Impact of Computer-Assisted System on the Learning Curve and Quality in Esophagogastroduodenoscopy: Randomized Controlled Trial.

Li Huang,Mengjiao Zhang,Dawei Wang,Lianlian Wu,Liwen Yao,Jun Liu,Renduo Shang,Zehua Dong,Qiutang Xiong,Huiling Wu,Ming Xu,Honggang Yu,Dexin Gong,Jia Li,Lihui Zhang,Yijie Zhu,Youming Xu

doi:10.3389/fmed.2021.781256

Abstract

Background and Aims: To investigate the impact of the computer-assisted system on esophagogastroduodenoscopy (EGD) training for novice trainees in a prospective randomized controlled trial.Methods: We have constructed a computer-aided system (CAD) using retrospective images based on deep learning which could automatically monitor the 26 anatomical landmarks of the upper digestive tract and document standard photos. Six novice trainees were allocated and grouped into the CAD group and control group. Each of them took the training course, pre and post-test, and EGD examination scored by two experts. The CAD group was trained with the assistance of the CAD system and the control group without.Results: Both groups achieved great improvements in EGD skills. The CAD group received a higher examination grading score in the EGD examination (72.83 ± 16.12 vs. 67.26 ± 15.64, p = 0.039), especially in the mucosa observation (26.40 ± 6.13 vs. 24.11 ± 6.21, p = 0.020) and quality of collected images (7.29 ± 1.09 vs. 6.70 ± 1.05). The CAD showed a lower blind spot rate (2.19 ± 2.28 vs. 3.92 ± 3.30, p = 0.008) compared with the control group.Conclusion: The artificial intelligence assistant system displayed assistant capacity on standard EGD training, and assisted trainees in achieving a learning curve with high operation quality, which has great potential for application.Clinical Trial Registration: This trial is registered at https:/clinicaltrials.gov/, number NCT04682821.

Highlights

Background and AimsTo investigate the impact of the computer-assisted system on esophagogastroduodenoscopy (EGD) training for novice trainees in a prospective randomized controlled trial
Despite the technical skills and non-technical skills, such as communication and teamwork needed for endoscopy manipulation, EGD training requires quality control and cognitive skills, such as low blind spot rate, which is a significant indicator for EGD quality [2, 3]
In the present research, extending our previous study of the EGD artificial intelligence system, we investigated its efficacy on EGD training in the prospective randomized control trial

Summary

Introduction

To investigate the impact of the computer-assisted system on esophagogastroduodenoscopy (EGD) training for novice trainees in a prospective randomized controlled trial. Hundreds of millions of esophagogastroduodenoscopy (EGD) procedures are performed every year worldwide and play a pivotal role in the diagnosis and management of upper gastrointestinal disorders [1]. The effectiveness of EGD depends on the endoscopists’ skills, which need a prolonged learning curve for novice trainees to perform high-quality endoscopy care. Even though the value of validated simulators has been proven in pre-patient EGD training, patient-based training procedures are necessary [11]. A real-time aided training system in patient-based EGD is still lacking

Methods

Results

Conclusion