Photodocumentation is one endoscopy quality performance indicator; however, manually auditing this indicator is challenging in clinical practice. Artificial intelligence technology may help to solve this problem. In this study, the upper gastrointestinal (GI)endoscopy images are classified into eight specific anatomical landmarks according to the society of Gastrointestinal Endoscopy (ESGE) guideline by the proposed deep learning (DL) system. Then, this classification model can be used to assess whether all images of anatomical locations are documented and the completeness of the photodocumentation rate could be used as the quality indicator. Also, the upper GI classification and quality indicator system could be extended to the lower GI endoscopy. However, a good DL model requires a large amount of training data for model development. In order to reduce the labeling time, we develop an accelerated data preparation approach. In this proposed approach, a smaller labeled data set is first used to train the base model, and then another larger unlabeled data set is classified by the base model. The base model and enhanced model achieve total accuracy of 96.29% and 96.64%, respectively. After developing a good classification model, we can use this DL system to assess whether all images of anatomical locations are documented. The photodocumentation completeness rate could be usedas the quality indicator for the endoscopist performance. A total of 472 upper GI endoscopies performed by 12 endoscopists are enrolled. The higher adenoma detection endoscopists have a higher complete examination rate (83.0% vs. 65.7%). For the proposed lower GI quality indicator system, 761 real-world examinations are analyzed. The accuracy of the proposed algorithm for the cecal intubation rate is 98.95% and the polypectomy agreement rate of the electronic reports and the DL algorithm is 0.87. A good correlation of DL withdrawal time between and that entered by the physician is found (r = 0.959). From the above experiments, the proposed DL endoscopy quality indicator system could help to improve the ndoscopy procedure's performance and provide better patient care.