Sharib Ali1, Adam Bailey2, Stephen Ash3, Maryam Haghighat4, Simon J Leedham5, Xin Lu6, James E East2, Jens Rittscher7, Barbara Braden8. 1. 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. 2. Translational Gastroenterology Unit, Experimental Medicine Division, Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom; Oxford National Institute for Health Research Biomedical Research Centre, Oxford, United Kingdom. 3. Ludwig Institute for Cancer Research, University of Oxford, Oxford, United Kingdom. 4. Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom; Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom. 5. Oxford National Institute for Health Research Biomedical Research Centre, Oxford, United Kingdom; Intestinal Stem Cell Biology Laboratory, Wellcome Trust Centre Human Genetics, University of Oxford, Oxford, United Kingdom. 6. Oxford National Institute for Health Research Biomedical Research Centre, Oxford, United Kingdom; Ludwig Institute for Cancer Research, University of Oxford, Oxford, United Kingdom. 7. 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; Ludwig Institute for Cancer Research, University of Oxford, Oxford, United Kingdom; Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom. Electronic address: jens.rittscher@eng.ox.ac.uk. 8. Translational Gastroenterology Unit, Experimental Medicine Division, Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom; Oxford National Institute for Health Research Biomedical Research Centre, Oxford, United Kingdom. Electronic address: barbara.braden@ndm.ox.ac.uk.
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.
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.
Authors: José Cornejo; Jorge A Cornejo-Aguilar; Mariela Vargas; Carlos G Helguero; Rafhael Milanezi de Andrade; Sebastian Torres-Montoya; Javier Asensio-Salazar; Alvaro Rivero Calle; Jaime Martínez Santos; Aaron Damon; Alfredo Quiñones-Hinojosa; Miguel D Quintero-Consuegra; Juan Pablo Umaña; Sebastian Gallo-Bernal; Manolo Briceño; Paolo Tripodi; Raul Sebastian; Paul Perales-Villarroel; Gabriel De la Cruz-Ku; Travis Mckenzie; Victor Sebastian Arruarana; Jiakai Ji; Laura Zuluaga; Daniela A Haehn; Albit Paoli; Jordan C Villa; Roxana Martinez; Cristians Gonzalez; Rafael J Grossmann; Gabriel Escalona; Ilaria Cinelli; Thais Russomano Journal: Biomed Res Int Date: 2022-03-24 Impact factor: 3.411