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. 6 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 assistance of the CAD system and the control group without. Results: Both two groups achieved great improvements in EGD skills. The CAD group received higher examination grading score in the EGD examination (72.83±16.12 VS 67.26±15.64, p=0.039), especially in 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 to 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. Registration: This trial is registered at ClinicalTrials.gov, number NCT04682821.
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