Utility of an Artificial Intelligence System for Classification of Esophageal Lesions when Simulating its Clinical use

Abstract

Background:Previous reports have shown favorable performance of artificial intelligence (AI) systems for diagnosing esophageal squamous cell carcinoma (ESCC) compared with endoscopists. However, these findings don’t reflect performance in clinical situations, as endoscopists classify lesions based on both magnified and non-magnified videos, while AI systems often use only a few magnified narrow band imaging (NBI) still images. We evaluated the performance of the AI system in simulated clinical situations.Methods:We used 25,048 images from 1,433 superficial ESCC and 4,746 images from 410 noncancerous esophagi to construct our AI system. For the validation dataset, we took NBI videos of suspected superficial ESCCs. The AI system diagnosis used one magnified still image taken from each video, while 19 endoscopists used whole videos. Results:We used 147 datasets including 83 superficial ESCC and 64 non-ESCC lesions. The accuracy, sensitivity and specificity for the classification of ESCC were, respectively, 80.9%, 85.5%, and 75.0% for the AI system and 69.2%, 67.5%, and 71.5% for the endoscopists. The AI system correctly classified all ESCCs invading the muscularis mucosa or submucosa and 96.8% of lesions ≥ 20 mm, whereas even the experts misdiagnosed some of them.Conclusions:Our AI system showed higher diagnostic ability for classifying ESCC and non-ESCC than endoscopists. It may provide valuable diagnostic support to endoscopists.