A Gastrointestinal Endoscopy Quality Control System Incorporated With Deep Learning Improved Endoscopist Performance in a Pretest and Post-Test Trial

INTRODUCTION: Gastrointestinal endoscopic quality is operator-dependent. To ensure the endoscopy quality, we constructed an endoscopic audit and feedback system named Endo.Adm and evaluated its effect in a form of pretest and posttest trial. METHODS: Endo.Adm system was developed using Python and Deep Convolutional Neural Ne2rk models. Sixteen endoscopists were recruited from Renmin Hospital of Wuhan University and were randomly assigned to undergo feedback of Endo.Adm or not (8 for the feedback group and 8 for the control group). The feedback group received weekly quality report cards which were automatically generated by Endo.Adm. We then compared the adenoma detection rate (ADR) and gastric precancerous conditions detection rate between baseline and postintervention phase for endoscopists in each group to evaluate the impact of Endo.Adm feedback. In total, 1,191 colonoscopies and 3,515 gastroscopies were included for analysis. RESULTS: ADR was increased after Endo.Adm feedback (10.8%–20.3%, P < 0.01, <odds ratio (OR) 2.13, 95% confidence interval (CI) 1.317–3.447), and endoscopists' ADR without feedback remained nearly unchanged (10.8%–10.9%, P = 0.57, OR 1.086, 95% CI 0.814–1.447). Gastric precancerous conditions detection rate increased in the feedback group (3%–7%, P < 0.01, OR 1.866, 95% CI 1.399–2.489) while no improvement was observed in the control group (3.9%–3.5%, P = 0.489, OR 0.856, 95% CI 0.550–1.332). DISCUSSION: Endo.Adm feedback contributed to multifaceted gastrointestinal endoscopic quality improvement. This system is practical to implement and may serve as a standard model for quality improvement in routine work (http://www.chictr.org.cn/, ChiCTR1900024153).