peristaltic wave index and, furthermore, extracts baseline impedance from the entire pH-impedance study. AI is expected to play a reliable role in facilitating measuring novel impedance metrics in patients with GERD in the near future.

Artificial intelligence facilitates measuring reflux episodes and postreflux swallow-induced peristaltic wave index from impedance-pH studies in patients with reflux disease

BACKGROUND/AIM

Reflux episodes and postreflux swallow-induced peristaltic wave (PSPW) index are useful impedance parameters that can augment the diagnosis of gastroesophageal reflux disease (GERD). However, manual analysis of pH-impedance tracings is time consuming, resulting in limited use of these novel impedance metrics. This study aims to evaluate whether a supervised learning artificial intelligence (AI) model is useful to identify reflux episodes and PSPW index.

METHODS

Consecutive patients underwent 24-h impedance-pH monitoring were enrolled for analysis. Multiple AI and machine learning with a deep residual net model for image recognition were explored based on manual interpretation of reflux episodes and PSPW according to criteria from the Wingate Consensus. Intraclass correlation coefficients (ICCs) were used to measure the strength of inter-rater agreement of data between manual and AI interpretations.

RESULTS

We analyzed 106 eligible patients with 7939 impedance events, of whom 38 patients with pathological acid exposure time (AET) and 68 patients with physiological AET. On the manual interpretation, patients with pathological AET had more reflux episodes and lower PSPW index than those with physiological AET. Overall accuracy of AI identification for reflux episodes and PSPW achieved 87% and 82%, respectively. Inter-rater agreements between AI and manual interpretations achieved excellent for individual numbers of reflux episodes and PSPW index (ICC = 0.965 and ICC = 0.921).

CONCLUSIONS

AI has the potential to accurately and efficiently measure impedance metrics including reflux episodes and PSPW index. AI can be a reliable adjunct for measuring novel impedance metrics for GERD in the near future.

Optimal Wireless Reflux Monitoring Metrics to Predict Discontinuation of Proton Pump Inhibitor Therapy

INTRODUCTION

Ambulatory reflux monitoring performed off proton pump inhibitor (PPI) is the gold standard diagnostic test for nonerosive gastroesophageal reflux disease (GERD). However, the diagnostic metrics and optimal duration of monitoring are not well defined. This study evaluated the performance of multiple metrics across distinct durations of wireless reflux monitoring off PPI against the ability to discontinue PPI therapy in patients with suboptimal PPI response.

METHODS

This single-arm clinical trial performed over 4 years at 2 centers enrolled adults with troublesome GERD symptoms and inadequate response to > 8 weeks of PPI. Participants underwent 96-hour wireless pH monitoring off PPI. Primary outcome was whether the subject successfully discontinued PPI or resumed PPI within 3 weeks.

RESULTS

Of 132 participants, 30% discontinued PPI. Among multiple metrics assessed, total acid exposure time (AET) of 4.0% performed best in predicting PPI discontinuation (odds ratio 2.9 [95% confidence interval 1.4, 6.4]; P = 0.006), with other thresholds of AET and DeMeester score performing comparably. AET was significantly higher on day 1 of monitoring compared with other days, and prognostic performance significantly declined when only assessing the first 48 hours of monitoring (area under the curve for 96 hours 0.63 vs area under the curve for 48 hours 0.57; P = 0.01).

DISCUSSION

This clinical trial highlights the AET threshold of 4.0% as a high-performing prognostic marker of PPI discontinuation. 96 hours of monitoring performed better than 48 hours, in predicting ability to discontinue PPI. These data can inform current diagnostic approaches for patients with GERD symptoms who are unresponsive to PPI therapy.

