MedTrace Logo

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

NCT04268719 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 76

Last updated 2020-02-13

No results posted yet for this study

Summary

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.

Conditions

  • Gastro Esophageal Reflux

Interventions

DIAGNOSTIC_TEST

wireless pH capsule recording

wireless pH capsule recording for up to 96 hours

Sponsors & Collaborators

  • King's College Hospital NHS Trust

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
90 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2017-11-23
Primary Completion
2018-07-26
Completion
2018-11-30

Countries

  • United Kingdom

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04268719 on ClinicalTrials.gov