A Pilot Study on Automatic Three-Dimensional Quantification of Barrett’s Esophagus for Risk Stratification and Therapy Monitoring

作者：Sharib Ali, Adam Bailey, Stephen Ash, Maryam Haghighat, Philip Allan, Tim Ambrose, Carolina V. Arancibia-Cárcamo, Ellie Barnes, Elizabeth Bird‐Lieberman, Jan Bornschein, Oliver Brain, Jane Collier, Emma Culver, Alessandra Geremia, Bruce George, Lucy Howarth, Kelsey Jones, Paul Klenerman, R. Palmer, Fiona Powrie, Astor Rodrigues, Jack Satsangi, Alison Simmons, Simon Travis, Holm H. Uhlig, Alissa Walsh, Simon J. Leedham, Xin Lü, James E. East, Jens Rittscher, Barbara Braden · 发表于：Gastroenterology · 年份：2021 · DOI：10.1053/j.gastro.2021.05.059 · 被引用次数：38 · 研究领域：Esophageal Cancer Research and Treatment、Gastroesophageal reflux and treatments、Colorectal Cancer Screening and Detection

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: 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 Barr...