Mean nocturnal baseline impedance, a novel metric of multichannel intraluminal impedance-pH monitoring in diagnosing gastroesophageal reflux disease

Gastroesophageal reflux disease (GERD) is a common disease with increasing prevalence worldwide. However, the diagnosis of GERD is challenging because there are no definite gold standard criteria. Recently, a novel impedance parameter, namely mean nocturnal baseline impedance (MNBI), has been proposed, which reflects the burden of longitudinal reflux and the integrity of esophageal mucosa. MNBI has shown an immense promise for increasing the diagnostic rate of multichannel intraluminal impedance-pH (MII-pH) monitoring and predicting the response to proton pump inhibitor (PPI) or anti-reflux intervention in patients with reflux symptoms. The present paper reviews the association between baseline impedance and esophageal mucosal integrity, the acquisition of MNBI in 24-h MII-pH monitoring, the clinical utilization of MNBI in improving the diagnosis rate of GERD in patients with typical reflux symptoms, predicting the response to PPI or anti-reflux treatment in these patients, the utilization of MNBI in diagnosing patients with atypical symptoms or extra-esophageal symptoms, and the correlation between reflux burden and MNBI. MNBI should be routinely assessed using MII-pH monitoring.

The present and future of gastroenterology and hepatology: an international SWOT analysis (the GASTROSWOT project)

GASTROSWOT is a strategic analysis of the current and projected states of the different subspecialties in gastroenterology that aims to provide guidance for research, clinical, and financial planning in gastroenterology. We executed a consensus-based international strengths, weaknesses, opportunities, and threats (SWOT) analysis. Four general coordinators, six field coordinators, and 12 experts participated in the study. SWOTs were provided for the following fields: neurogastroenterology, functional gastrointestinal disorders, and upper gastrointestinal diseases; inflammatory bowel disease; pancreatology and biliary diseases; endoscopy; gastrointestinal oncology; and hepatology. The GASTROSWOT analysis highlights the following in the current state of the field of gastroenterology: the incidence and complexity of several gastrointestinal diseases, including malignancies, are increasing; the COVID-19 pandemic has affected patient care on several levels; and with the advent of technical innovations in gastroenterology, a well trained workforce and strategic planning are required to optimise health-care utilisation. The analysis calls attention to the following in the future of gastroenterology: artificial intelligence and the use of big data will speed up discovery and smarter health-care provision in the field; the growth and diversification of gastroenterological specialties will improve specialised care for patients, but could promote fragmentation of care and health system inefficiencies; and furthermore, thoughtful planning is needed to reach an effective balance between the need for subspecialists and the value of general gastroenterology services.

Reflux Monitoring in Lung Disease: Is There a Better Metric than Esophageal Acid Exposure Time?

Ambulatory reflux monitoring can be performed with either a telemetry capsule to monitor for esophageal acid exposure alone for a period of 48-96 hours, and a 24 hour catheter based impedance/pH study which is most valuable for evaluating patients with objective evidence of GERD who are incompletely relieved with proton pump inhibitors. Some would consider catheter-based impedance/pH as the "best" test to evaluate patients with extraesophageal symptoms including suspected pulmonary complications of GERD. This editorial provides comment on the use of novel advanced metrics, mean nocturnal baseline impedance and post-reflux induced-swallow peristaltic wave index in analysis of these studies.

Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases

BACKGROUND

Artificial intelligence (AI) has recently been applied to endoscopy and questionnaires for the evaluation of oesophageal diseases (ODs).

AIM

We performed a systematic review with meta-analysis to evaluate the performance of AI in the diagnosis of malignant and benign OD.

METHODS

We searched MEDLINE, EMBASE, EMBASE Classic and the Cochrane Library. A bivariate random-effect model was used to calculate pooled diagnostic efficacy of AI models and endoscopists. The reference tests were histology for neoplasms and the clinical and instrumental diagnosis for gastro-oesophageal reflux disease (GERD). The pooled area under the summary receiver operating characteristic (AUROC), sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR) and diagnostic odds ratio (DOR) were estimated.

RESULTS

For the diagnosis of Barrett's neoplasia, AI had AUROC of 0.90, sensitivity 0.89, specificity 0.86, PLR 6.50, NLR 0.13 and DOR 50.53. AI models' performance was comparable with that of endoscopists (P = 0.35). For the diagnosis of oesophageal squamous cell carcinoma, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.97, 0.95, 0.92, 12.65, 0.05 and DOR 258.36, respectively. In this task, AI performed better than endoscopists although without statistically significant differences. In the detection of abnormal intrapapillary capillary loops, the performance of AI was: AUROC 0.98, sensitivity 0.94, specificity 0.94, PLR 14.75, NLR 0.07 and DOR 225.83. For the diagnosis of GERD based on questionnaires, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.99, 0.97, 0.97, 38.26, 0.03 and 1159.6, respectively.

