Near Focus NBI-Driven Artificial Intelligence for the Diagnosis of Gastro-Oesophageal Reflux Disease

February 11, 2020 updated by: King's College Hospital NHS Trust
Gastro-oesophageal reflux disease (GORD) is a chronic condition with symptoms arising secondary to the reflux of stomach contents (Vakil et al., 2006). It is divided into four phenotypes: Erosive Oesophagitis (EO), Non-Erosive Reflux Disease (NERD), Reflux Hypersensitivity (RH), Functional Heartburn (FH) (Nikaki, Woodland and Sifrim, 2016). The definition of these phenotype have evolved with the addition of diagnostic tests and methods of their interpretation, the most recent being the Lyon Consensus Statement (Gyawali et al., 2018). The majority of patients presenting with symptoms suggestive of GORD have no mucosal lesion seen at endoscopy (Nikaki, Woodland and Sifrim, 2016). Studies have shown a relation of increased IPCL numbers with GORD. This study aims to build a fully autmoated AI model using Near-Focus NBI images on patients with symptoms suggestive of GORD phenotyped in accordance with the Lyon Consensus.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

Gastro-oesophageal reflux disease (GORD) is a chronic condition with symptoms arising secondary to the reflux of stomach contents (Vakil et al., 2006). It is divided into four phenotypes: Erosive Oesophagitis (EO), Non-Erosive Reflux Disease (NERD), Reflux Hypersensitivity (RH), Functional Heartburn (FH) (Nikaki, Woodland and Sifrim, 2016). The definition of these phenotype have evolved with the addition of diagnostic tests and methods of their interpretation, the most recent being the Lyon Consensus Statement (Gyawali et al., 2018). The majority of patients presenting with symptoms suggestive of GORD have no mucosal lesion seen at endoscopy (Nikaki, Woodland and Sifrim, 2016). Complications of GORD, such as peptic stricture and Barrett's oesophagus, can be readily diagnosed using WLE. The diagnosis of reflux oesophagitis with standard-definition WLE is well described in the Los Angeles (LA) classification (Armstrong et al., 1996) and validated (Lundell et al., 1999) with LA grades C and D confirmatory of GORD (Gyawali et al., 2018). Furthermore, AET is demonstrated to increase with LA classification A to D (Lundell et al., 1999). There is, however, only a modest inter-observer agreement between LA grades (Kappa coefficient 0.4), especially A and B. Furthermore LA grade A oesophagitis is detected in up to 17.0% of asymptomatic patients (Nozu and Komiyama, 2008; Zagari et al., 2008). A diagnosis of GORD and decisions for anti-reflux surgery cannot be made on this basis, mandating pH testing to confirm GORD.

Narrow Band Imaging

Imaging of the gastro-oesophageal junction using high definition Olympus H260 scope using the LA classification of GORD with WLE and NBI demonstrated improvement in overall interobserver reproducibility when used in a combination compared with WLE alone; k 0.62 vs 0.45 (<0.05)(Lee et al., 2007). Features identified using digital magnification NBI at the squamo-columnar junction in cases of EO (n=41; LA grade A and B), NERD (n=36) and controls (n=32) include micro-erosions (100% EO; 52.8% NERD; 23.3% controls), increased vascularity (95.1% EO; 91.7% NERD; 36.7% controls) and round pit patterns (4.9% EO; 5.6% NERD; 70% controls). Increased vascularity combined with absence of round pit pattern distinguishes NERD from controls with sensitivity and specificity 86.1% and 83.3%. Inter-observer agreement in this single centre study was good for increased vascularity (k=0.95) and micro erosions (k=0.89) but low for pit pattern (k=0.59) (Fock et al., 2009).

Intra-papillary capillary loops (IPCLs) are mucosal capillaries arising from the submucosal vein to the papilla, usually arranged in a regular 'dot' like fashion approximately 100micrometres apart (Inoue, 2001). The visualisation of oesophageal IPCLs with NBI is well documented and form the basis of a NBI classification for squamous neoplasia (Inoue et al., 2015). IPCL morphology changes have been proposed in patients being investigated for NERD, in particular dilatation and elongation of IPCLs in patients with NERD with magnification NBI (Kato et al., 2006).

NBI with optical magnification for the diagnosis of GORD has been evaluated in 2 studies (Sharma et al., 2007; Lv et al., 2013). Sharma et al performed a feasibility trial with Olympus Q240Z with quadrantic examination of the distal 5cm by WLE then NBI in n=50 GORD (EO n=30; NERD n=20) and controls (n=30). Similar to Fock et al, the presence of microerosions and hypervascularity was significantly higher amongst GORD. IPCL number and morphology of tortuosity, dilatation were seen significantly more in GORD versus control. These findings were consistent in independent comparison of EO and NERD versus controls. ROC analysis thresholds for best sensitivity and specificity (respectively) for NERD were maximum ipcl/field 131 (90%, 70%), min 99 (85%, 70%) and average 117 (90%, 70%) (Sharma et al., 2007).

