Upper endoscopy photodocumentation quality evaluation with novel deep learning system.

ABSTRACT

OBJECTIVES: Visualization and photodocumentation during endoscopy procedures are suggested to be one indicator for endoscopy performance quality. However, this indicator is difficult to measure and audit manually in clinical practice. Artificial intelligence (AI) is an emerging technology that may solve this problem. METHODS: A deep learning model with an accuracy of 96.64% was developed from 15,305 images for upper endoscopy anatomy classification in the unit. Endoscopy images for asymptomatic patients receiving screening endoscopy were evaluated with this model to assess the completeness of photodocumentation rate. RESULTS: A total of 15,723 images from 472 upper endoscopies performed by 12 endoscopists were enrolled. The complete photodocumentation rate from the pharynx to the duodenum was 53.8% and from the esophagus to the duodenum was 78.0% in this study. Endoscopists with a higher adenoma detection rate had a higher complete examination rate from the pharynx to duodenum (60.0% vs. 38.7%, P < 0.0001) and from esophagus to duodenum (83.0% vs. 65.7%, P < 0.0001) compared with endoscopists with lower adenoma detection rate. The pharynx, gastric angle, gastric retroflex view, gastric antrum, and the first portion of duodenum are likely to be missed by endoscopists with lower adenoma detection rates. CONCLUSIONS: We report the use of a deep learning model to audit endoscopy photodocumentation quality in our unit. Endoscopists with better performance in colonoscopy had a better performance for this quality indicator. The use of such an AI system may help the endoscopy unit audit endoscopy performance.