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Background & aimsBarrett's epithelium measurement using widely accepted Prague C&M classification is highly operator dependent. We propose a novel methodology for measuring this risk score automatically. The method also enables quantification of the area of Barrett's epithelium (BEA) and islands, which was not possible before. Furthermore, it allows 3-dimensional (3D) reconstruction of the esophageal surface, enabling interactive 3D visualization. We aimed to assess the accuracy of the proposed artificial intelligence system on both phantom and endoscopic patient data.MethodsUsing advanced deep learning, a depth estimator network is used to predict endoscope camera distance from the gastric folds. By segmenting BEA and gastroesophageal junction and projecting them to the estimated mm distances, we measure C&M scores including the BEA. The derived endoscopy artificial intelligence system was tested on a purpose-built 3D printed esophagus phantom with varying BEAs and on 194 high-definition videos from 131 patients with C&M values scored by expert endoscopists.ResultsEndoscopic phantom video data demonstrated a 97.2% accuracy with a marginal ± 0.9 mm average deviation for C&M and island measurements, while for BEA we achieved 98.4% accuracy with only ±0.4 cm2 average deviation compared with ground-truth. On patient data, the C&M measurements provided by our system concurred with expert scores with marginal overall relative error (mean difference) of 8% (3.6 mm) and 7% (2.8 mm) for C and M scores, respectively.ConclusionsThe proposed methodology automatically extracts Prague C&M scores with high accuracy. Quantification and 3D reconstruction of the entire Barrett's area provides new opportunities for risk stratification and assessment of therapy response.

Original publication

DOI

10.1053/j.gastro.2021.05.059

Type

Journal article

Journal

Gastroenterology

Publication Date

09/2021

Volume

161

Pages

865 - 878.e8

Addresses

Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom; Oxford National Institute for Health Research Biomedical Research Centre, Oxford, United Kingdom; Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom. Electronic address: sharib.ali@eng.ox.ac.uk.

Keywords

TGU Investigators, Esophagogastric Junction, Humans, Barrett Esophagus, Disease Progression, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Esophagoscopy, Treatment Outcome, Severity of Illness Index, Risk Assessment, Risk Factors, Pilot Projects, Reproducibility of Results, Predictive Value of Tests, Automation, Aged, Female, Male, Esophageal Mucosa, Deep Learning