Ali, S orcid.org/0000-0003-1313-3542, Bailey, A, Ash, S et al. (7 more authors) (2021) A Pilot Study on Automatic Three-Dimensional Quantification of Barrett’s Esophagus for Risk Stratification and Therapy Monitoring. Gastroenterology, 161 (3). 865-878.e8. ISSN 0016-5085
Abstract
Background & Aims
Barrett’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.
Methods
Using 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.
Results
Endoscopic 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.
Conclusions
The 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 by the AGA Institute. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Imaging; Deep learning; Three-dimensional; Risk assessment; Esophageal cancer |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Sep 2022 11:10 |
Last Modified: | 01 Sep 2022 11:10 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1053/j.gastro.2021.05.059 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190506 |