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BACKGROUND & AIMSBarrett’s epithelium measurement using widely accepted Prague C&M criteria is highly operator dependent. By reconstructing the surface of the Barrett’s area in 3D from endoscopy video, we propose a novel methodology for measuring the C&M score automatically. This 3D reconstruction provides an extended field of view and also allows to precisely quantify the Barrett’s area including islands. We aim to assess the accuracy of the extracted measurements from phantom and demonstrate their clinical usability.METHODSAdvanced deep learning techniques are utilised to design estimators for depth and camera pose required to map standard endoscopy video to a 3D surface model. By segmenting the Barrett’s area and locating the position of the gastro-oesophageal junction (GEJ) we measure C&M scores and the Barrett’s oesophagus areas (BOA). Experiments using a purpose-built 3D printed oesophagus phantom and high-definition video from 98 patients scored by an expert endoscopist are used for validation.RESULTSEndoscopic phantom video data demonstrated a 95 % accuracy with a marginal ± 1.8 mm average deviation for C&M and island measurements, while for BOA we achieved nearly 93 % accuracy with only ± 1.1 cm2 average deviation compared to the ground-truth measurements. On patient data, the C&M measurements provided by our system concord with the reference provided by expert upper GI endoscopists.CONCLUSIONSThe proposed methodology is suitable for extracting Prague C&M scores automatically with a high degree of accuracy. Providing an accurate measurement of the entire Barrett’s area provides new opportunities for risk stratification and the 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.