CONCLUSIONS

AI demonstrated high performance in the clinical and endoscopic diagnosis of OD.

Artificial Intelligence in the Diagnosis of Upper Gastrointestinal Diseases

Artificial intelligence (AI) has enormous potential to support clinical routine workflows and therefore is gaining increasing popularity among medical professionals. In the field of gastroenterology, investigations on AI and computer-aided diagnosis (CAD) systems have mainly focused on the lower gastrointestinal (GI) tract. However, numerous CAD tools have been tested also in upper GI disorders showing encouraging results. The main application of AI in the upper GI tract is endoscopy; however, the need to analyze increasing loads of numerical and categorical data in short times has pushed researchers to investigate applications of AI systems in other upper GI settings, including gastroesophageal reflux disease, eosinophilic esophagitis, and motility disorders. AI and CAD systems will be increasingly incorporated into daily clinical practice in the coming years, thus at least basic notions will be soon required among physicians. For noninsiders, the working principles and potential of AI may be as fascinating as obscure. Accordingly, we reviewed systematic reviews, meta-analyses, randomized controlled trials, and original research articles regarding the performance of AI in the diagnosis of both malignant and benign esophageal and gastric diseases, also discussing essential characteristics of AI.

Clinical use of mean nocturnal baseline impedance and post-reflux swallow-induced peristaltic wave index for the diagnosis of gastro-esophageal reflux disease

The clinical diagnosis of gastro-esophageal reflux disease (GERD) is based on the presence of typical esophageal troublesome symptoms. In clinical practice, heartburn relief following a proton pump inhibitor (PPI) trial or endoscopy can confirm a diagnosis of GERD. In cases of diagnostic uncertainty or before anti-reflux interventions, combined impedance-pH monitoring (MII-pH) provides a comprehensive assessment of both physical and chemical properties of the refluxate, allowing to achieve a conclusive diagnosis of GERD. Recently, the Lyon Consensus proposed the use of mean nocturnal baseline impedance (MNBI) and post-reflux swallow-induced peristaltic wave index (PSPW-I) as novel MII-pH metrics to support the diagnosis of GERD. The calculation of MNBI and PSPW-I currently needs to be performed manually, but artificial intelligence systems for the automated analysis of MII-pH tracings are being developed. Several studies demonstrated the increased diagnostic yield MNBI and PSPW-I for the categorization of patients with GERD at both on- and off-PPI MII-pH monitoring. Accordingly, we performed a narrative review on the clinical use and diagnostic yield of MNBI and PSPW-I when the diagnosis of GERD is uncertain. Based on currently available evidence, we strongly support the evaluation of PSPW-I and MNBI as part of the standard assessment of MII-pH tracings for the evaluation of GERD, especially in patients with endoscopy-negative heartburn.

Integrated Relaxation Pressure Classification and Probe Positioning Failure Detection in High-Resolution Esophageal Manometry Using Machine Learning

High-resolution esophageal manometry is used for the study of esophageal motility disorders, with the help of catheters with up to 36 sensors. Color pressure topography plots are generated and analyzed and using the Chicago algorithm a final diagnosis is established. One of the main parameters in this algorithm is integrated relaxation pressure (IRP). The procedure is time consuming. Our aim was to firstly develop a machine learning based solution to detect probe positioning failure and to create a classifier to automatically determine whether the IRP is in the normal range or higher than the cut-off, based solely on the raw images. The first step was the preprocessing of the images, by finding the region of interest—the exact moment of swallowing. Afterwards, the images were resized and rescaled, so they could be used as input for deep learning models. We used the InceptionV3 deep learning model to classify the images as correct or failure in catheter positioning and to determine the exact class of the IRP. The accuracy of the trained convolutional neural networks was above 90% for both problems. This work is just the first step in fully automating the Chicago Classification, reducing human intervention.

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.