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Abstract
The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoE-EREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level.
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Details
1 University Hospital of Augsburg, (Internal) Medicine III – Gastroenterology, Augsburg, Germany (GRID:grid.419801.5) (ISNI:0000 0000 9312 0220)
2 Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Regensburg, Germany (GRID:grid.434958.7); OTH Regensburg, Regensburg Center of Health Sciences and Technology, Regensburg, Germany (GRID:grid.434958.7)
3 University of North Carolina, Center for Esophageal Diseases and Swallowing, Division of Gastroenterology and Hepatology, Department of Medicine, Chapel Hill, USA (GRID:grid.410711.2) (ISNI:0000 0001 1034 1720)
4 OTH Regensburg, Faculty Computer Science and Mathematics, Regensburg, Germany (GRID:grid.434958.7)
5 Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Regensburg, Germany (GRID:grid.434958.7)
6 University of Augsburg, General Pathology and Molecular Diagnostics, Medical Faculty, Augsburg, Germany (GRID:grid.7307.3) (ISNI:0000 0001 2108 9006)