Lv et al used the Olympus GIF-H260Z to evaluate NERD (n=40), EO (n=40), Barrett's (n=40) and healthy controls (n=40). IPCL number, morphology (prolonged/dilated/tortuous), microerosions, round pit pattern above or below the SCJ, were recorded as features of reflux. Significant differences were found with increased IPCL number, microerosions, non-round pit patterns below the SCJ in GERD (NERD/EO and BE) patients compared to controls and fewer microerosions in NERD patients compared to RE (Lv et al., 2013).

The definition of NERD in all studies to date, however, is variable and largely based on symptom evaluation, response to PPI and the absence of mucosal lesions at endoscopic examination without standardisation using pH studies.

Artificial Intelligence

To date there is one study evaluating the use of ANNs in predicting GORD based on 45 variables including demographics, medical history, health status, symptoms scores. All patients underwent OGD, 24-pH studies performed in those with no mucosal lesion at endoscopy: 103 GORD patient (62 with reflux oesophagitis and 41 with AET>5%) and n=56 FH patients GORD. The ANN demonstrated an accuracy of 100% compared to 78% using conventional statistical regression analysis (Pace et al., 2005). While these are optimistic findings, the proportion of training and test data used was not specified and further evaluation with larger datasets is clearly warranted. There are no image-driven AI models for the diagnosis of GORD to date. Machine learning with endoscopic images is a pathway of great interest as described in section 1.7.7, with IPCLs as a potential target, based on previous studies of NBI for the diagnosis of GORD. CNNs involving IPCL detection and morphology have been recently reported in the context of a pilot study for the computer assisted diagnosis of oesophageal early squamous cell cancer using segmentation technology with accuracy matching expert endoscopists (Zhao et al., 2018). The image segmentation technique of Adaptive Local Thresholding has been demonstrated to be useful in vessel detection in retinal photographs making this as attractive technique for IPCLs (Jiang and Mojon, 2003).

Study Type

Observational

Enrollment (Actual)

76

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 90 years (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Probability Sample

Study Population

Consecutive patients attending the Gastroenterology department for the investigation of symptoms suggestive of Gastro-Oesophageal Reflux Disease (GORD) were approached for recruitment. Patients had to have symptoms including heartburn, regurgitation or chest/epigastric pain for a minimum of 3 months.

Description

Inclusion Criteria:

  • Retaining capacity and medically fit for gastroscopy
  • Requiring gastroscopy by current BSG guidelines for the investigation of acid reflux/ dyspepsia

Exclusion Criteria:

  • Unable to give informed consent
  • History of oesophageal or gastric surgery
  • Allergy to proton-pump inhibitor
  • Known Barrett's Oesophagus/ oesophageal carcinoma/ oesophageal stricture/ known oesophageal dysmotility
  • Portal Hypertension
  • Pacemaker (BRAVO)

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Prospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Gastro-Esophageal Reflux Disease
Patients defined as having GERD as per Lyon Consensus
Diagnostic test: wireless pH capsule recording
wireless pH capsule recording for up to 96 hours
Non-acid Reflux
Patient excluded for GERD as per Lyon Consensus
Diagnostic test: wireless pH capsule recording
wireless pH capsule recording for up to 96 hours

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
To evaluate Intra-Papillary Capillary Loop (IPCL) changes secondary to oesophageal acid exposure.
Time Frame: 6 weeks post completion of wireless capsule pH recording
Parameters of IPCLs: IPCLs/region of interest, morphology: IPCL length, density correlated to oesophageal acid exposure
6 weeks post completion of wireless capsule pH recording
To develop an accurate and reliable artificial intelligence model for the diagnosis of Gastro-Oesophageal Reflux Disease (GORD)
Time Frame: 3 months after completion of all data collection
Patient data split into training/validation and test dataset for computer assisted and deep learning model training and testing
3 months after completion of all data collection

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
To explore factors that may predict response to treatment.
Time Frame: 6 weeks to include data of response to antacid treatment
Statistical analysis of models for IPCL number/ROI and morphology features against response to an antacid medication challenge.
6 weeks to include data of response to antacid treatment

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

King's College Hospital NHS Trust

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

November 23, 2017

Primary Completion (Actual)

July 26, 2018

Study Completion (Actual)

November 30, 2018

Study Registration Dates

First Submitted

February 11, 2020

First Submitted That Met QC Criteria

February 11, 2020

First Posted (Actual)

February 13, 2020

Study Record Updates

Last Update Posted (Actual)

February 13, 2020

Last Update Submitted That Met QC Criteria

February 11, 2020

Last Verified

February 1, 2020

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 227223

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

No

IPD Plan Description

Protocol possible to obtain upon request

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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